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Post-acquisition data mining techniques for LC-MS/MS-acquired data in drug metabolite identification.

Science.gov (United States)

Dhurjad, Pooja Sukhdev; Marothu, Vamsi Krishna; Rathod, Rajeshwari

2017-08-01

Metabolite identification is a crucial part of the drug discovery process. LC-MS/MS-based metabolite identification has gained widespread use, but the data acquired by the LC-MS/MS instrument is complex, and thus the interpretation of data becomes troublesome. Fortunately, advancements in data mining techniques have simplified the process of data interpretation with improved mass accuracy and provide a potentially selective, sensitive, accurate and comprehensive way for metabolite identification. In this review, we have discussed the targeted (extracted ion chromatogram, mass defect filter, product ion filter, neutral loss filter and isotope pattern filter) and untargeted (control sample comparison, background subtraction and metabolomic approaches) post-acquisition data mining techniques, which facilitate the drug metabolite identification. We have also discussed the importance of integrated data mining strategy.
4D seismic data acquisition method during coal mining

International Nuclear Information System (INIS)

Du, Wen-Feng; Peng, Su-Ping

2014-01-01

In order to observe overburden media changes caused by mining processing, we take the fully-mechanized working face of the BLT coal mine in Shendong mine district as an example to develop a 4D seismic data acquisition methodology during coal mining. The 4D seismic data acquisition is implemented to collect 3D seismic data four times in different periods, such as before mining, during the mining process and after mining to observe the changes of the overburden layer during coal mining. The seismic data in the research area demonstrates that seismic waves are stronger in energy, higher in frequency and have better continuous reflectors before coal mining. However, all this is reversed after coal mining because the overburden layer has been mined, the seismic energy and frequency decrease, and reflections have more discontinuities. Comparing the records collected in the survey with those from newly mined areas and other records acquired in the same survey with the same geometry and with a long time for settling after mining, it clearly shows that the seismic reflections have stronger amplitudes and are more continuous because the media have recovered by overburden layer compaction after a long time of settling after mining. By 4D seismic acquisition, the original background investigation of the coal layers can be derived from the first records, then the layer structure changes can be monitored through the records of mining action and compaction action after mining. This method has laid the foundation for further research into the variation principles of the overburden layer under modern coal-mining conditions. (paper)
Classification and data acquisition with incomplete data

Science.gov (United States)

Williams, David P.

In remote-sensing applications, incomplete data can result when only a subset of sensors (e.g., radar, infrared, acoustic) are deployed at certain regions. The limitations of single sensor systems have spurred interest in employing multiple sensor modalities simultaneously. For example, in land mine detection tasks, different sensor modalities are better-suited to capture different aspects of the underlying physics of the mines. Synthetic aperture radar sensors may be better at detecting surface mines, while infrared sensors may be better at detecting buried mines. By employing multiple sensor modalities to address the detection task, the strengths of the disparate sensors can be exploited in a synergistic manner to improve performance beyond that which would be achievable with either single sensor alone. When multi-sensor approaches are employed, however, incomplete data can be manifested. If each sensor is located on a separate platform ( e.g., aircraft), each sensor may interrogate---and hence collect data over---only partially overlapping areas of land. As a result, some data points may be characterized by data (i.e., features) from only a subset of the possible sensors employed in the task. Equivalently, this scenario implies that some data points will be missing features. Increasing focus in the future on using---and fusing data from---multiple sensors will make such incomplete-data problems commonplace. In many applications involving incomplete data, it is possible to acquire the missing data at a cost. In multi-sensor remote-sensing applications, data is acquired by deploying sensors to data points. Acquiring data is usually an expensive, time-consuming task, a fact that necessitates an intelligent data acquisition process. Incomplete data is not limited to remote-sensing applications, but rather, can arise in virtually any data set. In this dissertation, we address the general problem of classification when faced with incomplete data. We also address the
Data Mining for CRM

Science.gov (United States)

Thearling, Kurt

Data Mining technology allows marketing organizations to better understand their customers and respond to their needs. This chapter describes how Data Mining can be combined with customer relationship management to help drive improved interactions with customers. An example showing how to use Data Mining to drive customer acquisition activities is presented.
Due Diligence Processes for Public Acquisition of Mining-Impacted Landscapes

Science.gov (United States)

Martin, E.; Monohan, C.; Keeble-Toll, A. K.

2016-12-01

The acquisition of public land is critical for achieving conservation and habitat goals in rural regions projected to experience continuously high rates of population growth. To ensure that public funds are utilized responsibly in the purchase of conservation easements appropriate due diligence processes must be established that limit landowner liability post-acquisition. Traditional methods of characterizing contamination in regions where legacy mining activities were prevalent may not utilize current scientific knowledge and understanding of contaminant fate, transport and bioavailability, and therefore are likely to have type two error. Agency prescribed assessment methods utilized under CERLA in many cases fail to detect contamination that presents liability issues by failing to require water quality sampling that would reveal offsite transport potential of contaminants posing human health risks, including mercury. Historical analysis can be used to inform judgmental sampling to identify hotspots and contaminants of concern. Land acquisition projects at two historic mine sites in Nevada County, California, the Champion Mine Complex and the Black Swan Preserve have established the necessity of re-thinking due diligence processes for mining-impacted landscapes. These pilot projects demonstrate that pre-acquisition assessment in the Gold Country must include judgmental sampling and evaluation of contaminant transport. Best practices using the current scientific knowledge must be codified by agencies, consultants, and NGOs in order to ensure responsible use of public funds and to safeguard public health.
Sustainable Mining Environment: Technical Review of Post-mining Plans

Directory of Open Access Journals (Sweden)

Restu Juniah

2017-12-01

Full Text Available The mining industry exists because humans need mining commodities to meet their daily needs such as motor vehicles, mobile phones, electronic equipment and others. Mining commodities as mentioned in Government Regulation No. 23 of 2010 on Implementation of Mineral and Coal Mining Business Activities are radioactive minerals, metal minerals, nonmetallic minerals, rocks and coal. Mineral and coal mining is conducted to obtain the mining commodities through production operations. Mining and coal mining companies have an obligation to ensure that the mining environment in particular after the post production operation or post mining continues. The survey research aims to examine technically the post-mining plan in coal mining of PT Samantaka Batubara in Indragiri Hulu Regency of Riau Province towards the sustainability of the mining environment. The results indicate that the post-mining plan of PT Samantaka Batubara has met the technical aspects required in post mining planning for a sustainable mining environment. Postponement of post-mining land of PT Samantaka Batubara for garden and forest zone. The results of this study are expected to be useful and can be used by stakeholders, academics, researchers, practitioners and associations of mining, and the environment.
Legal Policy Of Peoples Rights In Around Mining Corporate Post-Mining Activities

Directory of Open Access Journals (Sweden)

Teng Berlianty

2015-08-01

Full Text Available This study aims to gain an understanding of the essence of the rights of communities around post-mining corporate responsibility towards the fulfillment of the rights of communities around post-mining as well as government policies to protect the sustainability of the post-mining communities around the mining business. This type of research is a normative legal research methods using primary legal materials secondary and tertiary. With the approach of sociolegal through down the field in Gebe to get data concrete. Data were analyzed with qualitative analysis. The results showed that the essence of the rights of communities around mining operations after the mine in the form of the right to a decent life welfare the right to social security in the form of employment the guarantee of free education and healthcare for the local population as well as the right to a good environment and healthy as a guarantee of the continuity of human existence and future generations. These rights have not been fully realized post-mining. Corporate responsibility in accordance with Article 74 of Law No. 40 of 2007 on the fulfillment of the rights of communities around mining operations after the mine in the form of welfare responsibilities clothing food and shelter especially electricity and water have not been met then the social responsibility to empower communities around the mine as stakeholders as well as environmental responsibility. Legal policy such as the empowerment of communities around the mine in order to be self-sufficient after the post-mining public service policies in education and health as a form of existence of government using existing programs nationally and subordinate to the PT. Antam. as well as environmental protection policies in the form of post-mining reclamation formulated in the companys liabilities.
Behavior Correlates of Post-Stroke Disability Using Data Mining and Infographics.

Science.gov (United States)

Yoon, Sunmoo; Gutierrez, Jose

Disability is a potential risk for stroke survivors. This study aims to identify disability risk factors associated with stroke and their relative importance and relationships from a national behavioral risk factor dataset. Data of post-stroke individuals in the U.S (n=19,603) including 397 variables were extracted from a publically available national dataset and analyzed. Data mining algorithms including C4.5 and linear regression with M5s methods were applied to build association models for post-stroke disability using Weka software. The relative importance and relationship of 70 variables associated with disability were presented in infographics for clinicians to understand easily. Fifty-five percent of post-stroke patients experience disability. Exercise, employment and satisfaction of life were relatively important factors associated with disability among stroke patients. Modifiable behavior factors strongly associated with disability include exercise (OR: 0.46, PData mining is promising to discover factors associated with post-stroke disability from a large population dataset. The findings can be potentially valuable for establishing the priorities for clinicians and researchers and for stroke patient education. The methods may generalize to other health conditions.
Data preprocessing in data mining

CERN Document Server

García, Salvador; Herrera, Francisco

2015-01-01

Data Preprocessing for Data Mining addresses one of the most important issues within the well-known Knowledge Discovery from Data process. Data directly taken from the source will likely have inconsistencies, errors or most importantly, it is not ready to be considered for a data mining process. Furthermore, the increasing amount of data in recent science, industry and business applications, calls to the requirement of more complex tools to analyze it. Thanks to data preprocessing, it is possible to convert the impossible into possible, adapting the data to fulfill the input demands of each data mining algorithm. Data preprocessing includes the data reduction techniques, which aim at reducing the complexity of the data, detecting or removing irrelevant and noisy elements from the data. This book is intended to review the tasks that fill the gap between the data acquisition from the source and the data mining process. A comprehensive look from a practical point of view, including basic concepts and surveying t...
Prediction of pork quality parameters by applying fractals and data mining on MRI

DEFF Research Database (Denmark)

Caballero, Daniel; Pérez-Palacios, Trinidad; Caro, Andrés

2017-01-01

This work firstly investigates the use of MRI, fractal algorithms and data mining techniques to determine pork quality parameters non-destructively. The main objective was to evaluate the capability of fractal algorithms (Classical Fractal algorithm, CFA; Fractal Texture Algorithm, FTA and One...... Point Fractal Texture Algorithm, OPFTA) to analyse MRI in order to predict quality parameters of loin. In addition, the effect of the sequence acquisition of MRI (Gradient echo, GE; Spin echo, SE and Turbo 3D, T3D) and the predictive technique of data mining (Isotonic regression, IR and Multiple linear...... regression, MLR) were analysed. Both fractal algorithm, FTA and OPFTA are appropriate to analyse MRI of loins. The sequence acquisition, the fractal algorithm and the data mining technique seems to influence on the prediction results. For most physico-chemical parameters, prediction equations with moderate...
Collaborative Data Mining

Science.gov (United States)

Moyle, Steve

Collaborative Data Mining is a setting where the Data Mining effort is distributed to multiple collaborating agents - human or software. The objective of the collaborative Data Mining effort is to produce solutions to the tackled Data Mining problem which are considered better by some metric, with respect to those solutions that would have been achieved by individual, non-collaborating agents. The solutions require evaluation, comparison, and approaches for combination. Collaboration requires communication, and implies some form of community. The human form of collaboration is a social task. Organizing communities in an effective manner is non-trivial and often requires well defined roles and processes. Data Mining, too, benefits from a standard process. This chapter explores the standard Data Mining process CRISP-DM utilized in a collaborative setting.
Multiple-user data acquisition and analysis system

International Nuclear Information System (INIS)

Manzella, V.; Chrien, R.E.; Gill, R.L.; Liou, H.I.; Stelts, M.L.

1981-01-01

The nuclear physics program at the Brookhaven National Laboratory High Flux Beam Reactor (HFBR) employs a pair of PDP-11 computers for the dual functions of data acquisition and analysis. The data acquisition is accomplished through CAMAC and features a microprogrammed branch driver to accommodate various experimental inputs. The acquisition computer performs the functions of multi-channel analyzers, multiscaling and time-sequenced multichannel analyzers and gamma-ray coincidence analyzers. The data analysis computer is available for rapid processing of data tapes written by the acquisition computer. The ability to accommodate many users is facilitated by separating the data acquisition and analysis functions, and allowing each user to tailor the analysis to the specific requirements of his own experiment. The system is to be upgraded soon by the introduction of a dual port disk to allow a data base to be available to each computer
Post-Acquisition IT Integration

DEFF Research Database (Denmark)

Henningsson, Stefan; Yetton, Philip

2013-01-01

The extant research on post-acquisition IT integration analyzes how acquirers realize IT-based value in individual acquisitions. However, serial acquirers make 60% of acquisitions. These acquisitions are not isolated events, but are components in growth-by-acquisition programs. To explain how...... serial acquirers realize IT-based value, we develop three propositions on the sequential effects on post-acquisition IT integration in acquisition programs. Their combined explanation is that serial acquirers must have a growth-by-acquisition strategy that includes the capability to improve...... IT integration capabilities, to sustain high alignment across acquisitions and to maintain a scalable IT infrastructure with a flat or decreasing cost structure. We begin the process of validating the three propositions by investigating a longitudinal case study of a growth-by-acquisition program....
Groundwater-quality data associated with abandoned underground coal mine aquifers in West Virginia, 1973-2016: Compilation of existing data from multiple sources

Science.gov (United States)

McAdoo, Mitchell A.; Kozar, Mark D.

2017-11-14

This report describes a compilation of existing water-quality data associated with groundwater resources originating from abandoned underground coal mines in West Virginia. Data were compiled from multiple sources for the purpose of understanding the suitability of groundwater from abandoned underground coal mines for public supply, industrial, agricultural, and other uses. This compilation includes data collected for multiple individual studies conducted from July 13, 1973 through September 7, 2016. Analytical methods varied by the time period of data collection and requirements of the independent studies.This project identified 770 water-quality samples from 294 sites that could be attributed to abandoned underground coal mine aquifers originating from multiple coal seams in West Virginia.
The importance of cultural leadership during post-acquisition integration

OpenAIRE

Mcconnon, Tom

2013-01-01

Mergers and acquisitions (M&A) are not only financial decisions but can also be understood as social processes. Due to the myriad of changes generated by an acquisition, the integration period is characterised by multiple adjustment difficulties. A substantive body of research blames post-acquisition ‘cultural clash’ caused by cultural differences between the two merging organisations as a major cause of disappointing integration outcomes. Yet research into the process of cultural leadership ...
The multiple zeta value data mine

International Nuclear Information System (INIS)

Buemlein, J.; Broadhurst, D.J.

2009-07-01

We provide a data mine of proven results for multiple zeta values (MZVs) of the form ζ(s 1 ,s 2 ,..,s k ) = sum ∞ n 1 >n 2 >...>n k >0 {1/(n 1 s 1 ..n k s k )} with weight w = sum K i=1 s i and depth k and for Euler sums of the form sum ∞ n 1 >n 2 >...>n k >0 {(ε 1 n 1 ..ε 1 n k )/(n 1 s 1 ..n k s k )} with signs ε i = ± 1. Notably, we achieve explicit proven reductions of all MZVs with weights w≤22, and all Euler sums with weights w≤12, to bases whose dimensions, bigraded by weight and depth, have sizes in precise agreement with the Broadhurst. Kreimer and Broadhurst conjectures. Moreover, we lend further support to these conjectures by studying even greater weights (w≤30), using modular arithmetic. To obtain these results we derive a new type of relation for Euler sums, the Generalized Doubling Relations. We elucidate the ''pushdown'' mechanism, whereby the ornate enumeration of primitive MZVs, by weight and depth, is reconciled with the far simpler enumeration of primitive Euler sums. There is some evidence that this pushdown mechanism finds its origin in doubling relations. We hope that our data mine, obtained by exploiting the unique power of the computer algebra language FORM, will enable the study of many more such consequences of the double-shuffle algebra of MZVs, and their Euler cousins, which are already the subject of keen interest, to practitioners of quantum field theory, and to mathematicians alike. (orig.)
Datafish Multiphase Data Mining Technique to Match Multiple Mutually Inclusive Independent Variables in Large PACS Databases.

Science.gov (United States)

Kelley, Brendan P; Klochko, Chad; Halabi, Safwan; Siegal, Daniel

2016-06-01

Retrospective data mining has tremendous potential in research but is time and labor intensive. Current data mining software contains many advanced search features but is limited in its ability to identify patients who meet multiple complex independent search criteria. Simple keyword and Boolean search techniques are ineffective when more complex searches are required, or when a search for multiple mutually inclusive variables becomes important. This is particularly true when trying to identify patients with a set of specific radiologic findings or proximity in time across multiple different imaging modalities. Another challenge that arises in retrospective data mining is that much variation still exists in how image findings are described in radiology reports. We present an algorithmic approach to solve this problem and describe a specific use case scenario in which we applied our technique to a real-world data set in order to identify patients who matched several independent variables in our institution's picture archiving and communication systems (PACS) database.
Data Mining in Distributed Database of the First Egyptian Thermal Research Reactor (ETRR-1)

International Nuclear Information System (INIS)

Abo Elez, R.H.; Ayad, N.M.A.; Ghuname, A.A.A.

2006-01-01

Distributed database (DDB)technology application systems are growing up to cover many fields an domains, and at different levels. the aim of this paper is to shade some lights on applying the new technology of distributed database on the ETRR-1 operation data logged by the data acquisition system (DACQUS)and one can extract a useful knowledge. data mining with scientific methods and specialize tools is used to support the extraction of useful knowledge from the rapidly growing volumes of data . there are many shapes and forms for data mining methods. predictive methods furnish models capable of anticipating the future behavior of quantitative or qualitative database variables. when the relationship between the dependent an independent variables is nearly liner, linear regression method is the appropriate data mining strategy. so, multiple linear regression models have been applied to a set of data samples of the ETRR-1 operation data, using least square method. the results show an accurate analysis of the multiple linear regression models as applied to the ETRR-1 operation data
Multiple Additive Regression Trees a Methodology for Predictive Data Mining for Fraud Detection

National Research Council Canada - National Science Library

da

2002-01-01

...) is using new and innovative techniques for fraud detection. Their primary techniques for fraud detection are the data mining tools of classification trees and neural networks as well as methods for pooling the results of multiple model fits...
Evaluation of human health risk from in situ recovery uranium mining, pre-and post-mining, and post-restoration

Energy Technology Data Exchange (ETDEWEB)

Ruedig, E.; Bhattacharyya, A.; Borch, T.; Johnson, T. [Colorado State University (United States); Till, J. [Risk Assessment Corporation (United States)

2014-07-01

In the United States, the restoration of in situ recovery (ISR) uranium mines is aimed at returning sites to pre-mining conditions. While this may seem an appropriate goal, little or no scientific information is available to justify utilizing baseline conditions for regulatory compliance. The chemical and radiological contaminants monitored for restoration compliance have not been evaluated to ensure they are proper indicators of the mitigation of risk. Pre-mining aquifers do not meet minimum United States drinking water standards, and must have an aquifer exemption in place prior to mining. Under these conditions, returning groundwater to near the original concentrations of contaminants may be unnecessary. Post-mining groundwater is also unlikely to meet standards for drinking water, but may be depleted in at least some toxic species as a result of the mining process. Here, we examine the risk to representative person from the personal use of groundwater sourced from an Uranium ISR mine. Water samples were collected from Cameco Resource's Smith Ranch-Highlands ISR Uranium mine near Casper, Wyoming, USA. Samples were acquired pre-mining, post-mining, and post-restoration. Concentrations of heavy metals and radionuclides were assessed by appropriate analytical techniques (e.g., mass spectroscopy or alpha spectroscopy) and these concentrations were used to estimate human health risk for three exposure scenarios: a scenario with high exposure, a scenario with medium exposure, and a scenario with low exposure. A simple biosphere transport model was constructed for each scenario to estimate the risk to humans from the use of contaminated waters for subsistence-related activities. Chemical and radiological risks were harmonized according to the United States Environmental Protection Agency's guidance for superfund sites. Each exposure scenario and its subsequent risk were evaluated individually for pre-mining, post-mining, and post-restoration aquifer waters

Privacy-Preserving Data Mining of Medical Data Using Data Separation-Based Techniques

Directory of Open Access Journals (Sweden)

Gang Kou

2007-08-01

Full Text Available Data mining is concerned with the extraction of useful knowledge from various types of data. Medical data mining has been a popular data mining topic of late. Compared with other data mining areas, medical data mining has some unique characteristics. Because medical files are related to human subjects, privacy concerns are taken more seriously than other data mining tasks. This paper applied data separation-based techniques to preserve privacy in classification of medical data. We take two approaches to protect privacy: one approach is to vertically partition the medical data and mine these partitioned data at multiple sites; the other approach is to horizontally split data across multiple sites. In the vertical partition approach, each site uses a portion of the attributes to compute its results, and the distributed results are assembled at a central trusted party using a majority-vote ensemble method. In the horizontal partition approach, data are distributed among several sites. Each site computes its own data, and a central trusted party is responsible to integrate these results. We implement these two approaches using medical datasets from UCI KDD archive and report the experimental results.
The multiple zeta value data mine

Energy Technology Data Exchange (ETDEWEB)

Buemlein, J. [Deutsches Elektronen-Synchrotron (DESY), Zeuthen (Germany); Broadhurst, D.J. [Open Univ., Milton Keynes (United Kingdom). Physics and Astronomy Dept.; Vermaseren, J.A.M. [Deutsches Elektronen-Synchrotron (DESY), Zeuthen (Germany); NIKHEF, Amsterdam (Netherlands)

2009-07-15

We provide a data mine of proven results for multiple zeta values (MZVs) of the form {zeta}(s{sub 1},s{sub 2},..,s{sub k}) = sum {sup {infinity}}{sub n{sub 1}}{sub >n{sub 2}}{sub >...>n{sub k}}{sub >0} {l_brace}1/(n{sub 1}{sup s{sub 1}}..n{sub k}{sup s{sub k}}){r_brace} with weight w = sum {sup K}{sub i=1}s{sub i} and depth k and for Euler sums of the form sum {sup {infinity}}{sub n{sub 1}}{sub >n{sub 2}}{sub >...>n{sub k}}{sub >0} {l_brace}({epsilon}{sub 1}{sup n{sub 1}}..{epsilon}{sub 1}{sup n{sub k}})/(n{sub 1}{sup s{sub 1}}..n{sub k}{sup s{sub k}}){r_brace} with signs {epsilon}{sub i} = {+-} 1. Notably, we achieve explicit proven reductions of all MZVs with weights w{<=}22, and all Euler sums with weights w{<=}12, to bases whose dimensions, bigraded by weight and depth, have sizes in precise agreement with the Broadhurst. Kreimer and Broadhurst conjectures. Moreover, we lend further support to these conjectures by studying even greater weights (w{<=}30), using modular arithmetic. To obtain these results we derive a new type of relation for Euler sums, the Generalized Doubling Relations. We elucidate the ''pushdown'' mechanism, whereby the ornate enumeration of primitive MZVs, by weight and depth, is reconciled with the far simpler enumeration of primitive Euler sums. There is some evidence that this pushdown mechanism finds its origin in doubling relations. We hope that our data mine, obtained by exploiting the unique power of the computer algebra language FORM, will enable the study of many more such consequences of the double-shuffle algebra of MZVs, and their Euler cousins, which are already the subject of keen interest, to practitioners of quantum field theory, and to mathematicians alike. (orig.)
High-speed multiple-channel analog to digital data-acquisition module for microprocessor systems

International Nuclear Information System (INIS)

Ethridge, C.D.

1977-01-01

Intelligent data acquisition and instrumentation systems established by the incorporation of microprocessor technology require high-speed analog to digital conversion of multiple-channel input signals. Sophisticated data systems or subsystems are enabled by the microprocessor software flexibility to establish adaptive input data procedures. These adaptive procedures are enhanced by versatile interface circuitry which is software controlled
Locating previously unknown patterns in data-mining results: a dual data- and knowledge-mining method

Directory of Open Access Journals (Sweden)

Knaus William A

2006-03-01

Full Text Available Abstract Background Data mining can be utilized to automate analysis of substantial amounts of data produced in many organizations. However, data mining produces large numbers of rules and patterns, many of which are not useful. Existing methods for pruning uninteresting patterns have only begun to automate the knowledge acquisition step (which is required for subjective measures of interestingness, hence leaving a serious bottleneck. In this paper we propose a method for automatically acquiring knowledge to shorten the pattern list by locating the novel and interesting ones. Methods The dual-mining method is based on automatically comparing the strength of patterns mined from a database with the strength of equivalent patterns mined from a relevant knowledgebase. When these two estimates of pattern strength do not match, a high "surprise score" is assigned to the pattern, identifying the pattern as potentially interesting. The surprise score captures the degree of novelty or interestingness of the mined pattern. In addition, we show how to compute p values for each surprise score, thus filtering out noise and attaching statistical significance. Results We have implemented the dual-mining method using scripts written in Perl and R. We applied the method to a large patient database and a biomedical literature citation knowledgebase. The system estimated association scores for 50,000 patterns, composed of disease entities and lab results, by querying the database and the knowledgebase. It then computed the surprise scores by comparing the pairs of association scores. Finally, the system estimated statistical significance of the scores. Conclusion The dual-mining method eliminates more than 90% of patterns with strong associations, thus identifying them as uninteresting. We found that the pruning of patterns using the surprise score matched the biomedical evidence in the 100 cases that were examined by hand. The method automates the acquisition of
Determine Appropriate Post Mining Land Use in Indonesia Coal Mining Using Land Suitability Evaluation

OpenAIRE

Maryati, Sri; Shimada, Hideki; Hamanaka, Akihiro; Sasaoka, Takashi; Matsui, Kikuo

2012-01-01

Coal mining industry gives many benefits for Indonesia including contribution in total Indonesian GDP. Most of coal mines in Indonesia are open pit mining method which disturbs large area of land. One of open pit mining impact is damage land and related to soil erosion occurrences it will degrade land by top soil loses. Indonesia Government has issued mine closure regulation to encourage mining industry provide post mining land use. Determination of post mining land use should be considering ...
Post-mining in France

International Nuclear Information System (INIS)

2007-01-01

This plentifully illustrated book aims at showing how new equilibria are building up during the transition between mining activity and post-mining, and at stressing on the necessity to keep up the cultural elements, the competencies and knowledge of mining works. The first chapter - mine and men - shows the importance of mineral substances in the objects of the everyday life, illustrates the importance of the mining tradition in France and describes the technical and administrative organisation of the end of the mining activity (works, rehabilitation, regulation, monitoring..). Chapter two - exploitation methods - presents the surface and underground facilities and their impact on the environment (extraction machines, workshops, ore processing plants, decantation ponds..). The third chapter deals with the rehabilitation and monitoring aspects: impact of mining activity stoppage on underground and surface waters, land stability, soils cleansing.. The last chapter summarizes the history of French mining region by region: Nord-Pas-de-Calais, Lorraine-Alsace, Massif central, Bretagne-Normandie, Provence-Alpes-Cote d'Azur and Pyrenees
Data-acquisition system for radon monitoring

International Nuclear Information System (INIS)

Franklin, J.C.; Zawadzki, R.J.; Meyer, T.O.; Hill, A.L.

1976-01-01

A data-acquisition system was designed by the Bureau of Mines to monitor five detectors with radon continuously flowing through each. These detectors could be monitored up to 12 times an hour, but were only monitored according to a preset time, thus allowing radon to be monitored continuously in a uranium mine. The counter can be set to monitor each detector for any period of time up to 16.5 minutes. This allows very low concentrations to be monitored longer to reduce statistical error. There would be no upper limit in radon concentration that could be monitored, but there would be a lower limit of 50 pCi/l. Each detector was calibrated by the Lucas flask method. Multiple samples were taken at two different concentrations, and the correction factor for each detector was determined by a least squares fit of the data. To verify the calibrations, a series of measurements at several concentrations were made against a constant source. The agreement at low radon concentrations (300 pCi/l) with the two-filter method was within 3 percent; thus, the total error would be this difference plus the two-filter error. At high concentrations, the coefficient of variation ranged between 2.1 and 9.8 percent for the five different detector units
JIGSAW: Acquisition, Display and Analysis system designed to collect data from Multiple Gamma-Ray detectors

International Nuclear Information System (INIS)

Haywood, S.E.; Bamford, G.J.; Rester, A.C.; Coldwell, R.L.

1992-01-01

In this paper, the authors report on work performed to date on JIGSAW - a self contained data acquisition, display and analysis system designed to collect data form multiple gamma-ray detectors. The data acquisition system utilizes commercially available VMEbus and NIM hardware modules and the VME exec real time operating system. A Unix based software package, written in ANSI standard C and with the XII graphics routines, allows the user to view the acquired spectra. Analysis of the histograms can be performed in background during the run with the ROBFIT suite of curve fitting routines
Knowledge-sharing Behavior and Post-acquisition Integration Failure

DEFF Research Database (Denmark)

Gammelgaard, Jens; Husted, Kenneth; Michailova, Snejina

2004-01-01

AbstractNot achieving the anticipated synergy effects in the post-acquisition integration context is a serious causefor the high acquisition failure rate. While existing studies on failures of acquisitions exist fromeconomics, finance, strategy, organization theory, and human resources management......, this paper appliesinsights from the knowledge-sharing literature. The paper establishes a conceptual link between obstaclesin the post-acquisition integration processes and individual knowledge-sharing behavior as related toknowledge transmitters and knowledge receivers. We argue that such an angle offers...... important insights toexplaining the high failure rate in acquisitions.Descriptors: post-acquisition integration, acquisition failure, individual knowledge-sharing behavior...
Radiological data acquisition, investigation and evaluation of mining relics

International Nuclear Information System (INIS)

1992-01-01

Within the scope of a Federal Project, the environmental radioactivity and the radon concentration in buildings caused by mining relics in the new Federal Lands of Germany are investigated. In the first phase of the project, about 8000 relics of former mining were identified by analysing existing documents, categorised, and recorded in a special data bank. Thereby, 'areas of suspicion' of 1500 km 2 spaciously defined in the beginning could be reduced to 'areas of investigation' of 250 km 2 now to be examined in close coordination with the land and district authorities by a programme gradually adapted to the radiological significance of the relics. Experience with site-specific measuring programmes have already been gained through three pilot projects at typical sites of former mining activities. Recommendations of the German Radiation Protection Commission serve for the evaluation of the results. By the measuring programme for radon in buildings of mining and geological predestined regions more than 25000 buildings of 210 communities have been investigated. The results confirm the expected prevailing influence of the geologic underground on the radon concentration. Extreme values are observed where direct connections additionally exist to mining relics in the ground. (orig./HP) With 11 figs. in annex [de
Industry Relatedness and Post-Acquisition Innovative Performance

DEFF Research Database (Denmark)

Cefis, Elena; Marsili, Orietta; Rigamonti, Damiana

2015-01-01

This paper examines how characteristics of acquiring and acquired firms influence the curvilinear (inverted U-shaped) relationship between relatedness and post-acquisition innovative performance. Using a relatedness index based on industry co-occurrence in a sample of 1,736 Dutch acquisitions, we...... find that acquirer's internal R&D and acquisition experience, and the small size of acquired firms, help to reach a balance between exploration of novelty and exploitation of synergies in unrelated acquisitions, and to achieve higher post-acquisition performance. However, while the acquirer's R......&D increases flexibility in the acquisition process in presence of deviations from the optimal level of relatedness, acquisition experience may enhance rigidities....
A systematic data acquisition and mining strategy for chemical profiling of Aster tataricus rhizoma (Ziwan) by UHPLC-Q-TOF-MS and the corresponding anti-depressive activity screening.

Science.gov (United States)

Sun, Yupeng; Li, Li; Liao, Man; Su, Min; Wan, Changchen; Zhang, Lantong; Zhang, Hailin

2018-05-30

In this study, a systematic data acquisition and mining strategy aimed at the traditional Chinese medicine (TCM) complex system based on ultra high-performance liquid chromatography coupled with quadrupole time of flight mass spectrometry (UHPLC-Q-TOF-MS) was reported. The workflow of this strategy is as follows: First, the high resolution mass data are acquired by both data-dependent acquisition mode (DDA) and data-independent acquisition mode (DIA). Then a global data mining that combined targeted and non-targeted compound finding is applied to analyze mass spectral data. Furthermore, some assistant tools, such as key product ions (KPIs), are employed for compound hunting and identification. The TCM Ziwan (ZW, Aster tataricus rhizoma) was used to illustrate this strategy for the first time. In this research, total 131 compounds including organic acids, peptides, terpenes, steroids, flavonoids, coumarins, anthraquinones and aldehydes were identified or tentatively characterized in ZW based on accurate mass measurements within ±5 ppm error, and 50 of them were unambiguously confirmed by comparing standard compounds. Afterwards, based on the traditional Chinese medical theory and the key determinants of firing patterns of ventral tegmental area (VTA) dopamine (DA) neurons in the development of depression, the confirmed compounds were subsequently evaluated the pharmacological effect of activity of VTA DA neurons and anti-depressive efficacy. This research provided not only a chemical profiling for further in vivo study of ZW, but also an efficient data acquisition and mining strategy to profile the chemical constituents and find new bioactive substances for other TCM complex system. Copyright © 2018 Elsevier B.V. All rights reserved.
Ensemble Data Mining Methods

Data.gov (United States)

National Aeronautics and Space Administration — Ensemble Data Mining Methods, also known as Committee Methods or Model Combiners, are machine learning methods that leverage the power of multiple models to achieve...
PRIVACY PRESERVING DATA MINING USING MULTIPLE OBJECTIVE OPTIMIZATION

Directory of Open Access Journals (Sweden)

V. Shyamala Susan

2016-10-01

Full Text Available Privacy preservation is that the most targeted issue in information publication, because the sensitive data shouldn't be leaked. For this sake, several privacy preservation data mining algorithms are proposed. In this work, feature selection using evolutionary algorithm and data masking coupled with slicing is treated as a multiple objective optimisation to preserve privacy. To start with, Genetic Algorithm (GA is carried out over the datasets to perceive the sensitive attributes and prioritise the attributes for treatment as per their determined sensitive level. In the next phase, to distort the data, noise is added to the higher level sensitive value using Hybrid Data Transformation (HDT method. In the following phase slicing algorithm groups the correlated attributes organized and by this means reduces the dimensionality by retaining the Advanced Clustering Algorithm (ACA. With the aim of getting the optimal dimensions of buckets, tuple segregating is accomplished by Metaheuristic Firefly Algorithm (MFA. The investigational consequences imply that the anticipated technique can reserve confidentiality and therefore the information utility is additionally high. Slicing algorithm allows the protection of association and usefulness in which effects in decreasing the information dimensionality and information loss. Performance analysis is created over OCC 7 and OCC 15 and our optimization method proves its effectiveness over two totally different datasets by showing 92.98% and 96.92% respectively.
Technological Similarity, Post-acquisition R&D Reorganization, and Innovation Performance in Horizontal Acquisition

DEFF Research Database (Denmark)

Colombo, Massimo G.; Rabbiosi, Larissa

2014-01-01

This paper aims to disentangle the mechanisms through which technological similarity between acquiring and acquired firms influences innovation in horizontal acquisitions. We develop a theoretical model that links technological similarity to: (i) two key aspects of post-acquisition reorganization...... of acquired R&D operations – the rationalization of the R&D operations and the replacement of the R&D top manager, and (ii) two intermediate effects that are closely associated with the post-acquisition innovation performance of the combined firm – improvements in R&D productivity and disruptions in R......&D personnel. We rely on PLS techniques to test our theoretical model using detailed information on 31 horizontal acquisitions in high- and medium-tech industries. Our results indicate that in horizontal acquisitions, technological similarity negatively affects post-acquisition innovation performance...
Mining the Mind Research Network: A Novel Framework for Exploring Large Scale, Heterogeneous Translational Neuroscience Research Data Sources

Science.gov (United States)

Bockholt, Henry J.; Scully, Mark; Courtney, William; Rachakonda, Srinivas; Scott, Adam; Caprihan, Arvind; Fries, Jill; Kalyanam, Ravi; Segall, Judith M.; de la Garza, Raul; Lane, Susan; Calhoun, Vince D.

2009-01-01

A neuroinformatics (NI) system is critical to brain imaging research in order to shorten the time between study conception and results. Such a NI system is required to scale well when large numbers of subjects are studied. Further, when multiple sites participate in research projects organizational issues become increasingly difficult. Optimized NI applications mitigate these problems. Additionally, NI software enables coordination across multiple studies, leveraging advantages potentially leading to exponential research discoveries. The web-based, Mind Research Network (MRN), database system has been designed and improved through our experience with 200 research studies and 250 researchers from seven different institutions. The MRN tools permit the collection, management, reporting and efficient use of large scale, heterogeneous data sources, e.g., multiple institutions, multiple principal investigators, multiple research programs and studies, and multimodal acquisitions. We have collected and analyzed data sets on thousands of research participants and have set up a framework to automatically analyze the data, thereby making efficient, practical data mining of this vast resource possible. This paper presents a comprehensive framework for capturing and analyzing heterogeneous neuroscience research data sources that has been fully optimized for end-users to perform novel data mining. PMID:20461147
Mining the mind research network: a novel framework for exploring large scale, heterogeneous translational neuroscience research data sources.

Directory of Open Access Journals (Sweden)

Henry Jeremy Bockholt

2010-04-01

Full Text Available A neuroinformatics (NI system is critical to brain imaging research in order to shorten the time between study conception and results. Such a NI system is required to scale well when large numbers of subjects are studied. Further, when multiple sites participate in research projects organizational issues become increasingly difficult. Optimized NI applications mitigate these problems. Additionally, NI software enables coordination across multiple studies, leveraging advantages potentially leading to exponential research discoveries. The web-based, Mind Research Network (MRN, database system has been designed and improved through our experience with 200 research studies and 250 researchers from 7 different institutions. The MRN tools permit the collection, management, reporting and efficient use of large scale, heterogeneous data sources, e.g., multiple institutions, multiple principal investigators, multiple research programs and studies, and multimodal acquisitions. We have collected and analyzed data sets on thousands of research participants and have set up a framework to automatically analyze the data, thereby making efficient, practical data mining of this vast resource possible. This paper presents a comprehensive framework for capturing and analyzing heterogeneous neuroscience research data sources that has been fully optimized for end-users to perform novel data mining.
Integrated data acquisition, storage, retrieval and processing using the COMPASS DataBase (CDB)

Energy Technology Data Exchange (ETDEWEB)

Urban, J., E-mail: urban@ipp.cas.cz [Institute of Plasma Physics AS CR, v.v.i., Za Slovankou 3, 182 00 Praha 8 (Czech Republic); Pipek, J.; Hron, M. [Institute of Plasma Physics AS CR, v.v.i., Za Slovankou 3, 182 00 Praha 8 (Czech Republic); Janky, F.; Papřok, R.; Peterka, M. [Institute of Plasma Physics AS CR, v.v.i., Za Slovankou 3, 182 00 Praha 8 (Czech Republic); Department of Surface and Plasma Science, Faculty of Mathematics and Physics, Charles University in Prague, V Holešovičkách 2, 180 00 Praha 8 (Czech Republic); Duarte, A.S. [Instituto de Plasmas e Fusão Nuclear, Instituto Superior Técnico, Universidade Técnica de Lisboa, 1049-001 Lisboa (Portugal)

2014-05-15

Highlights: • CDB is used as a new data storage solution for the COMPASS tokamak. • The software is light weight, open, fast and easily extensible and scalable. • CDB seamlessly integrates with any data acquisition system. • Rich metadata are stored for physics signals. • Data can be processed automatically, based on dependence rules. - Abstract: We present a complex data handling system for the COMPASS tokamak, operated by IPP ASCR Prague, Czech Republic [1]. The system, called CDB (COMPASS DataBase), integrates different data sources as an assortment of data acquisition hardware and software from different vendors is used. Based on widely available open source technologies wherever possible, CDB is vendor and platform independent and it can be easily scaled and distributed. The data is directly stored and retrieved using a standard NAS (Network Attached Storage), hence independent of the particular technology; the description of the data (the metadata) is recorded in a relational database. Database structure is general and enables the inclusion of multi-dimensional data signals in multiple revisions (no data is overwritten). This design is inherently distributed as the work is off-loaded to the clients. Both NAS and database can be implemented and optimized for fast local access as well as secure remote access. CDB is implemented in Python language; bindings for Java, C/C++, IDL and Matlab are provided. Independent data acquisitions systems as well as nodes managed by FireSignal [2] are all integrated using CDB. An automated data post-processing server is a part of CDB. Based on dependency rules, the server executes, in parallel if possible, prescribed post-processing tasks.
A nuclear data acquisition system flow control model

International Nuclear Information System (INIS)

Hack, S.N.

1988-01-01

A general Petri Net representation of a nuclear data acquisition system model is presented. This model provides for the unique requirements of a nuclear data acquisition system including the capabilities of concurrently acquiring asynchronous and synchronous data, of providing multiple priority levels of flow control arbitration, and of permitting multiple input sources to reside at the same priority without the problem of channel lockout caused by a high rate data source. Finally, a previously implemented gamma camera/physiological signal data acquisition system is described using the models presented
An Application of Multithreaded Data Mining in Educational Leadership Research

OpenAIRE

Fikis, David; Wang, Yinying; Bowers, Alex

2015-01-01

This study aims to apply high-performance computing to educational leadership research. Specifically, we applied an array of data acquisition and analytical techniques to the field of educational leadership research, including text data mining, probabiblistic topic modeling, and the use of software (CasperJS, GNU utilities, R, etc.) as well as hardware (the VELA batch computer and the multi-threaded data mining environment).

Multi spectral scaling data acquisition system

International Nuclear Information System (INIS)

Behere, Anita; Patil, R.D.; Ghodgaonkar, M.D.; Gopalakrishnan, K.R.

1997-01-01

In nuclear spectroscopy applications, it is often desired to acquire data at high rate with high resolution. With the availability of low cost computers, it is possible to make a powerful data acquisition system with minimum hardware and software development, by designing a PC plug-in acquisition board. But in using the PC processor for data acquisition, the PC can not be used as a multitasking node. Keeping this in view, PC plug-in acquisition boards with on-board processor find tremendous applications. Transputer based data acquisition board has been designed which can be configured as a high count rate pulse height MCA or as a Multi Spectral Scaler. Multi Spectral Scaling (MSS) is a new technique, in which multiple spectra are acquired in small time frames and are then analyzed. This paper describes the details of this multi spectral scaling data acquisition system. 2 figs
Data mining, mining data : energy consumption modelling

Energy Technology Data Exchange (ETDEWEB)

Dessureault, S. [Arizona Univ., Tucson, AZ (United States)

2007-09-15

Most modern mining operations are accumulating large amounts of data on production and business processes. Data, however, provides value only if it can be translated into information that appropriate users can utilize. This paper emphasized that a new technological focus should emerge, notably how to concentrate data into information; analyze information sufficiently to become knowledge; and, act on that knowledge. Researchers at the Mining Information Systems and Operations Management (MISOM) laboratory at the University of Arizona have created a method to transform data into action. The data-to-action approach was exercised in the development of an energy consumption model (ECM), in partnership with a major US-based copper mining company, 2 software companies, and the MISOM laboratory. The approach begins by integrating several key data sources using data warehousing techniques, and increasing the existing level of integration and data cleaning. An online analytical processing (OLAP) cube was also created to investigate the data and identify a subset of several million records. Data mining algorithms were applied using the information that was isolated by the OLAP cube. The data mining results showed that traditional cost drivers of energy consumption are poor predictors. A comparison was made between traditional methods of predicting energy consumption and the prediction formed using data mining. Traditionally, in the mines for which data were available, monthly averages of tons and distance are used to predict diesel fuel consumption. However, this article showed that new information technology can be used to incorporate many more variables into the budgeting process, resulting in more accurate predictions. The ECM helped mine planners improve the prediction of energy use through more data integration, measure development, and workflow analysis. 5 refs., 11 figs.
Post-mining safety implementations and environmental aspects of abandoned mine sites in Limousin. 2006 status (and perspectives 2007)

International Nuclear Information System (INIS)

2007-01-01

This document summarizes the actions carried out in 2006 at some French abandoned mine sites: 1 - safety implementations and risks abatement in the framework of post-mining actions: coal mines of Ahun (23) and Argentat (19), antimony mines of Biard (87); 2 - remedial actions at the tin/tungsten mine of Puy-les-Vignes (87) and at the gold mine of Chatelet (23); 3 - 2007 post-mining perspectives; 4 - environmental aspects of abandoned mine sites: gold mines of Chatelet (23), Cheni and Bourneix (87), uranium mines of Haute-Vienne (expertise, control of effluents, financial warranties about tailings storage sites maintenance). (J.S.)
Microbial and geochemical assessment of bauxitic un-mined and post-mined chronosequence soils from Mocho Mountains, Jamaica.

Science.gov (United States)

Lewis, Dawn E; Chauhan, Ashvini; White, John R; Overholt, Will; Green, Stefan J; Jasrotia, Puja; Wafula, Denis; Jagoe, Charles

2012-10-01

Microorganisms are very sensitive to environmental change and can be used to gauge anthropogenic impacts and even predict restoration success of degraded environments. Here, we report assessment of bauxite mining activities on soil biogeochemistry and microbial community structure using un-mined and three post-mined sites in Jamaica. The post-mined soils represent a chronosequence, undergoing restoration since 1987, 1997, and 2007. Soils were collected during dry and wet seasons and analyzed for pH, organic matter (OM), total carbon (TC), nitrogen (TN), and phosphorus. The microbial community structure was assessed through quantitative PCR and massively parallel bacterial ribosomal RNA (rRNA) gene sequencing. Edaphic factors and microbial community composition were analyzed using multivariate statistical approaches and revealed a significant, negative impact of mining on soil that persisted even after greater than 20 years of restoration. Seasonal fluctuations contributed to variation in measured soil properties and community composition, but they were minor in comparison to long-term effects of mining. In both seasons, post-mined soils were higher in pH but OM, TC, and TN decreased. Bacterial rRNA gene analyses demonstrated a general decrease in diversity in post-mined soils and up to a 3-log decrease in rRNA gene abundance. Community composition analyses demonstrated that bacteria from the Proteobacteria (α, β, γ, δ), Acidobacteria, and Firmicutes were abundant in all soils. The abundance of Firmicutes was elevated in newer post-mined soils relative to the un-mined soil, and this contrasted a decrease, relative to un-mined soils, in proteobacterial and acidobacterial rRNA gene abundances. Our study indicates long-lasting impacts of mining activities to soil biogeochemical and microbial properties with impending loss in soil productivity.
Solar Data Mining at Georgia State University

Science.gov (United States)

Angryk, R.; Martens, P. C.; Schuh, M.; Aydin, B.; Kempton, D.; Banda, J.; Ma, R.; Naduvil-Vadukootu, S.; Akkineni, V.; Küçük, A.; Filali Boubrahimi, S.; Hamdi, S. M.

2016-12-01

In this talk we give an overview of research projects related to solar data analysis that are conducted at Georgia State University. We will provide update on multiple advances made by our research team on the analysis of image parameters, spatio-temporal patterns mining, temporal data analysis and our experiences with big, heterogeneous solar data visualization, analysis, processing and storage. We will talk about up-to-date data mining methodologies, and their importance for big data-driven solar physics research.
Drug safety data mining with a tree-based scan statistic.

Science.gov (United States)

Kulldorff, Martin; Dashevsky, Inna; Avery, Taliser R; Chan, Arnold K; Davis, Robert L; Graham, David; Platt, Richard; Andrade, Susan E; Boudreau, Denise; Gunter, Margaret J; Herrinton, Lisa J; Pawloski, Pamala A; Raebel, Marsha A; Roblin, Douglas; Brown, Jeffrey S

2013-05-01

In post-marketing drug safety surveillance, data mining can potentially detect rare but serious adverse events. Assessing an entire collection of drug-event pairs is traditionally performed on a predefined level of granularity. It is unknown a priori whether a drug causes a very specific or a set of related adverse events, such as mitral valve disorders, all valve disorders, or different types of heart disease. This methodological paper evaluates the tree-based scan statistic data mining method to enhance drug safety surveillance. We use a three-million-member electronic health records database from the HMO Research Network. Using the tree-based scan statistic, we assess the safety of selected antifungal and diabetes drugs, simultaneously evaluating overlapping diagnosis groups at different granularity levels, adjusting for multiple testing. Expected and observed adverse event counts were adjusted for age, sex, and health plan, producing a log likelihood ratio test statistic. Out of 732 evaluated disease groupings, 24 were statistically significant, divided among 10 non-overlapping disease categories. Five of the 10 signals are known adverse effects, four are likely due to confounding by indication, while one may warrant further investigation. The tree-based scan statistic can be successfully applied as a data mining tool in drug safety surveillance using observational data. The total number of statistical signals was modest and does not imply a causal relationship. Rather, data mining results should be used to generate candidate drug-event pairs for rigorous epidemiological studies to evaluate the individual and comparative safety profiles of drugs. Copyright © 2013 John Wiley & Sons, Ltd.
MDSplus data acquisition system

International Nuclear Information System (INIS)

Stillerman, J.A.; Fredian, T.W.; Klare, K.; Manduchi, G.

1997-01-01

MDSplus, a tree based, distributed data acquisition system, was developed in collaboration with the ZTH Group at Los Alamos National Lab and the RFX Group at CNR in Padua, Italy. It is currently in use at MIT, RFX in Padua, TCV at EPFL in Lausanne, and KBSI in South Korea. MDSplus is made up of a set of X/motif based tools for data acquisition and display, as well as diagnostic configuration and management. It is based on a hierarchical experiment description which completely describes the data acquisition and analysis tasks and contains the results from these operations. These tools were designed to operate in a distributed, client/server environment with multiple concurrent readers and writers to the data store. While usually used over a Local Area Network, these tools can be used over the Internet to provide access for remote diagnosticians and even machine operators. An interface to a relational database is provided for storage and management of processed data. IDL is used as the primary data analysis and visualization tool. IDL is a registered trademark of Research Systems Inc. copyright 1996 American Institute of Physics
On Shaft Data Acquisition System (OSDAS)

Science.gov (United States)

Pedings, Marc; DeHart, Shawn; Formby, Jason; Naumann, Charles

2012-01-01

On Shaft Data Acquisition System (OSDAS) is a rugged, compact, multiple-channel data acquisition computer system that is designed to record data from instrumentation while operating under extreme rotational centrifugal or gravitational acceleration forces. This system, which was developed for the Heritage Fuel Air Turbine Test (HFATT) program, addresses the problem of recording multiple channels of high-sample-rate data on most any rotating test article by mounting the entire acquisition computer onboard with the turbine test article. With the limited availability of slip ring wires for power and communication, OSDAS utilizes its own resources to provide independent power and amplification for each instrument. Since OSDAS utilizes standard PC technology as well as shared code interfaces with the next-generation, real-time health monitoring system (SPARTAA Scalable Parallel Architecture for Real Time Analysis and Acquisition), this system could be expanded beyond its current capabilities, such as providing advanced health monitoring capabilities for the test article. High-conductor-count slip rings are expensive to purchase and maintain, yet only provide a limited number of conductors for routing instrumentation off the article and to a stationary data acquisition system. In addition to being limited to a small number of instruments, slip rings are prone to wear quickly, and introduce noise and other undesirable characteristics to the signal data. This led to the development of a system capable of recording high-density instrumentation, at high sample rates, on the test article itself, all while under extreme rotational stress. OSDAS is a fully functional PC-based system with 48 channels of 24-bit, high-sample-rate input channels, phase synchronized, with an onboard storage capacity of over 1/2-terabyte of solid-state storage. This recording system takes a novel approach to the problem of recording multiple channels of instrumentation, integrated with the test
Post-acquisition Integration as Sensemaking: Glimpses of Ambiguity, Confusion, Hypocrisy, and Politicization

OpenAIRE

Vaara, Eero

2003-01-01

Though many studies have examined post-acquisition integration challenges, they have mainly focused on rationalistic explanations for the difficulties encountered in post-acquisition integration. There remains little knowledge of how the ‘irrational’ features of post-acquisition decision-making may impede organizational integration. This study attempts to bridge that gap by examining post-acquisition decision-making from a sensemaking perspective. The paper presents an in-depth analysis of a ...
Data analysis and pattern recognition in multiple databases

CERN Document Server

Adhikari, Animesh; Pedrycz, Witold

2014-01-01

Pattern recognition in data is a well known classical problem that falls under the ambit of data analysis. As we need to handle different data, the nature of patterns, their recognition and the types of data analyses are bound to change. Since the number of data collection channels increases in the recent time and becomes more diversified, many real-world data mining tasks can easily acquire multiple databases from various sources. In these cases, data mining becomes more challenging for several essential reasons. We may encounter sensitive data originating from different sources - those cannot be amalgamated. Even if we are allowed to place different data together, we are certainly not able to analyse them when local identities of patterns are required to be retained. Thus, pattern recognition in multiple databases gives rise to a suite of new, challenging problems different from those encountered before. Association rule mining, global pattern discovery, and mining patterns of select items provide different...
Social big data mining

CERN Document Server

Ishikawa, Hiroshi

2015-01-01

Social Media. Big Data and Social Data. Hypotheses in the Era of Big Data. Social Big Data Applications. Basic Concepts in Data Mining. Association Rule Mining. Clustering. Classification. Prediction. Web Structure Mining. Web Content Mining. Web Access Log Mining, Information Extraction and Deep Web Mining. Media Mining. Scalability and Outlier Detection.
Exploratory analysis of textual data from the Mother and Child Handbook using the text-mining method: Relationships with maternal traits and post-partum depression.

Science.gov (United States)

Matsuda, Yoshio; Manaka, Tomoko; Kobayashi, Makiko; Sato, Shuhei; Ohwada, Michitaka

2016-06-01

The aim of the present study was to examine the possibility of screening apprehensive pregnant women and mothers at risk for post-partum depression from an analysis of the textual data in the Mother and Child Handbook by using the text-mining method. Uncomplicated pregnant women (n = 58) were divided into two groups according to State-Trait Anxiety Inventory grade (high trait [group I, n = 21] and low trait [group II, n = 37]) or Edinburgh Postnatal Depression Scale score (high score [group III, n = 15] and low score [group IV, n = 43]). An exploratory analysis of the textual data from the Maternal and Child Handbook was conducted using the text-mining method with the Word Miner software program. A comparison of the 'structure elements' was made between the two groups. The number of structure elements extracted by separated words from text data was 20 004 and the number of structure elements with a threshold of 2 or more as an initial value was 1168. Fifteen key words related to maternal anxiety, and six key words related to post-partum depression were extracted. The text-mining method is useful for the exploratory analysis of textual data obtained from pregnant woman, and this screening method has been suggested to be useful for apprehensive pregnant women and mothers at risk for post-partum depression. © 2016 Japan Society of Obstetrics and Gynecology.
Continued Data Acquisition Development

Energy Technology Data Exchange (ETDEWEB)

Schwellenbach, David [National Security Technologies, LLC. (NSTec), Mercury, NV (United States)

2017-11-27

This task focused on improving techniques for integrating data acquisition of secondary particles correlated in time with detected cosmic-ray muons. Scintillation detectors with Pulse Shape Discrimination (PSD) capability show the most promise as a detector technology based on work in FY13. Typically PSD parameters are determined prior to an experiment and the results are based on these parameters. By saving data in list mode, including the fully digitized waveform, any experiment can effectively be replayed to adjust PSD and other parameters for the best data capture. List mode requires time synchronization of two independent data acquisitions (DAQ) systems: the muon tracker and the particle detector system. Techniques to synchronize these systems were studied. Two basic techniques were identified: real time mode and sequential mode. Real time mode is the preferred system but has proven to be a significant challenge since two FPGA systems with different clocking parameters must be synchronized. Sequential processing is expected to work with virtually any DAQ but requires more post processing to extract the data.
Knowledge-Based Reinforcement Learning for Data Mining

Science.gov (United States)

Kudenko, Daniel; Grzes, Marek

Data Mining is the process of extracting patterns from data. Two general avenues of research in the intersecting areas of agents and data mining can be distinguished. The first approach is concerned with mining an agent’s observation data in order to extract patterns, categorize environment states, and/or make predictions of future states. In this setting, data is normally available as a batch, and the agent’s actions and goals are often independent of the data mining task. The data collection is mainly considered as a side effect of the agent’s activities. Machine learning techniques applied in such situations fall into the class of supervised learning. In contrast, the second scenario occurs where an agent is actively performing the data mining, and is responsible for the data collection itself. For example, a mobile network agent is acquiring and processing data (where the acquisition may incur a certain cost), or a mobile sensor agent is moving in a (perhaps hostile) environment, collecting and processing sensor readings. In these settings, the tasks of the agent and the data mining are highly intertwined and interdependent (or even identical). Supervised learning is not a suitable technique for these cases. Reinforcement Learning (RL) enables an agent to learn from experience (in form of reward and punishment for explorative actions) and adapt to new situations, without a teacher. RL is an ideal learning technique for these data mining scenarios, because it fits the agent paradigm of continuous sensing and acting, and the RL agent is able to learn to make decisions on the sampling of the environment which provides the data. Nevertheless, RL still suffers from scalability problems, which have prevented its successful use in many complex real-world domains. The more complex the tasks, the longer it takes a reinforcement learning algorithm to converge to a good solution. For many real-world tasks, human expert knowledge is available. For example, human
Self-calibrated multiple-echo acquisition with radial trajectories using the conjugate gradient method (SMART-CG).

Science.gov (United States)

Jung, Youngkyoo; Samsonov, Alexey A; Bydder, Mark; Block, Walter F

2011-04-01

To remove phase inconsistencies between multiple echoes, an algorithm using a radial acquisition to provide inherent phase and magnitude information for self correction was developed. The information also allows simultaneous support for parallel imaging for multiple coil acquisitions. Without a separate field map acquisition, a phase estimate from each echo in multiple echo train was generated. When using a multiple channel coil, magnitude and phase estimates from each echo provide in vivo coil sensitivities. An algorithm based on the conjugate gradient method uses these estimates to simultaneously remove phase inconsistencies between echoes, and in the case of multiple coil acquisition, simultaneously provides parallel imaging benefits. The algorithm is demonstrated on single channel, multiple channel, and undersampled data. Substantial image quality improvements were demonstrated. Signal dropouts were completely removed and undersampling artifacts were well suppressed. The suggested algorithm is able to remove phase cancellation and undersampling artifacts simultaneously and to improve image quality of multiecho radial imaging, the important technique for fast three-dimensional MRI data acquisition. Copyright © 2011 Wiley-Liss, Inc.
Jefferson Lab's Distributed Data Acquisition

International Nuclear Information System (INIS)

Trent Allison; Thomas Powers

2006-01-01

Jefferson Lab's Continuous Electron Beam Accelerator Facility (CEBAF) occasionally experiences fast intermittent beam instabilities that are difficult to isolate and result in downtime. The Distributed Data Acquisition (Dist DAQ) system is being developed to detect and quickly locate such instabilities. It will consist of multiple Ethernet based data acquisition chassis distributed throughout the seven-eights of a mile CEBAF site. Each chassis will monitor various control system signals that are only available locally and/or monitored by systems with small bandwidths that cannot identify fast transients. The chassis will collect data at rates up to 40 Msps in circular buffers that can be frozen and unrolled after an event trigger. These triggers will be derived from signals such as periodic timers or accelerator faults and be distributed via a custom fiber optic event trigger network. This triggering scheme will allow all the data acquisition chassis to be triggered simultaneously and provide a snapshot of relevant CEBAF control signals. The data will then be automatically analyzed for frequency content and transients to determine if and where instabilities exist
Mining Diagnostic Assessment Data for Concept Similarity

Science.gov (United States)

Madhyastha, Tara; Hunt, Earl

2009-01-01

This paper introduces a method for mining multiple-choice assessment data for similarity of the concepts represented by the multiple choice responses. The resulting similarity matrix can be used to visualize the distance between concepts in a lower-dimensional space. This gives an instructor a visualization of the relative difficulty of concepts…
Distributed multiprotocol acquisition network for environmental data

Science.gov (United States)

Barone, Fabrizio; De Rosa, Rosario; Milano, Leopoldo; Qipiani, Ketevan

2003-03-01

The acquisition and storage of large amount of data coming from distributed environmental sensors of different kind can be solved with the aid of a network between the acquisition subsystems, but problems can arise if they are not homogeneous. In this case the network should be as flexible as possible to ensure modularity and connectivity. In this work we describe the development and testing of a network based acquisition system. The network uses, where possible, commercial products, based on different standards, in order to increase the availability of its components, as well as its modularity. In addition it is completely independent from proprietary hardware and software products. In particular we tested an acquisition network based on multiple transmission protocol, like wireless and cabled RS232 and Fast Ethernet, which includes the acquisition, archiving and data analysis systems. Each acquisition subsystem can get time from satellite using GPS, and is able to monitor seismic activity, temperature, pressure, humidity and electromagnetic data. The sampling frequency and the dynamics of the acquired data can be matched to the characteristics of each probe. All the acquisition stations can use different platform as well as different operating systems. Tests have been performed to evaluate the capability of long acquisition periods and the fault tolerance of the whole system.
Modeling the effects of longwall mining on the ground water system

International Nuclear Information System (INIS)

Matetic, R.J.; Liu, J.; Elsworth, D.

1995-01-01

The effects of longwall mining on the local ground water regime are determined through field monitoring and numerical modeling. Field displacement data were obtained from multiple-position borehole extensometer (MPBX's) and survey monuments, combined with hydraulic drawdown and recovery tests completed both pre- and post-mining. Despite the development of significant mining induced displacements, the resulting effect on long-term water budgets was surprisingly small. Coupled flow-deformation modeling of the site was able to adequately define the post-mining mechanical and hydraulic response, including resulting conductivity magnitudes and water budgets. 6 refs., 5 figs., 2 tabs
Real-time multiple networked viewer capability of the DIII-D EC data acquisition system

International Nuclear Information System (INIS)

Ponce, D.; Gorelov, I.A.; Chiu, H.K.; Baity, F.W.

2005-01-01

A data acquisition system (DAS) which permits real-time viewing by multiple locally networked operators is being implemented for the electron cyclotron (EC) heating and current drive system at DIII-D. The DAS is expected to demonstrate performance equivalent to standalone oscilloscopes. Participation by remote viewers, including throughout the greater DIII-D facility, can also be incorporated. The real-time system uses one computer-controlled DAS per gyrotron. The DAS computers send their data to a central data server using individual and dedicated 200 Mbps fully duplexed Ethernet connections. The server has a dedicated 10 krpm hard drive for each gyrotron DAS. Selected channels can then be reprocessed and distributed to viewers over a standard local area network (LAN). They can also be bridged from the LAN to the internet. Calculations indicate that the hardware will support real-time writing of each channel at full resolution to the server hard drives. The data will be re-sampled for distribution to multiple viewers over the LAN in real-time. The hardware for this system is in place. The software is under development. This paper will present the design details and up-to-date performance metrics of the system

Multiple multichannel spectra acquisition and processing system with intelligent interface

International Nuclear Information System (INIS)

Chen Ying; Wei Yixiang; Qu Jianshi; Zheng Futang; Xu Shengkui; Xie Yuanming; Qu Xing; Ji Weitong; Qiu Xuehua

1986-01-01

A Multiple multichannel spectra acquisition and processing system with intelligent interface is described. Sixteen spectra measured with various lengths, channel widths, back biases and acquisition times can be identified and collected by the intelligent interface simultaneously while the connected computer is doing data processing. The execution time for the Ge(Li) gamma-ray spectrum analysis software on IBM PC-XT is about 55 seconds
Mining association patterns of drug-interactions using post marketing FDA's spontaneous reporting data.

Science.gov (United States)

Ibrahim, Heba; Saad, Amr; Abdo, Amany; Sharaf Eldin, A

2016-04-01

Pharmacovigilance (PhV) is an important clinical activity with strong implications for population health and clinical research. The main goal of PhV is the timely detection of adverse drug events (ADEs) that are novel in their clinical nature, severity and/or frequency. Drug interactions (DI) pose an important problem in the development of new drugs and post marketing PhV that contribute to 6-30% of all unexpected ADEs. Therefore, the early detection of DI is vital. Spontaneous reporting systems (SRS) have served as the core data collection system for post marketing PhV since the 1960s. The main objective of our study was to particularly identify signals of DI from SRS. In addition, we are presenting an optimized tailored mining algorithm called "hybrid Apriori". The proposed algorithm is based on an optimized and modified association rule mining (ARM) approach. A hybrid Apriori algorithm has been applied to the SRS of the United States Food and Drug Administration's (U.S. FDA) adverse events reporting system (FAERS) in order to extract significant association patterns of drug interaction-adverse event (DIAE). We have assessed the resulting DIAEs qualitatively and quantitatively using two different triage features: a three-element taxonomy and three performance metrics. These features were applied on two random samples of 100 interacting and 100 non-interacting DIAE patterns. Additionally, we have employed logistic regression (LR) statistic method to quantify the magnitude and direction of interactions in order to test for confounding by co-medication in unknown interacting DIAE patterns. Hybrid Apriori extracted 2933 interacting DIAE patterns (including 1256 serious ones) and 530 non-interacting DIAE patterns. Referring to the current knowledge using four different reliable resources of DI, the results showed that the proposed method can extract signals of serious interacting DIAEs. Various association patterns could be identified based on the relationships among
Data mining in radiology

International Nuclear Information System (INIS)

Kharat, Amit T; Singh, Amarjit; Kulkarni, Vilas M; Shah, Digish

2014-01-01

Data mining facilitates the study of radiology data in various dimensions. It converts large patient image and text datasets into useful information that helps in improving patient care and provides informative reports. Data mining technology analyzes data within the Radiology Information System and Hospital Information System using specialized software which assesses relationships and agreement in available information. By using similar data analysis tools, radiologists can make informed decisions and predict the future outcome of a particular imaging finding. Data, information and knowledge are the components of data mining. Classes, Clusters, Associations, Sequential patterns, Classification, Prediction and Decision tree are the various types of data mining. Data mining has the potential to make delivery of health care affordable and ensure that the best imaging practices are followed. It is a tool for academic research. Data mining is considered to be ethically neutral, however concerns regarding privacy and legality exists which need to be addressed to ensure success of data mining
Data Mining Methods Applied to Flight Operations Quality Assurance Data: A Comparison to Standard Statistical Methods

Science.gov (United States)

Stolzer, Alan J.; Halford, Carl

2007-01-01

In a previous study, multiple regression techniques were applied to Flight Operations Quality Assurance-derived data to develop parsimonious model(s) for fuel consumption on the Boeing 757 airplane. The present study examined several data mining algorithms, including neural networks, on the fuel consumption problem and compared them to the multiple regression results obtained earlier. Using regression methods, parsimonious models were obtained that explained approximately 85% of the variation in fuel flow. In general data mining methods were more effective in predicting fuel consumption. Classification and Regression Tree methods reported correlation coefficients of .91 to .92, and General Linear Models and Multilayer Perceptron neural networks reported correlation coefficients of about .99. These data mining models show great promise for use in further examining large FOQA databases for operational and safety improvements.
Preliminary study on X-ray fluorescence computed tomography imaging of gold nanoparticles: Acceleration of data acquisition by multiple pinholes scheme

Science.gov (United States)

Sasaya, Tenta; Sunaguchi, Naoki; Seo, Seung-Jum; Hyodo, Kazuyuki; Zeniya, Tsutomu; Kim, Jong-Ki; Yuasa, Tetsuya

2018-04-01

Gold nanoparticles (GNPs) have recently attracted attention in nanomedicine as novel contrast agents for cancer imaging. A decisive tomographic imaging technique has not yet been established to depict the 3-D distribution of GNPs in an object. An imaging technique known as pinhole-based X-ray fluorescence computed tomography (XFCT) is a promising method that can be used to reconstruct the distribution of GNPs from the X-ray fluorescence emitted by GNPs. We address the acceleration of data acquisition in pinhole-based XFCT for preclinical use using a multiple pinhole scheme. In this scheme, multiple projections are simultaneously acquired through a multi-pinhole collimator with a 2-D detector and full-field volumetric beam to enhance the signal-to-noise ratio of the projections; this enables fast data acquisition. To demonstrate the efficacy of this method, we performed an imaging experiment using a physical phantom with an actual multi-pinhole XFCT system that was constructed using the beamline AR-NE7A at KEK. The preliminary study showed that the multi-pinhole XFCT achieved a data acquisition time of 20 min at a theoretical detection limit of approximately 0.1 Au mg/ml and at a spatial resolution of 0.4 mm.
A Proposed Data Fusion Architecture for Micro-Zone Analysis and Data Mining

Energy Technology Data Exchange (ETDEWEB)

Kevin McCarthy; Milos Manic

2012-08-01

Data Fusion requires the ability to combine or “fuse” date from multiple data sources. Time Series Analysis is a data mining technique used to predict future values from a data set based upon past values. Unlike other data mining techniques, however, Time Series places special emphasis on periodicity and how seasonal and other time-based factors tend to affect trends over time. One of the difficulties encountered in developing generic time series techniques is the wide variability of the data sets available for analysis. This presents challenges all the way from the data gathering stage to results presentation. This paper presents an architecture designed and used to facilitate the collection of disparate data sets well suited to Time Series analysis as well as other predictive data mining techniques. Results show this architecture provides a flexible, dynamic framework for the capture and storage of a myriad of dissimilar data sets and can serve as a foundation from which to build a complete data fusion architecture.
The MAST data acquisition upgrade

International Nuclear Information System (INIS)

McArdle, G.J.; Shibaev, Sergei; Storrs, John; Thomas-Davies, Nigel; Stephen, Robert

2010-01-01

A programme has begun on MAST to replace its ageing CAMAC and VME based data acquisition systems with new modern hardware which, together with several improvements in the supporting infrastructure, will provide support for faster data acquisition rates, longer-pulse operation, faster data access and higher reliability. The main principle of the upgrade was to use commercial off-the-shelf (COTS) hardware and well-established standards wherever possible. CompactPCI or PXI was chosen as the digitiser form factor to replace CAMAC/VME, and Ethernet would be used as the means to access all devices. The modular architecture of the MAST data acquisition software framework has helped to minimise the integration effort required to phase in new subsystems and/or new technologies whilst continuing to use the old hardware in other systems. The software framework was updated to allow more versatile use of the network-attached data acquisition devices. The new data acquisition devices had multiple connector types, which created difficulties with the cable interfacing. To resolve this and provide support for easy substitution, a standard connector interface was chosen, based on the most common connector type and pin-out already in use, and several cable assemblies were produced to connect the proprietary interface of the digitiser to the standard interface block. The in-house IDA-3 data storage format is unable to accommodate the larger file sizes and is increasingly difficult to maintain, so it is to be gradually phased out. The NetCDF-4/HDF5 data standard is being adopted as its replacement, thus reducing in-house maintenance whilst providing a data format that is more accessible to the Fusion community. Several other infrastructure upgrades were necessitated by the anticipated increase in data traffic and volume including the Central Timing System, the MAST Ethernet infrastructure and servers for front-end data processing, data storage and data access management. These
Data mining and visualization techniques

Science.gov (United States)

Wong, Pak Chung [Richland, WA; Whitney, Paul [Richland, WA; Thomas, Jim [Richland, WA

2004-03-23

Disclosed are association rule identification and visualization methods, systems, and apparatus. An association rule in data mining is an implication of the form X.fwdarw.Y where X is a set of antecedent items and Y is the consequent item. A unique visualization technique that provides multiple antecedent, consequent, confidence, and support information is disclosed to facilitate better presentation of large quantities of complex association rules.
Advances in research methods for information systems research data mining, data envelopment analysis, value focused thinking

CERN Document Server

Osei-Bryson, Kweku-Muata

2013-01-01

Advances in social science research methodologies and data analytic methods are changing the way research in information systems is conducted. New developments in statistical software technologies for data mining (DM) such as regression splines or decision tree induction can be used to assist researchers in systematic post-positivist theory testing and development. Established management science techniques like data envelopment analysis (DEA), and value focused thinking (VFT) can be used in combination with traditional statistical analysis and data mining techniques to more effectively explore
Non-destructive analysis of sensory traits of dry-cured loins by MRI-computer vision techniques and data mining.

Science.gov (United States)

Caballero, Daniel; Antequera, Teresa; Caro, Andrés; Ávila, María Del Mar; G Rodríguez, Pablo; Perez-Palacios, Trinidad

2017-07-01

Magnetic resonance imaging (MRI) combined with computer vision techniques have been proposed as an alternative or complementary technique to determine the quality parameters of food in a non-destructive way. The aim of this work was to analyze the sensory attributes of dry-cured loins using this technique. For that, different MRI acquisition sequences (spin echo, gradient echo and turbo 3D), algorithms for MRI analysis (GLCM, NGLDM, GLRLM and GLCM-NGLDM-GLRLM) and predictive data mining techniques (multiple linear regression and isotonic regression) were tested. The correlation coefficient (R) and mean absolute error (MAE) were used to validate the prediction results. The combination of spin echo, GLCM and isotonic regression produced the most accurate results. In addition, the MRI data from dry-cured loins seems to be more suitable than the data from fresh loins. The application of predictive data mining techniques on computational texture features from the MRI data of loins enables the determination of the sensory traits of dry-cured loins in a non-destructive way. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.
Factors Associated With Healthcare-Acquired Catheter-Associated Urinary Tract Infections: Analysis Using Multiple Data Sources and Data Mining Techniques.

Science.gov (United States)

Park, Jung In; Bliss, Donna Z; Chi, Chih-Lin; Delaney, Connie W; Westra, Bonnie L

The purpose of this study was to identify factors associated with healthcare-acquired catheter-associated urinary tract infections (HA-CAUTIs) using multiple data sources and data mining techniques. Three data sets were integrated for analysis: electronic health record data from a university hospital in the Midwestern United States was combined with staffing and environmental data from the hospital's National Database of Nursing Quality Indicators and a list of patients with HA-CAUTIs. Three data mining techniques were used for identification of factors associated with HA-CAUTI: decision trees, logistic regression, and support vector machines. Fewer total nursing hours per patient-day, lower percentage of direct care RNs with specialty nursing certification, higher percentage of direct care RNs with associate's degree in nursing, and higher percentage of direct care RNs with BSN, MSN, or doctoral degree are associated with HA-CAUTI occurrence. The results also support the association of the following factors with HA-CAUTI identified by previous studies: female gender; older age (>50 years); longer length of stay; severe underlying disease; glucose lab results (>200 mg/dL); longer use of the catheter; and RN staffing. Additional findings from this study demonstrated that the presence of more nurses with specialty nursing certifications can reduce HA-CAUTI occurrence. While there may be valid reasons for leaving in a urinary catheter, findings show that having a catheter in for more than 48 hours contributes to HA-CAUTI occurrence. Finally, the findings suggest that more nursing hours per patient-day are related to better patient outcomes.
ORELA data acquisition system hardware. Volume 1: introduction

International Nuclear Information System (INIS)

Reynolds, J.W.

1977-01-01

The Oak Ridge Electron Linear Accelerator Facility (ORELA) has been specifically designed as a facility for neutron cross-section measurements by the time-of-flight technique. ORELA was designed so that a number of cross-section experiments can be performed simultaneously. This goal of simultaneous operation of several experiments, a maximum of six to date, has been achieved by using the multiple flight paths radiating from the target room, the multiple flight stations on each flight path, the laboratory facilities surrounding the central data area, and a shared data acquisition computer system. The flight stations contain the fast electronics for initial processing of the nuclear detector signals on a time scale of nanoseconds. The laboratories, and in some cases the flight stations, contain the equipment to digitize the nanosecond detector signals on a time scale of a few microseconds. At this point, the data passes into the ORELA Data Acquisition portion of the ORELA Data Handling System. An introduction to the ORELA Data Acquisition System is given, and the component parts of the system are briefly reviewed. Each specifically designed piece of hardware is briefly described with a simplified block diagram. Modifications to standard peripheral devices are reviewed. A list of drawings and programming notes are also included
Analysis of post-blasting source mechanisms of mining-induced seismic events in Rudna copper mine, Poland

Directory of Open Access Journals (Sweden)

Caputa Alicja

2015-10-01

Full Text Available The exploitation of georesources by underground mining can be responsible for seismic activity in areas considered aseismic. Since strong seismic events are connected with rockburst hazard, it is a continuous requirement to reduce seismic risk. One of the most effective methods to do so is blasting in potentially hazardous mining panels. In this way, small to moderate tremors are provoked and stress accumulation is substantially reduced. In this paper we present an analysis of post-blasting events using Full Moment Tensor (MT inversion at the Rudna mine, Poland, underground seismic network. In addition, we describe the problems we faced when analyzing seismic signals. Our studies show that focal mechanisms for events that occurred after blasts exhibit common features in the MT solution. The strong isotropic and small Double Couple (DC component of the MT, indicate that these events were provoked by detonations. On the other hand, post-blasting MT is considerably different than the MT obtained for strong mining events. We believe that seismological analysis of provoked and unprovoked events can be a very useful tool in confirming the effectiveness of blasting in seismic hazard reduction in mining areas.
Mining gene expression data of multiple sclerosis.

Directory of Open Access Journals (Sweden)

Pi Guo

Full Text Available Microarray produces a large amount of gene expression data, containing various biological implications. The challenge is to detect a panel of discriminative genes associated with disease. This study proposed a robust classification model for gene selection using gene expression data, and performed an analysis to identify disease-related genes using multiple sclerosis as an example.Gene expression profiles based on the transcriptome of peripheral blood mononuclear cells from a total of 44 samples from 26 multiple sclerosis patients and 18 individuals with other neurological diseases (control were analyzed. Feature selection algorithms including Support Vector Machine based on Recursive Feature Elimination, Receiver Operating Characteristic Curve, and Boruta algorithms were jointly performed to select candidate genes associating with multiple sclerosis. Multiple classification models categorized samples into two different groups based on the identified genes. Models' performance was evaluated using cross-validation methods, and an optimal classifier for gene selection was determined.An overlapping feature set was identified consisting of 8 genes that were differentially expressed between the two phenotype groups. The genes were significantly associated with the pathways of apoptosis and cytokine-cytokine receptor interaction. TNFSF10 was significantly associated with multiple sclerosis. A Support Vector Machine model was established based on the featured genes and gave a practical accuracy of ∼86%. This binary classification model also outperformed the other models in terms of Sensitivity, Specificity and F1 score.The combined analytical framework integrating feature ranking algorithms and Support Vector Machine model could be used for selecting genes for other diseases.
Perancangan Data Mining Untuk Analisis Kriteria Nasabah Kredit yang Potensial dan Manfaatnya Untuk Customer Relationship Management Perbankan

Directory of Open Access Journals (Sweden)

Putu Sukma Kurniawan

2016-03-01

Full Text Available The presence of data mining problems caused by the explosion of data experienced by many organizations that have accumulated so many years of data (purchasing data, sales data, customer data, transaction data, and others. Examples of industries that use data mining is the banking industry. There are still many banks using conventional methods in the analysis of their customers. This would lead to high operating costs for the bank. The concept of data mining can help banks to get a better analysis of their customers and also help in making the concept of customer relationship management. Data mining can help bank to create profiling customer. Results or final output obtained if the bank can execute customer relationship management is increasing customer loyalty to the bank, increasing profitability, and reducing customer acquisition costs.
Data mining in Cloud Computing

Directory of Open Access Journals (Sweden)

Ruxandra-Ştefania PETRE

2012-10-01

Full Text Available This paper describes how data mining is used in cloud computing. Data Mining is used for extracting potentially useful information from raw data. The integration of data mining techniques into normal day-to-day activities has become common place. Every day people are confronted with targeted advertising, and data mining techniques help businesses to become more efficient by reducing costs.Data mining techniques and applications are very much needed in the cloud computing paradigm. The implementation of data mining techniques through Cloud computing will allow the users to retrieve meaningful information from virtually integrated data warehouse that reduces the costs of infrastructure and storage.
Data acquisition

International Nuclear Information System (INIS)

Clout, P.N.

1982-01-01

Data acquisition systems are discussed for molecular biology experiments using synchrotron radiation sources. The data acquisition system requirements are considered. The components of the solution are described including hardwired solutions and computer-based solutions. Finally, the considerations for the choice of the computer-based solution are outlined. (U.K.)
Data mining and education.

Science.gov (United States)

Koedinger, Kenneth R; D'Mello, Sidney; McLaughlin, Elizabeth A; Pardos, Zachary A; Rosé, Carolyn P

2015-01-01

An emerging field of educational data mining (EDM) is building on and contributing to a wide variety of disciplines through analysis of data coming from various educational technologies. EDM researchers are addressing questions of cognition, metacognition, motivation, affect, language, social discourse, etc. using data from intelligent tutoring systems, massive open online courses, educational games and simulations, and discussion forums. The data include detailed action and timing logs of student interactions in user interfaces such as graded responses to questions or essays, steps in rich problem solving environments, games or simulations, discussion forum posts, or chat dialogs. They might also include external sensors such as eye tracking, facial expression, body movement, etc. We review how EDM has addressed the research questions that surround the psychology of learning with an emphasis on assessment, transfer of learning and model discovery, the role of affect, motivation and metacognition on learning, and analysis of language data and collaborative learning. For example, we discuss (1) how different statistical assessment methods were used in a data mining competition to improve prediction of student responses to intelligent tutor tasks, (2) how better cognitive models can be discovered from data and used to improve instruction, (3) how data-driven models of student affect can be used to focus discussion in a dialog-based tutoring system, and (4) how machine learning techniques applied to discussion data can be used to produce automated agents that support student learning as they collaborate in a chat room or a discussion board. © 2015 John Wiley & Sons, Ltd.
Data mining applications in healthcare.

Science.gov (United States)

Koh, Hian Chye; Tan, Gerald

2005-01-01

Data mining has been used intensively and extensively by many organizations. In healthcare, data mining is becoming increasingly popular, if not increasingly essential. Data mining applications can greatly benefit all parties involved in the healthcare industry. For example, data mining can help healthcare insurers detect fraud and abuse, healthcare organizations make customer relationship management decisions, physicians identify effective treatments and best practices, and patients receive better and more affordable healthcare services. The huge amounts of data generated by healthcare transactions are too complex and voluminous to be processed and analyzed by traditional methods. Data mining provides the methodology and technology to transform these mounds of data into useful information for decision making. This article explores data mining applications in healthcare. In particular, it discusses data mining and its applications within healthcare in major areas such as the evaluation of treatment effectiveness, management of healthcare, customer relationship management, and the detection of fraud and abuse. It also gives an illustrative example of a healthcare data mining application involving the identification of risk factors associated with the onset of diabetes. Finally, the article highlights the limitations of data mining and discusses some future directions.
Data mining for signals in spontaneous reporting databases: proceed with caution.

Science.gov (United States)

Stephenson, Wendy P; Hauben, Manfred

2007-04-01

To provide commentary and points of caution to consider before incorporating data mining as a routine component of any Pharmacovigilance program, and to stimulate further research aimed at better defining the predictive value of these new tools as well as their incremental value as an adjunct to traditional methods of post-marketing surveillance. Commentary includes review of current data mining methodologies employed and their limitations, caveats to consider in the use of spontaneous reporting databases and caution against over-confidence in the results of data mining. Future research should focus on more clearly delineating the limitations of the various quantitative approaches as well as the incremental value that they bring to traditional methods of pharmacovigilance.

Security Measures in Data Mining

OpenAIRE

Anish Gupta; Vimal Bibhu; Rashid Hussain

2012-01-01

Data mining is a technique to dig the data from the large databases for analysis and executive decision making. Security aspect is one of the measure requirement for data mining applications. In this paper we present security requirement measures for the data mining. We summarize the requirements of security for data mining in tabular format. The summarization is performed by the requirements with different aspects of security measure of data mining. The performances and outcomes are determin...
Data mining for service

CERN Document Server

2014-01-01

Virtually all nontrivial and modern service related problems and systems involve data volumes and types that clearly fall into what is presently meant as "big data", that is, are huge, heterogeneous, complex, distributed, etc. Data mining is a series of processes which include collecting and accumulating data, modeling phenomena, and discovering new information, and it is one of the most important steps to scientific analysis of the processes of services. Data mining application in services requires a thorough understanding of the characteristics of each service and knowledge of the compatibility of data mining technology within each particular service, rather than knowledge only in calculation speed and prediction accuracy. Varied examples of services provided in this book will help readers understand the relation between services and data mining technology. This book is intended to stimulate interest among researchers and practitioners in the relation between data mining technology and its application to ...
Compositional mining of multiple object API protocols through state abstraction.

Science.gov (United States)

Dai, Ziying; Mao, Xiaoguang; Lei, Yan; Qi, Yuhua; Wang, Rui; Gu, Bin

2013-01-01

API protocols specify correct sequences of method invocations. Despite their usefulness, API protocols are often unavailable in practice because writing them is cumbersome and error prone. Multiple object API protocols are more expressive than single object API protocols. However, the huge number of objects of typical object-oriented programs poses a major challenge to the automatic mining of multiple object API protocols: besides maintaining scalability, it is important to capture various object interactions. Current approaches utilize various heuristics to focus on small sets of methods. In this paper, we present a general, scalable, multiple object API protocols mining approach that can capture all object interactions. Our approach uses abstract field values to label object states during the mining process. We first mine single object typestates as finite state automata whose transitions are annotated with states of interacting objects before and after the execution of the corresponding method and then construct multiple object API protocols by composing these annotated single object typestates. We implement our approach for Java and evaluate it through a series of experiments.
Implications of Emerging Data Mining

Science.gov (United States)

Kulathuramaiyer, Narayanan; Maurer, Hermann

Data Mining describes a technology that discovers non-trivial hidden patterns in a large collection of data. Although this technology has a tremendous impact on our lives, the invaluable contributions of this invisible technology often go unnoticed. This paper discusses advances in data mining while focusing on the emerging data mining capability. Such data mining applications perform multidimensional mining on a wide variety of heterogeneous data sources, providing solutions to many unresolved problems. This paper also highlights the advantages and disadvantages arising from the ever-expanding scope of data mining. Data Mining augments human intelligence by equipping us with a wealth of knowledge and by empowering us to perform our daily tasks better. As the mining scope and capacity increases, users and organizations become more willing to compromise privacy. The huge data stores of the ‚master miners` allow them to gain deep insights into individual lifestyles and their social and behavioural patterns. Data integration and analysis capability of combining business and financial trends together with the ability to deterministically track market changes will drastically affect our lives.
Review of Data Mining Techniques for Churn Prediction in Telecom

Directory of Open Access Journals (Sweden)

Vishal Mahajan

2015-12-01

service. This data can be usefully mined for churn analysis and prediction. Significant research had been undertaken by researchers worldwide to understand the data mining practices that can be used for predicting customer churn. This paper provides a review of around 100 recent journal articles starting from year 2000 to present the various data mining techniques used in multiple customer based churn models. It then summarizes the existing telecom literature by highlighting the sample size used, churn variables employed and the findings of different DM techniques. Finally, we list the most popular techniques for churn prediction in telecom as decision trees, regression analysis and clustering, thereby providing a roadmap to new researchers to build upon novel churn management models.
Design database for quantitative trait loci (QTL) data warehouse, data mining, and meta-analysis.

Science.gov (United States)

Hu, Zhi-Liang; Reecy, James M; Wu, Xiao-Lin

2012-01-01

A database can be used to warehouse quantitative trait loci (QTL) data from multiple sources for comparison, genomic data mining, and meta-analysis. A robust database design involves sound data structure logistics, meaningful data transformations, normalization, and proper user interface designs. This chapter starts with a brief review of relational database basics and concentrates on issues associated with curation of QTL data into a relational database, with emphasis on the principles of data normalization and structure optimization. In addition, some simple examples of QTL data mining and meta-analysis are included. These examples are provided to help readers better understand the potential and importance of sound database design.
Microcomputer-assisted real time data acquisition for a uranium mine ventilation experiment

International Nuclear Information System (INIS)

Fernald, M.G.; Oberholtzer, J.E.

1981-01-01

A description of the Apple II computer system used in the field to process data acquisition system (DAS) radon levels data in real time is presented. Computer software converts each measurement to appropriate engineering units. The computer also calculates 1-hour and 8-hour running averages of all converted data and prints those results as soon as they are obtained on a line printer located at the test site for immediate inspection
Data mining for bioinformatics applications

CERN Document Server

Zengyou, He

2015-01-01

Data Mining for Bioinformatics Applications provides valuable information on the data mining methods have been widely used for solving real bioinformatics problems, including problem definition, data collection, data preprocessing, modeling, and validation. The text uses an example-based method to illustrate how to apply data mining techniques to solve real bioinformatics problems, containing 45 bioinformatics problems that have been investigated in recent research. For each example, the entire data mining process is described, ranging from data preprocessing to modeling and result validation. Provides valuable information on the data mining methods have been widely used for solving real bioinformatics problems Uses an example-based method to illustrate how to apply data mining techniques to solve real bioinformatics problems Contains 45 bioinformatics problems that have been investigated in recent research.
Analyzing clinical symptoms in multiple sclerosis using data mining

Directory of Open Access Journals (Sweden)

Zahra Raeisi

2017-04-01

Full Text Available Background: One of the today most common and incurable diseases that is associated with central neural system is ‘MS’ disease. Multiple sclerosis (MS is a demyelinating disease in which the insulating covers of nerve cells in the brain and spinal cord are damaged. In this disease become apparent a wide spectrum of symptoms such as lose muscles control and their coordination and vision derangement. The goal of this research is to consider to two problems: 1- Recognition of effective clinical symptoms on MS disease and 2- Considering levels of effectiveness of age, sex and education levels factors on MS disease and association between these factors according to verity of categories of this disease. Methods: Data mining science in medicine is worthy of attention with main application in diagnosis, therapy and prognosis, respectively high volume of collected datum. The data that were used in this article are about patients of Chaharmahal and Bakhtiari Province and collected by cure assistance. In this paper classification and association methods in software engineering field are used. Classification is a general process related to categorization, the process in which ideas and objects are recognized, differentiated, and understood. Association rules are created by analyzing data for frequent if/then patterns and using the criteria support and confidence to identify the most important relationships. Results: In consideration of first problem in this paper, concluded vision-clinical symptoms are the most effective symptoms and in consideration of second problem, concluded that from 584 records, women affected four times more than men. In other word 70% of MS patients with high graduate are in relapsing-remitting category and 62.5% of MS patients are 20-40 years old. Conclusion: Some of symptoms are quite temporary and transitory and are ignored by people. Awareness of clinical-symptoms prevalence manner can be warning for people before starting
Post-Mergers and Acquisitions: The Motives, Success Factors and Key Success Indicators

Directory of Open Access Journals (Sweden)

Hatem El Zuhairy

2015-07-01

Full Text Available There is a wide body of evidence showing a significant increase in the adoption of mergers and acquisitions (M&A worldwide. Moreover, research confirms that the integration and implementation stage (post-M&A has a major impact on the success or failure of a merger or acquisition. Therefore it has become increasingly important to explore the post-M&A phase further in order to support the management teams of organizations pursuing a merger or acquisition in meeting all their desired objectives. This paper proposes a framework to help in the successful execution of M&A. The framework contains three main elements: the motives, success factors and key success indicators (KSI. A qualitative research approach using the multiple case study methodology was conducted to test the framework. Ten case studies were selected from the industrial sector in Egypt and used to validate the research. The final version of the M&A framework was provided after applying the research results. Considering the practical implications of the M&A framework, a tool was proposed for its application in light of the balanced scorecard (BSC methodology. The proposed M&A scorecard tool should be used in the strategic planning and execution of M&A. Both the proposed M&A framework and the M&A scorecard tool should be used to guide the implementation of M&A in order to increase the success rate enjoyed by organizations.
Data mining

CERN Document Server

Gorunescu, Florin

2011-01-01

The knowledge discovery process is as old as Homo sapiens. Until some time ago, this process was solely based on the 'natural personal' computer provided by Mother Nature. Fortunately, in recent decades the problem has begun to be solved based on the development of the Data mining technology, aided by the huge computational power of the 'artificial' computers. Digging intelligently in different large databases, data mining aims to extract implicit, previously unknown and potentially useful information from data, since 'knowledge is power'. The goal of this book is to provide, in a friendly way
Data Stream Mining

Science.gov (United States)

Gaber, Mohamed Medhat; Zaslavsky, Arkady; Krishnaswamy, Shonali

Data mining is concerned with the process of computationally extracting hidden knowledge structures represented in models and patterns from large data repositories. It is an interdisciplinary field of study that has its roots in databases, statistics, machine learning, and data visualization. Data mining has emerged as a direct outcome of the data explosion that resulted from the success in database and data warehousing technologies over the past two decades (Fayyad, 1997,Fayyad, 1998,Kantardzic, 2003).
Data mining methods

CERN Document Server

Chattamvelli, Rajan

2015-01-01

DATA MINING METHODS, Second Edition discusses both theoretical foundation and practical applications of datamining in a web field including banking, e-commerce, medicine, engineering and management. This book starts byintroducing data and information, basic data type, data category and applications of data mining. The second chapterbriefly reviews data visualization technology and importance in data mining. Fundamentals of probability and statisticsare discussed in chapter 3, and novel algorithm for sample covariants are derived. The next two chapters give an indepthand useful discussion of data warehousing and OLAP. Decision trees are clearly explained and a new tabularmethod for decision tree building is discussed. The chapter on association rules discusses popular algorithms andcompares various algorithms in summary table form. An interesting application of genetic algorithm is introduced inthe next chapter. Foundations of neural networks are built from scratch and the back propagation algorithm is derived...
Data Acquisition System

International Nuclear Information System (INIS)

Cirstea, C.D.; Buda, S.I.; Constantin, F.

2005-01-01

This paper deals with a multi parametric acquisition system developed for a four input Analog to Digital Converter working in CAMAC Standard. The acquisition software is built in MS Visual C++ on a standard PC with a USB interface. It has a visual interface which permits Start/Stop of the acquisition, setting the type of acquisition (True/Live time), the time and various menus for primary data acquisition. The spectrum is dynamically visualized with a moving cursor indicating the content and position. The microcontroller PIC16C765 is used for data transfer from ADC to PC; The microcontroller and the software create an embedded system which emulates the CAMAC protocol programming the 4 input ADC for operating modes ('zero suppression', 'addressed' and 'sequential') and handling the data transfers from ADC to its internal memory. From its memory the data is transferred into the PC by the USB interface. The work is in progress. (authors)
Data acquisition system

International Nuclear Information System (INIS)

Cirstea, D.C.; Buda, S.I.; Constantin, F.

2005-01-01

The topic of this paper deals with a multi parametric acquisition system developed around a four input Analog to Digital Converter working in CAMAC Standard. The acquisition software is built in MS Visual C++ on a standard PC with a USB interface. It has a visual interface which permits Start/Stop of the acquisition, setting the type of acquisition (True/Live time), the time and various menus for primary data acquisition. The spectrum is dynamically visualized with a moving cursor indicating the content and position. The microcontroller PIC16C765 is used for data transfer from ADC to PC; The microcontroller and the software create an embedded system which emulates the CAMAC protocol programming, the 4 input ADC for operating modes ('zero suppression', 'addressed' and 'sequential') and handling the data transfers from ADC to its internal memory. From its memory the data is transferred into the PC by the USB interface. The work is in progress. (authors)
DATA MINING TECHNIQUES FOR EDUCATIONAL DATA: A REVIEW

OpenAIRE

Pragati Sharma; Dr. Sanjiv Sharma

2018-01-01

Recently, data mining is gaining more popularity among researcher. Data mining provides various techniques and methods for analysing data produced by various applications of different domain. Similarly, Educational mining is providing a way for analyzing educational data set. Educational mining concerns with developing methods for discovering knowledge from data that come from educational field and it helps to extract the hidden patterns and to discover new knowledge from large educational da...
Post-Mergers and Acquisitions: The Motives, Success Factors and Key Success Indicators

OpenAIRE

Hatem El Zuhairy; Ahmed Taher; Ingy Shafei

2015-01-01

There is a wide body of evidence showing a significant increase in the adoption of mergers and acquisitions (M&A) worldwide. Moreover, research confirms that the integration and implementation stage (post-M&A) has a major impact on the success or failure of a merger or acquisition. Therefore it has become increasingly important to explore the post-M&A phase further in order to support the management teams of organizations pursuing a merger or acquisition in meeting all their desired objective...
Data Mining Aplications in Livestock

Directory of Open Access Journals (Sweden)

Feyza ALEV ÇETİN

2016-03-01

Full Text Available Data mining provides discovering the required and applicable knowledge from very large amounts of information collected in one centre. Data mining has been used in the information industry and society. Although many methods of data mining has been used, these techniques has been remarkable in animal husbandry in recent years. For the solution of complex problems in animal husbandry many methods were discussed and developed. Brief information on data mining techniques such as k-means approach, k-nearest neighbor approach, multivariate adaptive regression function (MARS, naive Bayesian classifiers (NBC, artificial neural networks (ANN, support vector machines (SVM, decision trees are given in the study. Some data mining methods are presented and examples of the application of data mining in the field of animal husbandry in the world are provided with this study.
Data mining: childhood injury control and beyond.

Science.gov (United States)

Tepas, Joseph J

2009-08-01

Data mining is defined as the automatic extraction of useful, often previously unknown information from large databases or data sets. It has become a major part of modern life and is extensively used in industry, banking, government, and health care delivery. The process requires a data collection system that integrates input from multiple sources containing critical elements that define outcomes of interest. Appropriately designed data mining processes identify and adjust for confounding variables. The statistical modeling used to manipulate accumulated data may involve any number of techniques. As predicted results are periodically analyzed against those observed, the model is consistently refined to optimize precision and accuracy. Whether applying integrated sources of clinical data to inferential probabilistic prediction of risk of ventilator-associated pneumonia or population surveillance for signs of bioterrorism, it is essential that modern health care providers have at least a rudimentary understanding of what the concept means, how it basically works, and what it means to current and future health care.
A system design of data acquisition and processing for side-scatter lidar

Science.gov (United States)

Zhang, ZhanYe; Xie, ChenBo; Wang, ZhenZhu; Kuang, ZhiQiang; Deng, Qian; Tao, ZongMing; Liu, Dong; Wang, Yingjian

2018-03-01

A system for collecting data of Side-Scatter lidar based on Charge Coupled Device (CCD),is designed and implemented. The system of data acquisition is based on Microsoft. Net structure and the language of C# is used to call dynamic link library (DLL) of CCD for realization of the real-time data acquisition and processing. The software stores data as txt file for post data acquisition and analysis. The system has ability to operate CCD device in all-day, automatic, continuous and high frequency data acquisition and processing conditions, which will catch 24-hour information of the atmospheric scatter's light intensity and retrieve the spatial and temporal properties of aerosol particles. The experimental result shows that the system is convenient to observe the aerosol optical characteristics near surface.

Big data mining: In-database Oracle data mining over hadoop

Science.gov (United States)

Kovacheva, Zlatinka; Naydenova, Ina; Kaloyanova, Kalinka; Markov, Krasimir

2017-07-01

Big data challenges different aspects of storing, processing and managing data, as well as analyzing and using data for business purposes. Applying Data Mining methods over Big Data is another challenge because of huge data volumes, variety of information, and the dynamic of the sources. Different applications are made in this area, but their successful usage depends on understanding many specific parameters. In this paper we present several opportunities for using Data Mining techniques provided by the analytical engine of RDBMS Oracle over data stored in Hadoop Distributed File System (HDFS). Some experimental results are given and they are discussed.
Granular-relational data mining how to mine relational data in the paradigm of granular computing ?

CERN Document Server

Hońko, Piotr

2017-01-01

This book provides two general granular computing approaches to mining relational data, the first of which uses abstract descriptions of relational objects to build their granular representation, while the second extends existing granular data mining solutions to a relational case. Both approaches make it possible to perform and improve popular data mining tasks such as classification, clustering, and association discovery. How can different relational data mining tasks best be unified? How can the construction process of relational patterns be simplified? How can richer knowledge from relational data be discovered? All these questions can be answered in the same way: by mining relational data in the paradigm of granular computing! This book will allow readers with previous experience in the field of relational data mining to discover the many benefits of its granular perspective. In turn, those readers familiar with the paradigm of granular computing will find valuable insights on its application to mining r...
Validation of new 3D post processing algorithm for improved maximum intensity projections of MR angiography acquisitions in the brain

Energy Technology Data Exchange (ETDEWEB)

Bosmans, H; Verbeeck, R; Vandermeulen, D; Suetens, P; Wilms, G; Maaly, M; Marchal, G; Baert, A L [Louvain Univ. (Belgium)

1995-12-01

The objective of this study was to validate a new post processing algorithm for improved maximum intensity projections (mip) of intracranial MR angiography acquisitions. The core of the post processing procedure is a new brain segmentation algorithm. Two seed areas, background and brain, are automatically detected. A 3D region grower then grows both regions towards each other and this preferentially towards white regions. In this way, the skin gets included into the final `background region` whereas cortical blood vessels and all brain tissues are included in the `brain region`. The latter region is then used for mip. The algorithm runs less than 30 minutes on a full dataset on a Unix workstation. Images from different acquisition strategies including multiple overlapping thin slab acquisition, magnetization transfer (MT) MRA, Gd-DTPA enhanced MRA, normal and high resolution acquisitions and acquisitions from mid field and high field systems were filtered. A series of contrast enhanced MRA acquisitions obtained with identical parameters was filtered to study the robustness of the filter parameters. In all cases, only a minimal manual interaction was necessary to segment the brain. The quality of the mip was significantly improved, especially in post Gd-DTPA acquisitions or using MT, due to the absence of high intensity signals of skin, sinuses and eyes that otherwise superimpose on the angiograms. It is concluded that the filter is a robust technique to improve the quality of MR angiograms.
Validation of new 3D post processing algorithm for improved maximum intensity projections of MR angiography acquisitions in the brain

International Nuclear Information System (INIS)

Bosmans, H.; Verbeeck, R.; Vandermeulen, D.; Suetens, P.; Wilms, G.; Maaly, M.; Marchal, G.; Baert, A.L.

1995-01-01

The objective of this study was to validate a new post processing algorithm for improved maximum intensity projections (mip) of intracranial MR angiography acquisitions. The core of the post processing procedure is a new brain segmentation algorithm. Two seed areas, background and brain, are automatically detected. A 3D region grower then grows both regions towards each other and this preferentially towards white regions. In this way, the skin gets included into the final 'background region' whereas cortical blood vessels and all brain tissues are included in the 'brain region'. The latter region is then used for mip. The algorithm runs less than 30 minutes on a full dataset on a Unix workstation. Images from different acquisition strategies including multiple overlapping thin slab acquisition, magnetization transfer (MT) MRA, Gd-DTPA enhanced MRA, normal and high resolution acquisitions and acquisitions from mid field and high field systems were filtered. A series of contrast enhanced MRA acquisitions obtained with identical parameters was filtered to study the robustness of the filter parameters. In all cases, only a minimal manual interaction was necessary to segment the brain. The quality of the mip was significantly improved, especially in post Gd-DTPA acquisitions or using MT, due to the absence of high intensity signals of skin, sinuses and eyes that otherwise superimpose on the angiograms. It is concluded that the filter is a robust technique to improve the quality of MR angiograms
Mining Views : database views for data mining

NARCIS (Netherlands)

Blockeel, H.; Calders, T.; Fromont, É.; Goethals, B.; Prado, A.; Nijssen, S.; De Raedt, L.

2007-01-01

We propose a relational database model towards the integration of data mining into relational database systems, based on the so called virtual mining views. We show that several types of patterns and models over the data, such as itemsets, association rules, decision trees and clusterings, can be
Mining Views : database views for data mining

NARCIS (Netherlands)

Blockeel, H.; Calders, T.; Fromont, É.; Goethals, B.; Prado, A.

2008-01-01

We present a system towards the integration of data mining into relational databases. To this end, a relational database model is proposed, based on the so called virtual mining views. We show that several types of patterns and models over the data, such as itemsets, association rules and decision
Data mining in agriculture

CERN Document Server

Mucherino, Antonio; Pardalos, Panos M

2009-01-01

Data Mining in Agriculture represents a comprehensive effort to provide graduate students and researchers with an analytical text on data mining techniques applied to agriculture and environmental related fields. This book presents both theoretical and practical insights with a focus on presenting the context of each data mining technique rather intuitively with ample concrete examples represented graphically and with algorithms written in MATLAB®. Examples and exercises with solutions are provided at the end of each chapter to facilitate the comprehension of the material. For each data mining technique described in the book variants and improvements of the basic algorithm are also given. Also by P.J. Papajorgji and P.M. Pardalos: Advances in Modeling Agricultural Systems, 'Springer Optimization and its Applications' vol. 25, ©2009.
Data Mining Tools in Science Education

OpenAIRE

Premysl Zaskodny

2012-01-01

The main principle of paper is Data Mining in Science Education (DMSE) as Problem Solving. The main goal of paper is consisting in Delimitation of Complex Data Mining Tool and Partial Data Mining Tool of DMSE. The procedure of paper is consisting of Data Preprocessing in Science Education, Data Processing in Science Education, Description of Curricular Process as Complex Data Mining Tool (CP-DMSE), Description of Analytical Synthetic Modeling as Partial Data Mining Tool (ASM-DMSE) and finally...
A methodology for the assessment of rehabilitation success of post mining landscapes-sediment and radionuclide transport at the former Nabarlek uranium mine, Northern Territory, Australia

International Nuclear Information System (INIS)

Hancock, G.R.; Grabham, M.K.; Martin, P; Evans, K.G.; Bollhoefer, A.

2006-01-01

Protection of the environment post-mining is an important issue, especially where runoff and erosion can lead to undesirable material leaving post-mining landscapes and contaminating surrounding land and watercourses. Methods for assessment of the environmental impact and long-term behaviour of post-mining landforms based on scientific methodology are needed especially where field data are absent or poor. An appraisal of the former Nabarlek uranium mine was conducted to assess the site from a soil erosion perspective as part of an independent evaluation of overall rehabilitation success. Determination of the gross erosion occurring, sediment discharge to Cooper Creek and the resultant sediment associated radionuclide load in Cooper Creek were the primary objectives of the study. These objectives were achieved through the application of several models using parameter values collected from the site. The study found that the area containing the mill tailings repository is extremely stable and meets the guidelines established for long-term storage of uranium mill tailings. Most other areas on the site are stable; however there are some areas with a high sediment loss. Sediment concentration in Cooper Creek, which drains the site, was found to be within the Australian water quality guidelines for fresh water, however sediment concentrations in tributaries were found to exceed recommended levels. Radionuclide determinations on soil samples showed that the highest specific activities (Bq kg -1 ) were present on a small (0.44 ha) area with a relatively high erosion rate. This small area contributed the majority of the estimated flux to Cooper Creek of uranium-series radionuclides sorbed or structurally incorporated to eroded soil particles sourced from the mine site. This study provides a methodology for assessment of the erosional stability of such a landscape and consequent impact on water quality, using extensive field data and readily available and well known models
Real world data mining applications

CERN Document Server

Abou-Nasr, Mahmoud; Stahlbock, Robert; Weiss, Gary M

2014-01-01

Data mining applications range from commercial to social domains, with novel applications appearing swiftly; for example, within the context of social networks. The expanding application sphere and social reach of advanced data mining raise pertinent issues of privacy and security. Present-day data mining is a progressive multidisciplinary endeavor. This inter- and multidisciplinary approach is well reflected within the field of information systems. The information systems research addresses software and hardware requirements for supporting computationally and data-intensive applications. Furthermore, it encompasses analyzing system and data aspects, and all manual or automated activities. In that respect, research at the interface of information systems and data mining has significant potential to produce actionable knowledge vital for corporate decision-making. The aim of the proposed volume is to provide a balanced treatment of the latest advances and developments in data mining; in particular, exploring s...
Data mining in pharma sector: benefits.

Science.gov (United States)

Ranjan, Jayanthi

2009-01-01

The amount of data getting generated in any sector at present is enormous. The information flow in the pharma industry is huge. Pharma firms are progressing into increased technology-enabled products and services. Data mining, which is knowledge discovery from large sets of data, helps pharma firms to discover patterns in improving the quality of drug discovery and delivery methods. The paper aims to present how data mining is useful in the pharma industry, how its techniques can yield good results in pharma sector, and to show how data mining can really enhance in making decisions using pharmaceutical data. This conceptual paper is written based on secondary study, research and observations from magazines, reports and notes. The author has listed the types of patterns that can be discovered using data mining in pharma data. The paper shows how data mining is useful in the pharma industry and how its techniques can yield good results in pharma sector. Although much work can be produced for discovering knowledge in pharma data using data mining, the paper is limited to conceptualizing the ideas and view points at this stage; future work may include applying data mining techniques to pharma data based on primary research using the available, famous significant data mining tools. Research papers and conceptual papers related to data mining in Pharma industry are rare; this is the motivation for the paper.
Data mining goes multidimensional.

Science.gov (United States)

Hettler, M

1997-03-01

The success of a healthcare organization depends on its ability to acquire, store, analyze and compare data across many parts of the enterprise, by many individuals. While relational databases have been around since the 1970s, their two-dimensional structure has limited--or made impossible--the kind of cross-dimensional trend analysis so necessary to healthcare today. Enter online analytical processing (OLAP), in which servers store data in multiple dimensions, opening a world of opportunity for data-mining across the enterprise. In this issue of HEALTHCARE INFORMATICS, we feature our first report from the National Software Testing Laboratories (NSTL) about technologies that will change the way healthcare does business. A division of The McGraw-Hill Companies, NSTL is an independent software and hardware testing lab offering services that include compatibility testing, bug testing, comparison testing, documentation evaluation and usability.
Four-channel high speed synchronized acquisition multiple trigger storage measurement system

International Nuclear Information System (INIS)

Guo Jian; Wang Wenlian; Zhang Zhijie

2010-01-01

A new storage measurement system based on the CPLD, MCU and FLASH (large-capacity flash memory) is proposed. The large capacity storage characteristic of the FLASH MEMORY is used to realize multi channel synchronized acquisition and the function of multiple records and read once. The function of multi channel synchronization, high speed data acquisition, the triggering several times, and the adjustability of working parameters expands the application of storage measurement system. The storage measurement system can be used in a variety of pressure and temperature test in explosion field. (authors)
Mining High-Dimensional Data

Science.gov (United States)

Wang, Wei; Yang, Jiong

With the rapid growth of computational biology and e-commerce applications, high-dimensional data becomes very common. Thus, mining high-dimensional data is an urgent problem of great practical importance. However, there are some unique challenges for mining data of high dimensions, including (1) the curse of dimensionality and more crucial (2) the meaningfulness of the similarity measure in the high dimension space. In this chapter, we present several state-of-art techniques for analyzing high-dimensional data, e.g., frequent pattern mining, clustering, and classification. We will discuss how these methods deal with the challenges of high dimensionality.
Data acquisition, handling, and display for the heater experiments at Stripa

Energy Technology Data Exchange (ETDEWEB)

McEvoy, M.B.

1979-02-01

In June 1978, a joint Swedish/American research team began acquiring data from the Stripa mine in Sweden, 340 m below the surface. Electrical heaters are used to assess the suitability of granite rock as a repository for radioactive waste material. Extensive instrumentation also measures temperature, stress, and displacement effects caused by these heaters. This report describes the data acquisition system, its design considerations, capabilities, and operational use. The techniques employed to detect and analyze any anomalous experimental results are also described. Environmental considerations are described in an appendix.
Information management system breadboard data acquisition and control system.

Science.gov (United States)

Mallary, W. E.

1972-01-01

Description of a breadboard configuration of an advanced information management system based on requirements for high data rates and local and centralized computation for subsystems and experiments to be housed on a space station. The system is to contain a 10-megabit-per-second digital data bus, remote terminals with preprocessor capabilities, and a central multiprocessor. A concept definition is presented for the data acquisition and control system breadboard, and a detailed account is given of the operation of the bus control unit, the bus itself, and the remote acquisition and control unit. The data bus control unit is capable of operating under control of both its own test panel and the test processor. In either mode it is capable of both single- and multiple-message operation in that it can accept a block of data requests or update commands for transmission to the remote acquisition and control unit, which in turn is capable of three levels of data-handling complexity.
Prediction of black box warning by mining patterns of Convergent Focus Shift in clinical trial study populations using linked public data.

Science.gov (United States)

Ma, Handong; Weng, Chunhua

2016-04-01

-term BBW acquisition events without compromising prediction accuracy. This study contributes a method for post-marketing pharmacovigilance using Convergent Focus Shift (CFS) patterns in clinical trial study populations mined from linked public data resources. These signals are otherwise unavailable from individual data resources. We demonstrated the added value of linked public data and the feasibility of integrating ClinicalTrials.gov summaries and drug safety labels for post-marketing surveillance. Future research is needed to ensure better accessibility and linkage of heterogeneous drug safety data for efficient pharmacovigilance. Copyright © 2016 Elsevier Inc. All rights reserved.
Data mining theories, algorithms, and examples

CERN Document Server

Ye, Nong

2013-01-01

AN OVERVIEW OF DATA MINING METHODOLOGIESIntroduction to data mining methodologiesMETHODOLOGIES FOR MINING CLASSIFICATION AND PREDICTION PATTERNSRegression modelsBayes classifiersDecision treesMulti-layer feedforward artificial neural networksSupport vector machinesSupervised clusteringMETHODOLOGIES FOR MINING CLUSTERING AND ASSOCIATION PATTERNSHierarchical clusteringPartitional clusteringSelf-organized mapProbability distribution estimationAssociation rulesBayesian networksMETHODOLOGIES FOR MINING DATA REDUCTION PATTERNSPrincipal components analysisMulti-dimensional scalingLatent variable anal
Data acquisition system for steady state experiments at multi-sites

International Nuclear Information System (INIS)

Nakanishi, H.; Emoto, M.; Nagayama, Y.

2010-11-01

A high-performance data acquisition system (LABCOM system) has been developed for steady state fusion experiments in Large Helical Device (LHD). The most important characteristics of this system are the 110 MB/s high-speed real-time data acquisition capability and also the scalability on its performance by using unlimited number of data acquisition (DAQ) units. It can also acquire experimental data from multiple remote sites through the 1 Gbps fusion-dedicated virtual private network (SNET) in Japan. In LHD steady-state experiments, the DAQ cluster has established the world record of acquired data amount of 90 GB/shot which almost reaches the ITER data estimate. Since all the DAQ, storage, and data clients of LABCOM system are distributed on the local area network (LAN), remote experimental data can be also acquired simply by extending the LAN to the wide-area SNET. The speed lowering problem in long-distance TCP/IP data transfer has been improved by using an optimized congestion control and packet pacing method. Japan-France and Japan-US network bandwidth tests have revealed that this method actually utilize 90% of ideal throughput in both cases. Toward the fusion goal, a common data access platform is indispensable so that detailed physics data can be easily compared between multiple large and small experiments. The demonstrated bilateral collaboration scheme will be analogous to that of ITER and the supporting machines. (author)
Data-Mining Research in Education

OpenAIRE

Cheng, Jiechao

2017-01-01

As an interdisciplinary discipline, data mining (DM) is popular in education area especially when examining students' learning performances. It focuses on analyzing educational related data to develop models for improving learners' learning experiences and enhancing institutional effectiveness. Therefore, DM does help education institutions provide high-quality education for its learners. Applying data mining in education also known as educational data mining (EDM), which enables to better un...

Data Mining Web Services for Science Data Repositories

Science.gov (United States)

Graves, S.; Ramachandran, R.; Keiser, K.; Maskey, M.; Lynnes, C.; Pham, L.

2006-12-01

The maturation of web services standards and technologies sets the stage for a distributed "Service-Oriented Architecture" (SOA) for NASA's next generation science data processing. This architecture will allow members of the scientific community to create and combine persistent distributed data processing services and make them available to other users over the Internet. NASA has initiated a project to create a suite of specialized data mining web services designed specifically for science data. The project leverages the Algorithm Development and Mining (ADaM) toolkit as its basis. The ADaM toolkit is a robust, mature and freely available science data mining toolkit that is being used by several research organizations and educational institutions worldwide. These mining services will give the scientific community a powerful and versatile data mining capability that can be used to create higher order products such as thematic maps from current and future NASA satellite data records with methods that are not currently available. The package of mining and related services are being developed using Web Services standards so that community-based measurement processing systems can access and interoperate with them. These standards-based services allow users different options for utilizing them, from direct remote invocation by a client application to deployment of a Business Process Execution Language (BPEL) solutions package where a complex data mining workflow is exposed to others as a single service. The ability to deploy and operate these services at a data archive allows the data mining algorithms to be run where the data are stored, a more efficient scenario than moving large amounts of data over the network. This will be demonstrated in a scenario in which a user uses a remote Web-Service-enabled clustering algorithm to create cloud masks from satellite imagery at the Goddard Earth Sciences Data and Information Services Center (GES DISC).
Acquisition of multiple prior distributions in tactile temporal order judgment

Directory of Open Access Journals (Sweden)

Yasuhito eNagai

2012-08-01

Full Text Available The Bayesian estimation theory proposes that the brain acquires the prior distribution of a task and integrates it with sensory signals to minimize the effect of sensory noise. Psychophysical studies have demonstrated that our brain actually implements Bayesian estimation in a variety of sensory-motor tasks. However, these studies only imposed one prior distribution on participants within a task period. In this study, we investigated the conditions that enable the acquisition of multiple prior distributions in temporal order judgment (TOJ of two tactile stimuli across the hands. In Experiment 1, stimulation intervals were randomly selected from one of two prior distributions (biased to right hand earlier and biased to left hand earlier in association with color cues (green and red, respectively. Although the acquisition of the two priors was not enabled by the color cues alone, it was significant when participants shifted their gaze (above or below in response to the color cues. However, the acquisition of multiple priors was not significant when participants moved their mouths (opened or closed. In Experiment 2, the spatial cues (above and below were used to identify which eye position or retinal cue position was crucial for the eye-movement-dependent acquisition of multiple priors in Experiment 1. The acquisition of the two priors was significant when participants moved their gaze to the cues (i.e., the cue positions on the retina were constant across the priors, as well as when participants did not shift their gazes (i.e., the cue positions on the retina changed according to the priors. Thus, both eye and retinal cue positions were effective in acquiring multiple priors. Based on previous neurophysiological reports, we discuss possible neural correlates that contribute to the acquisition of multiple priors.
Mining and Integration of Environmental Data

Science.gov (United States)

Tran, V.; Hluchy, L.; Habala, O.; Ciglan, M.

2009-04-01

The project ADMIRE (Advanced Data Mining and Integration Research for Europe) is a 7th FP EU ICT project aims to deliver a consistent and easy-to-use technology for extracting information and knowledge. The project is motivated by the difficulty of extracting meaningful information by data mining combinations of data from multiple heterogeneous and distributed resources. It will also provide an abstract view of data mining and integration, which will give users and developers the power to cope with complexity and heterogeneity of services, data and processes. The data sets describing phenomena from domains like business, society, and environment often contain spatial and temporal dimensions. Integration of spatio-temporal data from different sources is a challenging task due to those dimensions. Different spatio-temporal data sets contain data at different resolutions (e.g. size of the spatial grid) and frequencies. This heterogeneity is the principal challenge of geo-spatial and temporal data sets integration - the integrated data set should hold homogeneous data of the same resolution and frequency. Thus, to integrate heterogeneous spatio-temporal data from distinct source, transformation of one or more data sets is necessary. Following transformation operation are required: • transformation to common spatial and temporal representation - (e.g. transformation to common coordinate system), • spatial and/or temporal aggregation - data from detailed data source are aggregated to match the resolution of other resources involved in the integration process, • spatial and/or temporal record decomposition - records from source with lower resolution data are decomposed to match the granularity of the other data source. This operation decreases data quality (e.g. transformation of data from 50km grid to 10 km grid) - data from lower resolution data set in the integrated schema are imprecise, but it allows us to preserve higher resolution data. We can decompose the
Mastering SQL Server 2014 data mining

CERN Document Server

Bassan, Amarpreet Singh

2014-01-01

If you are a developer who is working on data mining for large companies and would like to enhance your knowledge of SQL Server Data Mining Suite, this book is for you. Whether you are brand new to data mining or are a seasoned expert, you will be able to master the skills needed to build a data mining solution.
Combining Multiple Data Acquisition Systems to Study Corticospinal Output and Multi-segment Biomechanics.

Science.gov (United States)

Asmussen, Michael J; Bailey, Aaron Z; Keir, Peter J; Potvin, Jim; Bergel, Tim; Nelson, Aimee J

2016-01-09

Transcranial magnetic stimulation techniques allow for an in-depth investigation into the neural mechanisms that underpin human behavior. To date, the use of TMS to study human movement, has been limited by the challenges related to precisely timing the delivery of TMS to features of the unfolding movement and, also, by accurately characterizing kinematics and kinetics. To overcome these technical challenges, TMS delivery and acquisition systems should be integrated with an online motion tracking system. The present manuscript details technical innovations that integrate multiple acquisition systems to facilitate and advance the use of TMS to study human movement. Using commercially available software and hardware systems, a step-by-step approach to both the hardware assembly and the software scripts necessary to perform TMS studies triggered by specific features of a movement is provided. The approach is focused on the study of upper limb, planar, multi-joint reaching movements. However, the same integrative system is amenable to a multitude of sophisticated studies of human motor control.
Post-mining safety implementations and environmental aspects of abandoned mine sites in Limousin. 2006 status (and perspectives 2007); Mises en securite en apres-mine et aspects environnementaux des anciens sites miniers en Limousin. Bilan 2006 (et perspectives 2007)

Energy Technology Data Exchange (ETDEWEB)

NONE

2007-07-01

This document summarizes the actions carried out in 2006 at some French abandoned mine sites: 1 - safety implementations and risks abatement in the framework of post-mining actions: coal mines of Ahun (23) and Argentat (19), antimony mines of Biard (87); 2 - remedial actions at the tin/tungsten mine of Puy-les-Vignes (87) and at the gold mine of Chatelet (23); 3 - 2007 post-mining perspectives; 4 - environmental aspects of abandoned mine sites: gold mines of Chatelet (23), Cheni and Bourneix (87), uranium mines of Haute-Vienne (expertise, control of effluents, financial warranties about tailings storage sites maintenance). (J.S.)
Effects of Epilepsy on Language Functions: Scoping Review and Data Mining Findings.

Science.gov (United States)

Dutta, Manaswita; Murray, Laura; Miller, Wendy; Groves, Doyle

2018-03-01

This study involved a scoping review to identify possible gaps in the empirical description of language functioning in epilepsy in adults. With access to social network data, data mining was used to determine if individuals with epilepsy are expressing language-related concerns. For the scoping review, scientific databases were explored to identify pertinent articles. Findings regarding the nature of epilepsy etiologies, patient characteristics, tested language modalities, and language measures were compiled. Data mining focused on social network databases to obtain a set of relevant language-related posts. The search yielded 66 articles. Epilepsy etiologies except temporal lobe epilepsy and older adults were underrepresented. Most studies utilized aphasia tests and primarily assessed single-word productions; few studies included healthy control groups. Data mining revealed several posts regarding epilepsy-related language problems, including word retrieval, reading, writing, verbal memory difficulties, and negative effects of epilepsy treatment on language. Our findings underscore the need for future specification of the integrity of language in epilepsy, particularly with respect to discourse and high-level language abilities. Increased awareness of epilepsy-related language issues and understanding the patients' perspectives about their language concerns will allow researchers and speech-language pathologists to utilize appropriate assessments and improve quality of care.
TargetMine, an integrated data warehouse for candidate gene prioritisation and target discovery.

Directory of Open Access Journals (Sweden)

Yi-An Chen

Full Text Available Prioritising candidate genes for further experimental characterisation is a non-trivial challenge in drug discovery and biomedical research in general. An integrated approach that combines results from multiple data types is best suited for optimal target selection. We developed TargetMine, a data warehouse for efficient target prioritisation. TargetMine utilises the InterMine framework, with new data models such as protein-DNA interactions integrated in a novel way. It enables complicated searches that are difficult to perform with existing tools and it also offers integration of custom annotations and in-house experimental data. We proposed an objective protocol for target prioritisation using TargetMine and set up a benchmarking procedure to evaluate its performance. The results show that the protocol can identify known disease-associated genes with high precision and coverage. A demonstration version of TargetMine is available at http://targetmine.nibio.go.jp/.
Applied data mining

CERN Document Server

Xu, Guandong

2013-01-01

Data mining has witnessed substantial advances in recent decades. New research questions and practical challenges have arisen from emerging areas and applications within the various fields closely related to human daily life, e.g. social media and social networking. This book aims to bridge the gap between traditional data mining and the latest advances in newly emerging information services. It explores the extension of well-studied algorithms and approaches into these new research arenas.
Neutral particle beam distributed data acquisition system

International Nuclear Information System (INIS)

Daly, R.T.; Kraimer, M.R.; Novick, A.H.

1987-01-01

A distributed data acquisition system has been designed to support experiments at the Argonne Neutral Particle Beam Accelerator. The system uses a host VAXstation II/GPX computer acting as an experimenter's station linked via Ethernet with multiple MicroVAX IIs and rtVAXs dedicated to acquiring data and controlling hardware at remote sites. This paper describes the hardware design of the system, the applications support software on the host and target computers, and the real-time performance
An Enhanced Text-Mining Framework for Extracting Disaster Relevant Data through Social Media and Remote Sensing Data Fusion

Science.gov (United States)

Scheele, C. J.; Huang, Q.

2016-12-01

In the past decade, the rise in social media has led to the development of a vast number of social media services and applications. Disaster management represents one of such applications leveraging massive data generated for event detection, response, and recovery. In order to find disaster relevant social media data, current approaches utilize natural language processing (NLP) methods based on keywords, or machine learning algorithms relying on text only. However, these approaches cannot be perfectly accurate due to the variability and uncertainty in language used on social media. To improve current methods, the enhanced text-mining framework is proposed to incorporate location information from social media and authoritative remote sensing datasets for detecting disaster relevant social media posts, which are determined by assessing the textual content using common text mining methods and how the post relates spatiotemporally to the disaster event. To assess the framework, geo-tagged Tweets were collected for three different spatial and temporal disaster events: hurricane, flood, and tornado. Remote sensing data and products for each event were then collected using RealEarthTM. Both Naive Bayes and Logistic Regression classifiers were used to compare the accuracy within the enhanced text-mining framework. Finally, the accuracies from the enhanced text-mining framework were compared to the current text-only methods for each of the case study disaster events. The results from this study address the need for more authoritative data when using social media in disaster management applications.
Data acquisition in modeling using neural networks and decision trees

Directory of Open Access Journals (Sweden)

R. Sika

2011-04-01

Full Text Available The paper presents a comparison of selected models from area of artificial neural networks and decision trees in relation with actualconditions of foundry processes. The work contains short descriptions of used algorithms, their destination and method of data preparation,which is a domain of work of Data Mining systems. First part concerns data acquisition realized in selected iron foundry, indicating problems to solve in aspect of casting process modeling. Second part is a comparison of selected algorithms: a decision tree and artificial neural network, that is CART (Classification And Regression Trees and BP (Backpropagation in MLP (Multilayer Perceptron networks algorithms.Aim of the paper is to show an aspect of selecting data for modeling, cleaning it and reducing, for example due to too strong correlationbetween some of recorded process parameters. Also, it has been shown what results can be obtained using two different approaches:first when modeling using available commercial software, for example Statistica, second when modeling step by step using Excel spreadsheetbasing on the same algorithm, like BP-MLP. Discrepancy of results obtained from these two approaches originates from a priorimade assumptions. Mentioned earlier Statistica universal software package, when used without awareness of relations of technologicalparameters, i.e. without user having experience in foundry and without scheduling ranks of particular parameters basing on acquisition, can not give credible basis to predict the quality of the castings. Also, a decisive influence of data acquisition method has been clearly indicated, the acquisition should be conducted according to repetitive measurement and control procedures. This paper is based on about 250 records of actual data, for one assortment for 6 month period, where only 12 data sets were complete (including two that were used for validation of neural network and useful for creating a model. It is definitely too
Implementation of High Speed Distributed Data Acquisition System

Science.gov (United States)

Raju, Anju P.; Sekhar, Ambika

2012-09-01

This paper introduces a high speed distributed data acquisition system based on a field programmable gate array (FPGA). The aim is to develop a "distributed" data acquisition interface. The development of instruments such as personal computers and engineering workstations based on "standard" platforms is the motivation behind this effort. Using standard platforms as the controlling unit allows independence in hardware from a particular vendor and hardware platform. The distributed approach also has advantages from a functional point of view: acquisition resources become available to multiple instruments; the acquisition front-end can be physically remote from the rest of the instrument. High speed data acquisition system transmits data faster to a remote computer system through Ethernet interface. The data is acquired through 16 analog input channels. The input data commands are multiplexed and digitized and then the data is stored in 1K buffer for each input channel. The main control unit in this design is the 16 bit processor implemented in the FPGA. This 16 bit processor is used to set up and initialize the data source and the Ethernet controller, as well as control the flow of data from the memory element to the NIC. Using this processor we can initialize and control the different configuration registers in the Ethernet controller in a easy manner. Then these data packets are sending to the remote PC through the Ethernet interface. The main advantages of the using FPGA as standard platform are its flexibility, low power consumption, short design duration, fast time to market, programmability and high density. The main advantages of using Ethernet controller AX88796 over others are its non PCI interface, the presence of embedded SRAM where transmit and reception buffers are located and high-performance SRAM-like interface. The paper introduces the implementation of the distributed data acquisition using FPGA by VHDL. The main advantages of this system are high
Developing and Implementing the Data Mining Algorithms in RAVEN

International Nuclear Information System (INIS)

Sen, Ramazan Sonat; Maljovec, Daniel Patrick; Alfonsi, Andrea; Rabiti, Cristian

2015-01-01

The RAVEN code is becoming a comprehensive tool to perform probabilistic risk assessment, uncertainty quantification, and verification and validation. The RAVEN code is being developed to support many programs and to provide a set of methodologies and algorithms for advanced analysis. Scientific computer codes can generate enormous amounts of data. To post-process and analyze such data might, in some cases, take longer than the initial software runtime. Data mining algorithms/methods help in recognizing and understanding patterns in the data, and thus discover knowledge in databases. The methodologies used in the dynamic probabilistic risk assessment or in uncertainty and error quantification analysis couple system/physics codes with simulation controller codes, such as RAVEN. RAVEN introduces both deterministic and stochastic elements into the simulation while the system/physics code model the dynamics deterministically. A typical analysis is performed by sampling values of a set of parameter values. A major challenge in using dynamic probabilistic risk assessment or uncertainty and error quantification analysis for a complex system is to analyze the large number of scenarios generated. Data mining techniques are typically used to better organize and understand data, i.e. recognizing patterns in the data. This report focuses on development and implementation of Application Programming Interfaces (APIs) for different data mining algorithms, and the application of these algorithms to different databases.
Developing and Implementing the Data Mining Algorithms in RAVEN

Energy Technology Data Exchange (ETDEWEB)

Sen, Ramazan Sonat [Idaho National Lab. (INL), Idaho Falls, ID (United States); Maljovec, Daniel Patrick [Idaho National Lab. (INL), Idaho Falls, ID (United States); Alfonsi, Andrea [Idaho National Lab. (INL), Idaho Falls, ID (United States); Rabiti, Cristian [Idaho National Lab. (INL), Idaho Falls, ID (United States)

2015-09-01

The RAVEN code is becoming a comprehensive tool to perform probabilistic risk assessment, uncertainty quantification, and verification and validation. The RAVEN code is being developed to support many programs and to provide a set of methodologies and algorithms for advanced analysis. Scientific computer codes can generate enormous amounts of data. To post-process and analyze such data might, in some cases, take longer than the initial software runtime. Data mining algorithms/methods help in recognizing and understanding patterns in the data, and thus discover knowledge in databases. The methodologies used in the dynamic probabilistic risk assessment or in uncertainty and error quantification analysis couple system/physics codes with simulation controller codes, such as RAVEN. RAVEN introduces both deterministic and stochastic elements into the simulation while the system/physics code model the dynamics deterministically. A typical analysis is performed by sampling values of a set of parameter values. A major challenge in using dynamic probabilistic risk assessment or uncertainty and error quantification analysis for a complex system is to analyze the large number of scenarios generated. Data mining techniques are typically used to better organize and understand data, i.e. recognizing patterns in the data. This report focuses on development and implementation of Application Programming Interfaces (APIs) for different data mining algorithms, and the application of these algorithms to different databases.
The Hazards of Data Mining in Healthcare.

Science.gov (United States)

Househ, Mowafa; Aldosari, Bakheet

2017-01-01

From the mid-1990s, data mining methods have been used to explore and find patterns and relationships in healthcare data. During the 1990s and early 2000's, data mining was a topic of great interest to healthcare researchers, as data mining showed some promise in the use of its predictive techniques to help model the healthcare system and improve the delivery of healthcare services. However, it was soon discovered that mining healthcare data had many challenges relating to the veracity of healthcare data and limitations around predictive modelling leading to failures of data mining projects. As the Big Data movement has gained momentum over the past few years, there has been a reemergence of interest in the use of data mining techniques and methods to analyze healthcare generated Big Data. Much has been written on the positive impacts of data mining on healthcare practice relating to issues of best practice, fraud detection, chronic disease management, and general healthcare decision making. Little has been written about the limitations and challenges of data mining use in healthcare. In this review paper, we explore some of the limitations and challenges in the use of data mining techniques in healthcare. Our results show that the limitations of data mining in healthcare include reliability of medical data, data sharing between healthcare organizations, inappropriate modelling leading to inaccurate predictions. We conclude that there are many pitfalls in the use of data mining in healthcare and more work is needed to show evidence of its utility in facilitating healthcare decision-making for healthcare providers, managers, and policy makers and more evidence is needed on data mining's overall impact on healthcare services and patient care.
Artificial Post mining lakes - a challenge for the integration in natural hydrography and river basin management

Science.gov (United States)

Fleischhammel, Petra; Schoenheinz, Dagmar; Grünewald, Uwe

2010-05-01

In terms of the European Water Framework Directive (WFD), post mining lakes are artificial water bodies (AWB). The sustainable integration of post mining lakes in the groundwater and surface water landscape and their consideration in river basin management plans have to be linked with various (geo)hydrological, hydro(geo)chemical, technological and socioeconomic issues. The Lower Lusatian lignite mining district in eastern Germany is part of the major river basins of river Elbe and river Oder. Regionally, the mining area is situated in the sub-basins of river Spree and Schwarze Elster. After the cessation of mining activities and thereby of the artificially created groundwater drawdown in numerous mining pits, a large number of post mining lakes are evolving as consequence of natural groundwater table recovery. The lakes' designated uses vary from water reservoirs to landscape, recreation or fish farming lakes. Groundwater raise is not only substantial for the lake filling, but also for the area rehabilitation and a largely self regulated water balance in post mining landscapes. Since the groundwater flow through soil and dump sites being affected by the former mining activities, groundwater experiences various changes in its hydrochemical properties as e.g. mineralization and acidification. Consequently, downstream located groundwater fed running and standing water bodies will be affected too. Respective the European Water Framework Directive, artificial post mining lakes are not allowed to cause significant adverse impacts on the good ecological status/potential of downstream groundwater and surface water bodies. The high sulphate concentrations of groundwater fed mining lakes which reach partly more than 1,000 mg/l are e.g. damaging concrete constructures in downstream water bodies thereby representing threats for hydraulic facilities and drinking water supply. Due to small amounts of nutrients, the lakes are characterised by oligo¬trophic to slightly
Soil formation and soil biological properties post mining sites after coal mining in central Europe

Czech Academy of Sciences Publication Activity Database

Kaneda, Satoshi; Frouz, Jan; Krištůfek, Václav; Elhottová, Dana; Pižl, Václav; Starý, Josef; Háněl, Ladislav; Tajovský, Karel; Chroňáková, Alica

2007-01-01

Roč. 53, - (2007), s. 13 ISSN 0288-5840. [Annual Meeting Japanese Society of Soil Science and Plant Nutrition . 22.08.2007, Setagaya city] Institutional research plan: CEZ:AV0Z60660521 Keywords : soil formation * soil biological properties * post mining sites Subject RIV: EH - Ecology, Behaviour
Fast control and data acquisition in the neutral beam test facility

International Nuclear Information System (INIS)

Luchetta, A.; Manduchi, G.; Taliercio, C.

2014-01-01

Highlights: • The paper describes the fast control and data acquisition in the ITER neutral beam test facility. • The usage of real time control in ion beam generation and extraction is proposed. • Real time management of breakdowns is described. • The implementation of event-driven data acquisition is reported. - Abstract: Fast control and data acquisition are required in the ion source test bed of the ITER neutral beam test facility, referred to as SPIDER. Fast control will drive the operation of the power supply systems with particular reference to special asynchronous events, such as the breakdowns. These are short-circuits among grids or between grids and vessel that can occur repeatedly during beam operation. They are normal events and, as such, they will be managed by the fast control system. Cycle time associated to such fast control is down to hundreds of microseconds. Fast data acquisition is required when breakdowns occur. Event-driven data acquisition is triggered in real time by fast control at the occurrence of each breakdown. Pre- and post-event samples are acquired, allowing capturing information on transient phenomena in a whole time-window centered on the event. Sampling rate of event-driven data acquisition is up to 5 MS/s. Fast data acquisition may also be independent of breakdowns as in the case of the cavity ring-down spectroscopy where data chunks are acquired at 100 MS/s in bursts of 1.5 ms every 100 ms and are processed in real time to produce derived measurements. The paper after the description of the SPIDER fast control and data acquisition application will report the system design based on commercially available hardware and the MARTe and MDSplus software frameworks. The results obtained by running a full prototype of the fast control and data acquisition system are also reported and discussed. They demonstrate that all SPIDER fast control and data acquisition requirements can be met in the prototype solution
Fast control and data acquisition in the neutral beam test facility

Energy Technology Data Exchange (ETDEWEB)

Luchetta, A., E-mail: adriano.luchetta@igi.cnr.it; Manduchi, G.; Taliercio, C.

2014-05-15

Highlights: • The paper describes the fast control and data acquisition in the ITER neutral beam test facility. • The usage of real time control in ion beam generation and extraction is proposed. • Real time management of breakdowns is described. • The implementation of event-driven data acquisition is reported. - Abstract: Fast control and data acquisition are required in the ion source test bed of the ITER neutral beam test facility, referred to as SPIDER. Fast control will drive the operation of the power supply systems with particular reference to special asynchronous events, such as the breakdowns. These are short-circuits among grids or between grids and vessel that can occur repeatedly during beam operation. They are normal events and, as such, they will be managed by the fast control system. Cycle time associated to such fast control is down to hundreds of microseconds. Fast data acquisition is required when breakdowns occur. Event-driven data acquisition is triggered in real time by fast control at the occurrence of each breakdown. Pre- and post-event samples are acquired, allowing capturing information on transient phenomena in a whole time-window centered on the event. Sampling rate of event-driven data acquisition is up to 5 MS/s. Fast data acquisition may also be independent of breakdowns as in the case of the cavity ring-down spectroscopy where data chunks are acquired at 100 MS/s in bursts of 1.5 ms every 100 ms and are processed in real time to produce derived measurements. The paper after the description of the SPIDER fast control and data acquisition application will report the system design based on commercially available hardware and the MARTe and MDSplus software frameworks. The results obtained by running a full prototype of the fast control and data acquisition system are also reported and discussed. They demonstrate that all SPIDER fast control and data acquisition requirements can be met in the prototype solution.

Accounting and Financial Data Analysis Data Mining Tools

Directory of Open Access Journals (Sweden)

Diana Elena Codreanu

2011-05-01

Full Text Available Computerized accounting systems in recent years have seen an increase in complexity due to thecompetitive economic environment but with the help of data analysis solutions such as OLAP and DataMining can be a multidimensional data analysis, can detect the fraud and can discover knowledge hidden indata, ensuring such information is useful for decision making within the organization. In the literature thereare many definitions for data mining but all boils down to same idea: the process takes place to extract newinformation from large data collections, information without the aid of data mining tools would be verydifficult to obtain. Information obtained by data mining process has the advantage that only respond to thequestion of what happens but at the same time argue and show why certain things are happening. In this paperwe wish to present advanced techniques for analysis and exploitation of data stored in a multidimensionaldatabase.
Contrast data mining concepts, algorithms, and applications

CERN Document Server

Dong, Guozhu

2012-01-01

A Fruitful Field for Researching Data Mining Methodology and for Solving Real-Life Problems Contrast Data Mining: Concepts, Algorithms, and Applications collects recent results from this specialized area of data mining that have previously been scattered in the literature, making them more accessible to researchers and developers in data mining and other fields. The book not only presents concepts and techniques for contrast data mining, but also explores the use of contrast mining to solve challenging problems in various scientific, medical, and business domains. Learn from Real Case Studies
Data Mining Solutions for the Business Environment

Directory of Open Access Journals (Sweden)

Ruxandra-Stefania PETRE

2014-02-01

Full Text Available Over the past years, data mining became a matter of considerable importance due to the large amounts of data available in the applications belonging to various domains. Data mining, a dynamic and fast-expanding field, that applies advanced data analysis techniques, from statistics, machine learning, database systems or artificial intelligence, in order to discover relevant patterns, trends and relations contained within the data, information impossible to observe using other techniques. The paper focuses on presenting the applications of data mining in the business environment. It contains a general overview of data mining, providing a definition of the concept, enumerating six primary data mining techniques and mentioning the main fields for which data mining can be applied. The paper also presents the main business areas which can benefit from the use of data mining tools, along with their use cases: retail, banking and insurance. Also the main commercially available data mining tools and their key features are presented within the paper. Besides the analysis of data mining and the business areas that can successfully apply it, the paper presents the main features of a data mining solution that can be applied for the business environment and the architecture, with its main components, for the solution, that would help improve customer experiences and decision-making
Collaborative Data Mining Tool for Education

Science.gov (United States)

Garcia, Enrique; Romero, Cristobal; Ventura, Sebastian; Gea, Miguel; de Castro, Carlos

2009-01-01

This paper describes a collaborative educational data mining tool based on association rule mining for the continuous improvement of e-learning courses allowing teachers with similar course's profile sharing and scoring the discovered information. This mining tool is oriented to be used by instructors non experts in data mining such that, its…
High-performance secure multi-party computation for data mining applications

DEFF Research Database (Denmark)

Bogdanov, Dan; Niitsoo, Margus; Toft, Tomas

2012-01-01

Secure multi-party computation (MPC) is a technique well suited for privacy-preserving data mining. Even with the recent progress in two-party computation techniques such as fully homomorphic encryption, general MPC remains relevant as it has shown promising performance metrics in real...... operations such as multiplication and comparison. Secondly, the confidential processing of financial data requires the use of more complex primitives, including a secure division operation. This paper describes new protocols in the Sharemind model for secure multiplication, share conversion, equality, bit...
Spatio-Temporal Pattern Mining on Trajectory Data Using Arm

Science.gov (United States)

Khoshahval, S.; Farnaghi, M.; Taleai, M.

2017-09-01

Preliminary mobile was considered to be a device to make human connections easier. But today the consumption of this device has been evolved to a platform for gaming, web surfing and GPS-enabled application capabilities. Embedding GPS in handheld devices, altered them to significant trajectory data gathering facilities. Raw GPS trajectory data is a series of points which contains hidden information. For revealing hidden information in traces, trajectory data analysis is needed. One of the most beneficial concealed information in trajectory data is user activity patterns. In each pattern, there are multiple stops and moves which identifies users visited places and tasks. This paper proposes an approach to discover user daily activity patterns from GPS trajectories using association rules. Finding user patterns needs extraction of user's visited places from stops and moves of GPS trajectories. In order to locate stops and moves, we have implemented a place recognition algorithm. After extraction of visited points an advanced association rule mining algorithm, called Apriori was used to extract user activity patterns. This study outlined that there are useful patterns in each trajectory that can be emerged from raw GPS data using association rule mining techniques in order to find out about multiple users' behaviour in a system and can be utilized in various location-based applications.
PECAN: library-free peptide detection for data-independent acquisition tandem mass spectrometry data

Energy Technology Data Exchange (ETDEWEB)

Ting, Ying S.; Egertson, Jarrett D.; Bollinger, James G.; Searle, Brian C.; Payne, Samuel H.; Noble, William Stafford; MacCoss, Michael J.

2017-08-07

Data-independent acquisition (DIA) is an emerging mass spectrometry (MS)-based technique for unbiased and reproducible measurement of protein mixtures. DIA tandem mass spectrometry spectra are often highly multiplexed, containing product ions from multiple cofragmenting precursors. Detecting peptides directly from DIA data is therefore challenging; most DIA data analyses require spectral libraries. Here we present PECECAN (http://pecan.maccosslab.org), a library-free, peptide-centric tool that robustly and accurately detects peptides directly from DIA data. PECECAN reports evidence of detection based on product ion scoring, which enables detection of low-abundance analytes with poor precursor ion signal. We demonstrate the chromatographic peak picking accuracy and peptide detection capability of PECECAN, and we further validate its detection with data-dependent acquisition and targeted analyses. Lastly, we used PECECAN to build a plasma proteome library from DIA data and to query known sequence variants.
Set-oriented data mining in relational databases

NARCIS (Netherlands)

Houtsma, M.A.W.; Swami, Arun

1995-01-01

Data mining is an important real-life application for businesses. It is critical to find efficient ways of mining large data sets. In order to benefit from the experience with relational databases, a set-oriented approach to mining data is needed. In such an approach, the data mining operations are
A Four Channel Beam Current Monitor Data Acquisition System Using Embedded Processors

Energy Technology Data Exchange (ETDEWEB)

Wheat, Jr., Robert Mitchell [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Dalmas, Dale A. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Dale, Gregory E. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

2015-08-11

Data acquisition from multiple beam current monitors is required for electron accelerator production of Mo-99. A two channel system capable of recording data from two beam current monitors has been developed, is currently in use, and is discussed below. The development of a cost-effective method of extending this system to more than two channels and integrating of these measurements into an accelerator control system is the main focus of this report. Data from these current monitors is digitized, processed, and stored by a digital data acquisition system. Limitations and drawbacks with the currently deployed digital data acquisition system have been identified as have been potential solutions, or at least improvements, to these problems. This report will discuss and document the efforts we've made in improving the flexibility and lowering the cost of the data acquisition system while maintaining the minimum requirements.
OD Matrix Acquisition Based on Mobile Phone Positioning Data

Directory of Open Access Journals (Sweden)

Xiaoqing ZUO

2014-06-01

Full Text Available Dynamic OD matrix is basic data of traffic travel guidance, traffic control, traffic management and traffic planning, and reflects the basic needs of travelers on the traffic network. With the rising popularity of positioning technology and the communication technology and the generation of huge mobile phone users, the mining and use of mobile phone positioning data, can get more traffic intersections and import and export data. These data will be integrated into obtaining the regional OD matrix, which is bound to bring convenience. In this article, mobile phone positioning data used in the data acquisition of intelligent transportation system, research a kind of regional dynamic OD matrix acquisition method based on the mobile phone positioning data. The method based on purpose of transportation, using time series similarity classification algorithm based on piecewise linear representation of the corner point (CP-PLR, mapping each base station cell to traffic zone of different traffic characteristics, and through a series of mapping optimization of base station cell to traffic zone to realize city traffic zone division based on mobile phone traffic data, on the basis, adjacency matrix chosen as the physical data structure of OD matrix storage, the principle of obtaining regional dynamic OD matrix based on the mobile phone positioning data are expounded, and the algorithm of obtaining regional dynamic OD matrix based on mobile phone positioning data are designed and verified.
Data acquisition using PDP-11 and MBD branch driver at L.B.L

International Nuclear Information System (INIS)

Harvey, E.H. Jr.

1979-05-01

A data acquisition system was designed and preliminary versions implemented for physics experiments at LBL. It utilizes an MBD microprogramed branch driver and a PDP-11 operating under RSM-11M. An MBD executive is used to implement software breakpoints within the MBD. The PDP-11 data acquisition buffer management scheme allows for multiple tasks dynamically accessing the data stream. Emphasis is directed towards future expansions. High data rates are also emphasized. 2 tables
High speed USB data logger for position sensitive detector data acquisition

International Nuclear Information System (INIS)

Poudel, S.K.; Kulkarni, V.B.; Kumar, Santosh; Chandak, R.M.; Krishna, P.S.R.; Mukhopadhyay, R.

2010-01-01

Ratio ADC (RDC) module used in neutron Position Sensitive Detector (PSD) data acquisition, gives digital code signifying the position of neutron event. A High Speed USB based RDC Data Logger card has been made for logging data from multiple RDCs to PC. A CPLD on the card continuously polls the RDCs for data, and fills it in the FIFO memory of a high speed USB microcontroller. A VC++ program for neutron scattering experiments reads event codes from FIFO of microcontroller and builds spectrum on PC. This program sweeps physical parameters of sample and collects PSD data for pre-determined monitor counts. (author)
Application of data mining techniques for nuclear data and instrumentation

International Nuclear Information System (INIS)

Toshniwal, Durga

2013-01-01

Data mining is defined as the discovery of previously unknown, valid, novel, potentially useful, and understandable patterns in large databases. It encompasses many different techniques and algorithms which differ in the kinds of data that can be analyzed and the form of knowledge representation used to convey the discovered knowledge. Patterns in the data can be represented in many different forms, including classification rules, association rules, clusters, etc. Data mining thus deals with the discovery of hidden trends and patterns from large quantities of data. The field of data mining is emerging as a new, fundamental research area with important applications to science, engineering, medicine, business, and education. It is an interdisciplinary research area and draws upon several roots, including database systems, machine learning, information systems, statistics and expert systems. Data mining, when performed on time series data, is known as time series data mining (TSDM). A time series is a sequence of real numbers, each number representing a value at a point of time. During the past few years, there has been an explosion of research in the area of time series data mining. This includes attempts to model time series data, to design languages to query such data, and to develop access structures to efficiently process queries on such data. Time series data arises naturally in many real-world applications. Efficient discovery of knowledge through time series data mining can be helpful in several domains such as: Stock market analysis, Weather forecasting etc. An important application area of data mining techniques is in nuclear power plant and related data. Nuclear power plant data can be represented in form of time sequences. Often it may be of prime importance to analyze such data to find trends and anomalies. The general goals of data mining include feature extraction, similarity search, clustering and classification, association rule mining and anomaly
Process mining online assessment data

NARCIS (Netherlands)

Pechenizkiy, M.; Trcka, N.; Vasilyeva, E.; Aalst, van der W.M.P.; De Bra, P.M.E.; Barnes, T.; Desmarais, M.; Romero, C.; Ventura, S.

2009-01-01

Traditional data mining techniques have been extensively applied to find interesting patterns, build descriptive and predictive models from large volumes of data accumulated through the use of different information systems. The results of data mining can be used for getting a better understanding of
Applied data mining for business and industry

CERN Document Server

Giudici, Paolo

2009-01-01

The increasing availability of data in our current, information overloaded society has led to the need for valid tools for its modelling and analysis. Data mining and applied statistical methods are the appropriate tools to extract knowledge from such data. This book provides an accessible introduction to data mining methods in a consistent and application oriented statistical framework, using case studies drawn from real industry projects and highlighting the use of data mining methods in a variety of business applications. Introduces data mining methods and applications.Covers classical and Bayesian multivariate statistical methodology as well as machine learning and computational data mining methods.Includes many recent developments such as association and sequence rules, graphical Markov models, lifetime value modelling, credit risk, operational risk and web mining.Features detailed case studies based on applied projects within industry.Incorporates discussion of data mining software, with case studies a...
Pocket data mining big data on small devices

CERN Document Server

Gaber, Mohamed Medhat; Gomes, Joao Bartolo

2014-01-01

Owing to continuous advances in the computational power of handheld devices like smartphones and tablet computers, it has become possible to perform Big Data operations including modern data mining processes onboard these small devices. A decade of research has proved the feasibility of what has been termed as Mobile Data Mining, with a focus on one mobile device running data mining processes. However, it is not before 2010 until the authors of this book initiated the Pocket Data Mining (PDM) project exploiting the seamless communication among handheld devices performing data analysis tasks that were infeasible until recently. PDM is the process of collaboratively extracting knowledge from distributed data streams in a mobile computing environment. This book provides the reader with an in-depth treatment on this emerging area of research. Details of techniques used and thorough experimental studies are given. More importantly and exclusive to this book, the authors provide detailed practical guide on the depl...
Analysis of the planned post-mining landscape of MIBRAG's open-cast mines with regard to a possible environmental impact of alteration processes in mixed dumps

International Nuclear Information System (INIS)

Jolas, P.; Hofmann, B.

2010-01-01

There has been an increasing body of knowledge with regard to hydro- and geochemical alteration processes in overburden dumps and their impact on groundwater quality in lignite mining and reclamation operations associated with post-mining landscapes in Germany. The operators of the MIBRAG mines have examined issues regarding alteration processes and how they affect the environment and which opportunities exist to actively influence the dumping process. The objectives were to counteract any possible negative impact of the alteration processes. Special emphasis was on the impact caused by oxidation of sulfur containing minerals. This paper presented an analysis of the situation at United Schleenhain Mine and how it reflects on the work to date for MIBRAG's mines. A future outlook was also presented. Specifically, the paper discussed the development of the United Schleenhain mine and the post-mining landscape. The potential for discharge of substances was also evaluated along with acidification. 1 tab., 5 figs.
MAST data acquisition system

International Nuclear Information System (INIS)

Shibaev, S.; Counsell, G.; Cunningham, G.; Manhood, S.J.; Thomas-Davies, N.; Waterhouse, J.

2006-01-01

The data acquisition system of the Mega-Amp Spherical Tokamak (MAST) presently collects up to 400 MB of data in about 3000 data items per shot, and subsequent fast growth is expected. Since the start of MAST operations (in 1999) the system has changed dramatically. Though we continue to use legacy CAMAC hardware, newer VME, PCI, and PXI based sub-systems collect most of the data now. All legacy software has been redesigned and new software has been developed. Last year a major system improvement was made-replacement of the message distribution system. The new message system provides easy connection of any sub-system independently of its platform and serves as a framework for many new applications. A new data acquisition controller provides full control of common sub-systems, central error logging, and data acquisition alarms for the MAST plant. A number of new sub-systems using Linux and Windows OSs on VME, PCI, and PXI platforms have been developed. A new PXI unit has been designed as a base sub-system accommodating any type of data acquisition and control devices. Several web applications for the real-time MAST monitoring and data presentation have been developed
Data mining for social network data

CERN Document Server

Memon, Nasrullah; Hicks, David L; Chen, Hsinchun

2010-01-01

Driven by counter-terrorism efforts, marketing analysis and an explosion in online social networking in recent years, data mining has moved to the forefront of information science. This proposed Special Issue on ""Data Mining for Social Network Data"" will present a broad range of recent studies in social networking analysis. It will focus on emerging trends and needs in discovery and analysis of communities, solitary and social activities, and activities in open fora, and commercial sites as well. It will also look at network modeling, infrastructure construction, dynamic growth and evolution
Organizational Data Mining

Science.gov (United States)

Nemati, Hamid R.; Barko, Christopher D.

Many organizations today possess substantial quantities of business information but have very little real business knowledge. A recent survey of 450 business executives reported that managerial intuition and instinct are more prevalent than hard facts in driving organizational decisions. To reverse this trend, businesses of all sizes would be well advised to adopt Organizational Data Mining (ODM). ODM is defined as leveraging Data Mining tools and technologies to enhance the decision-making process by transforming data into valuable and actionable knowledge to gain a competitive advantage. ODM has helped many organizations optimize internal resource allocations while better understanding and responding to the needs of their customers. The fundamental aspects of ODM can be categorized into Artificial Intelligence (AI), Information Technology (IT), and Organizational Theory (OT), with OT being the key distinction between ODM and Data Mining. In this chapter, we introduce ODM, explain its unique characteristics, and report on the current status of ODM research. Next we illustrate how several leading organizations have adopted ODM and are benefiting from it. Then we examine the evolution of ODM to the present day and conclude our chapter by contemplating ODM's challenging yet opportunistic future.

Process Mining Online Assessment Data

Science.gov (United States)

Pechenizkiy, Mykola; Trcka, Nikola; Vasilyeva, Ekaterina; van der Aalst, Wil; De Bra, Paul

2009-01-01

Traditional data mining techniques have been extensively applied to find interesting patterns, build descriptive and predictive models from large volumes of data accumulated through the use of different information systems. The results of data mining can be used for getting a better understanding of the underlying educational processes, for…
Soudan 2 data acquisition and trigger electronics

International Nuclear Information System (INIS)

Dawson, J.; Laird, R.; May, E.; Mondal, N.; Schlereth, J.; Solomey, N.; Thron, J.; Heppelmann, S.

1985-01-01

The 1.1 kton Soudan 2 detector is read out by 16K anode wires and 3 2K cathode strips. Preamps from each wire or strip are bussed together in groups of 8 to reduce the number of ADC channels. The resulting 6144 channels of ionization signal are flash-digitized every 150 ns and stored in RAM. The raw data hit patterns are continually compared with programmable trigger multiplicity and adjacency conditions. The data acquisition process is managed in a system of 24 parallel crates each containing an Intel 8086 microprocessors, which supervises a pipe-lined data compactors, and allows transfer of the compacted data via CAMAC to the host computer. The 8086's also manage the local trigger conditions and can perform some parallel processing of the data. Due to the scale of the system and multiplicity of identical channels, semi-custom gate array chips are used for much of the logic, utilizing 2.5 micron CMOS technology
Soudan 2 data acquisition and trigger electronics

International Nuclear Information System (INIS)

Dawson, J.; Haberichter, W.; Laird, R.

1985-01-01

The 1.1 kton Soudan 2 calorimetric drift-chamber detector is read out by 16K anode wires and 32K cathode strips. Preamps from each wire or strip are bussed together in groups of 8 to reduce the number of ADC channels. The resulting 6144 channels of ionization signal are flash-digitized every 200 ns and stored in RAM. The raw data hit patterns are continually compared with programmable trigger multiplicity and adjacency conditions. The data acquisition process is managed in a system of 24 parallel crates each containing an Intel 80C86 microprocessor, which supervises a pipe-lined data compactor, and allows transfer of the compacted data via CAMAC to the host computer. The 80C86's also manage the local trigger conditions and can perform some parallel processing of the data. Due to the scale of the system and multiplicity of identical channels, semi-custom gate array chips are used for much of the logic, utilizing 2.5 micron CMOS technology
Soudan 2 data acquisition and trigger electronics

International Nuclear Information System (INIS)

Dawson, J.; Heppelmann, S.; Laird, R.; May, E.; Mondal, N.; Schlereth, J.; Solomey, N.; Thron, J.

1985-01-01

The 1.1 kton Soudan 2 detector is read out by 16K anode wires and 32K cathode strips. Preamps from each wire or strip are bussed together in groups of 8 to reduce the number of ADC channels. The resulting 6144 channels of ionization signal are flash-digitized every 150 ns and stored in RAM. The raw data hit patterns are continually compared with programmable trigger multiplicity and adjacency conditions. The data acquisition process is managed in a system of 24 parallel crates each containing an Intel 8086 microprocessors, which supervises a pipe-lined data compactors, and allows transfer of the compacted data via CAMAC to the host computer. The 8086's also manage the local trigger conditions and can perform some parallel processing of the data. Due to the scale of the system and multiplicity of identical channels, semi-custom gate array chips are used for much of the logic, utilizing 2.5 micron CMOS technology
Acquisition of Multiple Questions in English, Russian, and Malayalam

Science.gov (United States)

Grebenyova, Lydia

2011-01-01

This article presents the results of four studies exploring the acquisition of the language-specific syntactic and semantic properties of multiple interrogatives in English, Russian, and Malayalam, languages that behave differently with respect to the syntax and semantics of multiple interrogatives. A corpus analysis investigated the frequency of…
Interestingness measures and strategies for mining multi-ontology multi-level association rules from gene ontology annotations for the discovery of new GO relationships.

Science.gov (United States)

Manda, Prashanti; McCarthy, Fiona; Bridges, Susan M

2013-10-01

The Gene Ontology (GO), a set of three sub-ontologies, is one of the most popular bio-ontologies used for describing gene product characteristics. GO annotation data containing terms from multiple sub-ontologies and at different levels in the ontologies is an important source of implicit relationships between terms from the three sub-ontologies. Data mining techniques such as association rule mining that are tailored to mine from multiple ontologies at multiple levels of abstraction are required for effective knowledge discovery from GO annotation data. We present a data mining approach, Multi-ontology data mining at All Levels (MOAL) that uses the structure and relationships of the GO to mine multi-ontology multi-level association rules. We introduce two interestingness measures: Multi-ontology Support (MOSupport) and Multi-ontology Confidence (MOConfidence) customized to evaluate multi-ontology multi-level association rules. We also describe a variety of post-processing strategies for pruning uninteresting rules. We use publicly available GO annotation data to demonstrate our methods with respect to two applications (1) the discovery of co-annotation suggestions and (2) the discovery of new cross-ontology relationships. Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.
Data acquisition in a high-speed rotating frame for New Mexico Institute of Mining and Technology liquid sodium αω dynamo experiment.

Science.gov (United States)

Si, Jiahe; Colgate, Stirling A; Li, Hui; Martinic, Joe; Westpfahl, David

2013-10-01

New Mexico Institute of Mining and Technology liquid sodium αω-dynamo experiment models the magnetic field generation in the universe as discussed in detail by Colgate, Li, and Pariev [Phys. Plasmas 8, 2425 (2001)]. To obtain a quasi-laminar flow with magnetic Reynolds number R(m) ~ 120, the dynamo experiment consists of two co-axial cylinders of 30.5 cm and 61 cm in diameter spinning up to 70 Hz and 17.5 Hz, respectively. During the experiment, the temperature of the cylinders must be maintained to 110 °C to ensure that the sodium remains fluid. This presents a challenge to implement a data acquisition (DAQ) system in such high temperature, high-speed rotating frame, in which the sensors (including 18 Hall sensors, 5 pressure sensors, and 5 temperature sensors, etc.) are under the centrifugal acceleration up to 376g. In addition, the data must be transmitted and stored in a computer 100 ft away for safety. The analog signals are digitized, converted to serial signals by an analog-to-digital converter and a field-programmable gate array. Power is provided through brush/ring sets. The serial signals are sent through ring/shoe sets capacitively, then reshaped with cross-talk noises removed. A microcontroller-based interface circuit is used to decode the serial signals and communicate with the data acquisition computer. The DAQ accommodates pressure up to 1000 psi, temperature up to more than 130 °C, and magnetic field up to 1000 G. First physics results have been analyzed and published. The next stage of the αω-dynamo experiment includes the DAQ system upgrade.
Performance Confirmation Data Acquisition System

International Nuclear Information System (INIS)

D.W. Markman

2000-01-01

The purpose of this analysis is to identify and analyze concepts for the acquisition of data in support of the Performance Confirmation (PC) program at the potential subsurface nuclear waste repository at Yucca Mountain. The scope and primary objectives of this analysis are to: (1) Review the criteria for design as presented in the Performance Confirmation Data Acquisition/Monitoring System Description Document, by way of the Input Transmittal, Performance Confirmation Input Criteria (CRWMS M and O 1999c). (2) Identify and describe existing and potential new trends in data acquisition system software and hardware that would support the PC plan. The data acquisition software and hardware will support the field instruments and equipment that will be installed for the observation and perimeter drift borehole monitoring, and in-situ monitoring within the emplacement drifts. The exhaust air monitoring requirements will be supported by a data communication network interface with the ventilation monitoring system database. (3) Identify the concepts and features that a data acquisition system should have in order to support the PC process and its activities. (4) Based on PC monitoring needs and available technologies, further develop concepts of a potential data acquisition system network in support of the PC program and the Site Recommendation and License Application
Data Mining Based on Cloud-Computing Technology

Directory of Open Access Journals (Sweden)

Ren Ying

2016-01-01

Full Text Available There are performance bottlenecks and scalability problems when traditional data-mining system is used in cloud computing. In this paper, we present a data-mining platform based on cloud computing. Compared with a traditional data mining system, this platform is highly scalable, has massive data processing capacities, is service-oriented, and has low hardware cost. This platform can support the design and applications of a wide range of distributed data-mining systems.
Data mining mobile devices

CERN Document Server

Mena, Jesus

2013-01-01

With today's consumers spending more time on their mobiles than on their PCs, new methods of empirical stochastic modeling have emerged that can provide marketers with detailed information about the products, content, and services their customers desire.Data Mining Mobile Devices defines the collection of machine-sensed environmental data pertaining to human social behavior. It explains how the integration of data mining and machine learning can enable the modeling of conversation context, proximity sensing, and geospatial location throughout large communities of mobile users
A Survey of Educational Data-Mining Research

Science.gov (United States)

Huebner, Richard A.

2013-01-01

Educational data mining (EDM) is an emerging discipline that focuses on applying data mining tools and techniques to educationally related data. The discipline focuses on analyzing educational data to develop models for improving learning experiences and improving institutional effectiveness. A literature review on educational data mining topics…
Next generation PET data acquisition architectures

Science.gov (United States)

Jones, W. F.; Reed, J. H.; Everman, J. L.; Young, J. W.; Seese, R. D.

1997-06-01

New architectures for higher performance data acquisition in PET are proposed. Improvements are demanded primarily by three areas of advancing PET state of the art. First, larger detector arrays such as the Hammersmith ECAT/sup (R/) EXACT HR/sup ++/ exceed the addressing capacity of 32 bit coincidence event words. Second, better scintillators (LSO) make depth-of interaction (DOI) and time-of-flight (TOF) operation more practical. Third, fully optimized single photon attenuation correction requires higher rates of data collection. New technologies which enable the proposed third generation Real Time Sorter (RTS III) include: (1) 80 Mbyte/sec Fibre Channel RAID disk systems, (2) PowerPC on both VMEbus and PCI Local bus, and (3) quadruple interleaved DRAM controller designs. Data acquisition flexibility is enhanced through a wider 64 bit coincidence event word. PET methodology support includes DOI (6 bits), TOF (6 bits), multiple energy windows (6 bits), 512/spl times/512 sinogram indexes (18 bits), and 256 crystal rings (16 bits). Throughput of 10 M events/sec is expected for list-mode data collection as well as both on-line and replay histogramming. Fully efficient list-mode storage for each PET application is provided by real-time bit packing of only the active event word bits. Real-time circuits provide DOI rebinning.
Next generation PET data acquisition architectures

International Nuclear Information System (INIS)

Jones, W.F.; Reed, J.H.; Everman, J.L.

1996-01-01

New architectures for higher performance data acquisition in PET are proposed. Improvements are demanded primarily by three areas of advancing PET state of the art. First, larger detector arrays such as the Hammersmith ECAT reg-sign EXACT HR ++ exceed the addressing capacity of 32 bit coincidence event words. Second, better scintillators (LSO) make depth-of-interaction (DOI) and time-of-flight (TOF) operation more practical. Third, fully optimized single photon attenuation correction requires higher rates of data collection. New technologies which enable the proposed third generation Real Time Sorter (RTS III) include: (1) 80 M byte/sec Fibre Channel RAID disk systems, (2) PowerPC on both VMEbus and PCI Local bus, and (3) quadruple interleaved DRAM controller designs. Data acquisition flexibility is enhanced through a wider 64 bit coincidence event word. PET methodology support includes DOI (6 bits), TOF (6 bits), multiple energy windows (6 bits), 512 x 512 sinogram indexes (18 bits), and 256 crystal rings (16 bits). Throughput of 10 M events/sec is expected for list-mode data collection as well as both on-line and replay histogramming. Fully efficient list-mode storage for each PET application is provided by real-time bit packing of only the active event word bits. Real-time circuits provide DOI rebinning
Data Mining Mining Data: MSHA Enforcement Efforts, Underground Coal Mine Safety, and New Health Implications

OpenAIRE

Kniesner, Thomas J.; Leeth, John D.

2003-01-01

Studies of industrial safety regulations, OSHA in particular, often find little effect on worker safety. Critics of the regulatory approach argue that safety standards have little to do with industrial injuries, and defenders of the regulatory approach cite infrequent inspections and low penalties for violating safety standards. We use recently assembled data from the Mine Safety and Health Administration (MSHA) concerning underground coal mine production, safety regulatory activities, and wo...
Open-source tools for data mining.

Science.gov (United States)

Zupan, Blaz; Demsar, Janez

2008-03-01

With a growing volume of biomedical databases and repositories, the need to develop a set of tools to address their analysis and support knowledge discovery is becoming acute. The data mining community has developed a substantial set of techniques for computational treatment of these data. In this article, we discuss the evolution of open-source toolboxes that data mining researchers and enthusiasts have developed over the span of a few decades and review several currently available open-source data mining suites. The approaches we review are diverse in data mining methods and user interfaces and also demonstrate that the field and its tools are ready to be fully exploited in biomedical research.
Microcomputer data acquisition and control.

Science.gov (United States)

East, T D

1986-01-01

In medicine and biology there are many tasks that involve routine well defined procedures. These tasks are ideal candidates for computerized data acquisition and control. As the performance of microcomputers rapidly increases and cost continues to go down the temptation to automate the laboratory becomes great. To the novice computer user the choices of hardware and software are overwhelming and sadly most of the computer sales persons are not at all familiar with real-time applications. If you want to bill your patients you have hundreds of packaged systems to choose from; however, if you want to do real-time data acquisition the choices are very limited and confusing. The purpose of this chapter is to provide the novice computer user with the basics needed to set up a real-time data acquisition system with the common microcomputers. This chapter will cover the following issues necessary to establish a real time data acquisition and control system: Analysis of the research problem: Definition of the problem; Description of data and sampling requirements; Cost/benefit analysis. Choice of Microcomputer hardware and software: Choice of microprocessor and bus structure; Choice of operating system; Choice of layered software. Digital Data Acquisition: Parallel Data Transmission; Serial Data Transmission; Hardware and software available. Analog Data Acquisition: Description of amplitude and frequency characteristics of the input signals; Sampling theorem; Specification of the analog to digital converter; Hardware and software available; Interface to the microcomputer. Microcomputer Control: Analog output; Digital output; Closed-Loop Control. Microcomputer data acquisition and control in the 21st Century--What is in the future? High speed digital medical equipment networks; Medical decision making and artificial intelligence.
New KENS data acquisition system

International Nuclear Information System (INIS)

Arai, M.; Furusaka, M.; Satoh, S.

1989-01-01

In this report, the authors discuss a data acquisition system, KENSnet, which is newly introduced to the KENS facility. The criteria for the data acquisition system was about 1 MIPS for CPU speed and 150 Mbytes for storage capacity for a computer per spectrometer. VAX computers were chosen with their propreitary operating system, VMS. The Vax computers are connected by a DECnet network mediated by Ethernet. Front-end computers, Apple Macintosh Plus and Macintosh II, were chosen for their user-friendly manipulation and intelligence. New CAMAC-based data acquisition electronics were developed. The data acquisition control program (ICP) and the general data analysis program (Genie) were both developed at ISIS and have been installed. 2 refs., 3 figs., 1 tab
Replacement team of mining drilling rigs

OpenAIRE

Hamodi, Hussan; Lundberg, Jan

2014-01-01

This paper presents a practical model to calculate the optimal replacement time (ORT) of drilling rigs used in underground mining. As a case study, cost data for drilling rig were collected over four years from a Swedish mine. The cost data include acquisition, operating, maintenance and downtime costs when using a redundant rig. A discount rate is used to determine the value of these costs over time. The study develops an optimisation model to identify the ORT of a mining drilling rig which ...
A data acquisition system for the wide angle shower apparatus (WASA)

International Nuclear Information System (INIS)

Gustafsson, L.; Carius, S.; Fransson, K.; Sukhanov, A.

1994-01-01

A new data acquisition system based on concepts such as data switches and multiple-processors is described. The main topic is how data coming from a multicrate front-end in CAMAC, VME and FASTBUS are transported over different links to a buffer-matrix data switch and further into a farm of microprocessors. Modularity, scalability and multilevel data monitoring are important parts of the design goals that are presented. The system is intended for use in an experiment searching for rare events where high interaction rates are necessary and where a fast and selective trigger is difficult to define. Other experimental constraints, the trigger logical structure and the performance of the data acquisition are also described
The MDSplus data acquisition system, current status and future directions

International Nuclear Information System (INIS)

Stillerman, J.; Fredian, T.W.

1999-01-01

The MDSplus data acquisition system was developed in collaboration with the ZTH group at Los Alamos National Laboratory and the RFX group at CNR in Padua, Italy and is currently in use at MIT, RFX in Padua, and TCV at EPFL in Lausanne. MDSplus is based on a hierarchical experiment description which completely describes the data acquisition and analysis tasks and contains the results from these operations. It also includes a set of X/motif based tools for data acquisition and display, as well as diagnostic configuration and management. These tools were designed to operate in a distributed, client/server environment with multiple concurrent readers and writers to the data store. An interface to a relational database is provided for storage and management of processed data. A commercially available package called IDL is used as the primary data analysis and visualization tool. The current projects include a new interface to the electronic logbook, tools for remote collaborators and WWW access, and a port of the system to UNIX and Windows-NT/95. (orig.)

Improving quality of laser scanning data acquisition through calibrated amplitude and pulse deviation measurement

Science.gov (United States)

Pfennigbauer, Martin; Ullrich, Andreas

2010-04-01

Newest developments in laser scanner technologies put surveyors in the position to comply with the ever increasing demand of high-speed, high-accuracy, and highly reliable data acquisition from terrestrial, mobile, and airborne platforms. Echo digitization in pulsed time-of-flight laser ranging has demonstrated its superior performance in the field of bathymetry and airborne laser scanning for more than a decade, however at the cost of somewhat time consuming off line post processing. State-of-the-art online waveform processing as implemented in RIEGL's V-Line not only saves users post-processing time to obtain true 3D point clouds, it also adds the assets of calibrated amplitude and reflectance measurement for data classification and pulse deviation determination for effective and reliable data validation. We present results from data acquisitions in different complex target situations.
Construct mine environment monitoring system based on wireless mesh network

Science.gov (United States)

Chen, Xin; Ge, Gengyu; Liu, Yinmei; Cheng, Aimin; Wu, Jun; Fu, Jun

2018-04-01

The system uses wireless Mesh network as a network transmission medium, and strive to establish an effective and reliable underground environment monitoring system. The system combines wireless network technology and embedded technology to monitor the internal data collected in the mine and send it to the processing center for analysis and environmental assessment. The system can be divided into two parts: the main control network module and the data acquisition terminal, and the SPI bus technology is used for mutual communication between them. Multi-channel acquisition and control interface design Data acquisition and control terminal in the analog signal acquisition module, digital signal acquisition module, and digital signal output module. The main control network module running Linux operating system, in which the transplant SPI driver, USB card driver and AODV routing protocol. As a result, the internal data collection and reporting of the mine are realized.
The role of EPFL to Lorraine's post-mining program

International Nuclear Information System (INIS)

Charpentier, D.

2004-01-01

A century of mining and industry has left scars that make it harder to reconstruct a satisfying economy and environment in northern Lorraine's iron-and-steel and coal basins. For this reason, the French state and Lorraine region are supporting redevelopment in these areas. A special 'post-mining' section in the fourth state/region contract foresees exceptional outlays. The experience acquired since 1986 by the EPFL (land public authority of Lorraine) in handling former industrial sites, recycling real estate and dealing with urban wastelands has led the state and region to rely on it for implementing this program. The EPFL has thus become a financial partner and operator under several headings of the aforementioned section: treating blighted areas; polluted sites and soil; connecting mining basins to other areas; operations for re-shaping the landscape; a pole in environmental engineering; the territorial dynamics of border areas; the search for a large zone for installing business; and real estate reserved for re-housing victims. (author)
SPATIO-TEMPORAL PATTERN MINING ON TRAJECTORY DATA USING ARM

Directory of Open Access Journals (Sweden)

S. Khoshahval

2017-09-01

Full Text Available Preliminary mobile was considered to be a device to make human connections easier. But today the consumption of this device has been evolved to a platform for gaming, web surfing and GPS-enabled application capabilities. Embedding GPS in handheld devices, altered them to significant trajectory data gathering facilities. Raw GPS trajectory data is a series of points which contains hidden information. For revealing hidden information in traces, trajectory data analysis is needed. One of the most beneficial concealed information in trajectory data is user activity patterns. In each pattern, there are multiple stops and moves which identifies users visited places and tasks. This paper proposes an approach to discover user daily activity patterns from GPS trajectories using association rules. Finding user patterns needs extraction of user’s visited places from stops and moves of GPS trajectories. In order to locate stops and moves, we have implemented a place recognition algorithm. After extraction of visited points an advanced association rule mining algorithm, called Apriori was used to extract user activity patterns. This study outlined that there are useful patterns in each trajectory that can be emerged from raw GPS data using association rule mining techniques in order to find out about multiple users’ behaviour in a system and can be utilized in various location-based applications.
APNEA list mode data acquisition and real-time event processing

Energy Technology Data Exchange (ETDEWEB)

Hogle, R.A.; Miller, P. [GE Corporate Research & Development Center, Schenectady, NY (United States); Bramblett, R.L. [Lockheed Martin Specialty Components, Largo, FL (United States)

1997-11-01

The LMSC Active Passive Neutron Examinations and Assay (APNEA) Data Logger is a VME-based data acquisition system using commercial-off-the-shelf hardware with the application-specific software. It receives TTL inputs from eighty-eight {sup 3}He detector tubes and eight timing signals. Two data sets are generated concurrently for each acquisition session: (1) List Mode recording of all detector and timing signals, timestamped to 3 microsecond resolution; (2) Event Accumulations generated in real-time by counting events into short (tens of microseconds) and long (seconds) time bins following repetitive triggers. List Mode data sets can be post-processed to: (1) determine the optimum time bins for TRU assay of waste drums, (2) analyze a given data set in several ways to match different assay requirements and conditions and (3) confirm assay results by examining details of the raw data. Data Logger events are processed and timestamped by an array of 15 TMS320C40 DSPs and delivered to an embedded controller (PowerPC604) for interim disk storage. Three acquisition modes, corresponding to different trigger sources are provided. A standard network interface to a remote host system (Windows NT or SunOS) provides for system control, status, and transfer of previously acquired data. 6 figs.
The Impact of the Dimensions of Transformational Leadership on the Post-acquisition Performance of the Acquired Company

Directory of Open Access Journals (Sweden)

Sladjana Savovic

2017-08-01

Full Text Available Mergers and acquisitions (M&A are the important mechanisms through which companies can achieve growth, gain access to new markets and diversify their activities. Although companies engage themselves in M&As with optimism, empirical evidence shows that many M&A transactions are not successful. Therefore, research is often focused on the identification of the ways to improve post-acquisition performance. One of the key success factors of M&A is to provide adequate transformational leadership during the process of change, especially in the critical phase of the post-acquisition integration. A transformational leader should provide incentives and support to the employees in order for them to accept changes and focus on achieving challenging goals. This paper explores the impact of the different dimensions of transformational leadership on the post-acquisition performance based on the example of a company operating in the Republic of Serbia’s retail sector, which was the subject of a cross-border acquisition. In order to ensure the adequate representativeness of the sample, a questionnaire was distributed in all parts of the company throughout the Republic of Serbia. The results of this study show that all the dimensions of transformational leadership positively impact post-acquisition performance. The “individual consideration” dimension of transformational leadership has the strongest impact on post-acquisition performance, whereas the “intellectual simulation” dimension has the weakest.
Learning data mining with R

CERN Document Server

Makhabel, Bater

2015-01-01

This book is intended for the budding data scientist or quantitative analyst with only a basic exposure to R and statistics. This book assumes familiarity with only the very basics of R, such as the main data types, simple functions, and how to move data around. No prior experience with data mining packages is necessary; however, you should have a basic understanding of data mining concepts and processes.
Rule-based statistical data mining agents for an e-commerce application

Science.gov (United States)

Qin, Yi; Zhang, Yan-Qing; King, K. N.; Sunderraman, Rajshekhar

2003-03-01

Intelligent data mining techniques have useful e-Business applications. Because an e-Commerce application is related to multiple domains such as statistical analysis, market competition, price comparison, profit improvement and personal preferences, this paper presents a hybrid knowledge-based e-Commerce system fusing intelligent techniques, statistical data mining, and personal information to enhance QoS (Quality of Service) of e-Commerce. A Web-based e-Commerce application software system, eDVD Web Shopping Center, is successfully implemented uisng Java servlets and an Oracle81 database server. Simulation results have shown that the hybrid intelligent e-Commerce system is able to make smart decisions for different customers.
Data Acquisition for Modular Biometric Monitoring System

Science.gov (United States)

Chmiel, Alan J. (Inventor); Humphreys, Bradley T. (Inventor); Grodsinsky, Carlos M. (Inventor)

2014-01-01

A modular system for acquiring biometric data includes a plurality of data acquisition modules configured to sample biometric data from at least one respective input channel at a data acquisition rate. A representation of the sampled biometric data is stored in memory of each of the plurality of data acquisition modules. A central control system is in communication with each of the plurality of data acquisition modules through a bus. The central control system is configured to collect data asynchronously, via the bus, from the memory of the plurality of data acquisition modules according to a relative fullness of the memory of the plurality of data acquisition modules.
KENS data acquisition system KENSnet

International Nuclear Information System (INIS)

Arai, Masatoshi; Furusaka, Michihiro; Satoh, Setsuo; Johnson, M.W.

1988-01-01

The installation of a new data acquisition system KENSnet has been completed at the KENS neutron facility. For data collection, 160 Mbytes are necessary for temporary disk storage, and 1 MIPS of CPU is required. For the computing system, models were chosen from the VAX family of computers running their proprietary operating system VMS. The VMS operating system has a very user friendly interface, and is well suited to instrument control applications. New data acquisition electronics were developed. A gate module receives a signal of proton extraction time from the accelerator, and checks the veto signals from the sample environment equipment (vacuum, temperature, chopper phasing, etc.). Then the signal is issued to a delay-time module. A time-control module starts timing from the delayed start signal from the delay-time module, and distributes an encoded time-boundary address to memory modules at the preset times anabling the memory modules to accumulate data histograms. The data acquisition control program (ICP) and the general data analysis program (Genie) were both developed at ISIS, and have been installed in the new data acquisition system. They give the experimenter 'user-friendly' data acquisition and a good environment for data manipulation. The ICP controls the DAE and transfers the histogram data into the computers. (N.K.)
Quantification of Operational Risk Using A Data Mining

Science.gov (United States)

Perera, J. Sebastian

1999-01-01

What is Data Mining? - Data Mining is the process of finding actionable information hidden in raw data. - Data Mining helps find hidden patterns, trends, and important relationships often buried in a sea of data - Typically, automated software tools based on advanced statistical analysis and data modeling technology can be utilized to automate the data mining process
Transparent data mining for big and small data

CERN Document Server

Quercia, Daniele; Pasquale, Frank

2017-01-01

This book focuses on new and emerging data mining solutions that offer a greater level of transparency than existing solutions. Transparent data mining solutions with desirable properties (e.g. effective, fully automatic, scalable) are covered in the book. Experimental findings of transparent solutions are tailored to different domain experts, and experimental metrics for evaluating algorithmic transparency are presented. The book also discusses societal effects of black box vs. transparent approaches to data mining, as well as real-world use cases for these approaches. As algorithms increasingly support different aspects of modern life, a greater level of transparency is sorely needed, not least because discrimination and biases have to be avoided. With contributions from domain experts, this book provides an overview of an emerging area of data mining that has profound societal consequences, and provides the technical background to for readers to contribute to the field or to put existing approaches to prac...
Data Acquisition System

International Nuclear Information System (INIS)

Watwood, D.; Beatty, J.

1991-01-01

The Data Acquisition System (DAS) is comprised of a Hewlett-Packard (HP) model 9816, Series 200 Computer System with the appropriate software to acquire, control, and archive data from a Data Acquisition/Control Unit, models HP3497A and HP3498A. The primary storage medium is an HP9153 16-megabyte hard disc. The data is backed-up on three floppy discs. One floppy disc drive is contained in the HP9153 chassis; the other two comprise an HP9122 dual disc drive. An HP82906A line printer supplies hard copy backup. A block diagram of the hardware setup is shown. The HP3497A/3498A Data Acquisition/Control Units read each input channel and transmit the raw voltage reading to the HP9816 CPU via the HPIB bus. The HP9816 converts this voltage to the appropriate engineering units using the calibration curves for the sensor being read. The HP9816 archives both the raw and processed data along with the time and the readings were taken to hard and floppy discs. The processed values and reading time are printed on the line printer. This system is designed to accommodate several types of sensors; each type is discussed in the following sections
Biomedical Data Mining

NARCIS (Netherlands)

Peek, N.; Combi, C.; Tucker, A.

2009-01-01

Objective: To introduce the special topic of Methods of Information in Medicine on data mining in biomedicine, with selected papers from two workshops on Intelligent Data Analysis in bioMedicine (IDAMAP) held in Verona (2006) and Amsterdam (2007). Methods: Defining the field of biomedical data
Data mining for dummies

CERN Document Server

Brown, Meta S

2014-01-01

Delve into your data for the key to success Data mining is quickly becoming integral to creating value and business momentum. The ability to detect unseen patterns hidden in the numbers exhaustively generated by day-to-day operations allows savvy decision-makers to exploit every tool at their disposal in the pursuit of better business. By creating models and testing whether patterns hold up, it is possible to discover new intelligence that could change your business''s entire paradigm for a more successful outcome. Data Mining for Dummies shows you why it doesn''t take a data scientist to gain
KEEL 3.0: An Open Source Software for Multi-Stage Analysis in Data Mining

Directory of Open Access Journals (Sweden)

Isaac Triguero

2017-01-01

Full Text Available This paper introduces the 3rd major release of the KEEL Software. KEEL is an open source Java framework (GPLv3 license that provides a number of modules to perform a wide variety of data mining tasks. It includes tools to perform data management, design of multiple kind of experiments, statistical analyses, etc. This framework also contains KEEL-dataset, a data repository for multiple learning tasks featuring data partitions and algorithmsr results over these problems. In this work, we describe the most recent components added to KEEL 3.0, including new modules for semi-supervised learning, multi-instance learning, imbalanced classification and subgroup discovery. In addition, a new interface in R has been incorporated to execute algorithms included in KEEL. These new features greatly improve the versatility of KEEL to deal with more modern data mining problems.
The Resource Manager the ATLAS Trigger and Data Acquisition System

CERN Document Server

Aleksandrov, Igor; The ATLAS collaboration; Lehmann Miotto, Giovanna; Soloviev, Igor

2016-01-01

The Resource Manager of the ATLAS Trigger and Data Acquisition system The Resource Manager is one of the core components of the Data Acquisition system of the ATLAS experiment at the LHC. The Resource Manager marshals the right for applications to access resources which may exist in multiple but limited copies, in order to avoid conflicts due to program faults or operator errors. The access to resources is managed in a manner similar to what a lock manager would do in other software systems. All the available resources and their association to software processes are described in the Data Acquisition configuration database. The Resource Manager is queried about the availability of resources every time an application needs to be started. The Resource Manager’s design is based on a client-server model, hence it consists of two components: the Resource Manager "server" application and the "client" shared library. The Resource Manager server implements all the needed functionalities, while the Resource Manager c...
Finding Gold in Data Mining

Science.gov (United States)

Flaherty, Bill

2013-01-01

Data-mining systems provide a variety of opportunities for school district personnel to streamline operations and focus on student achievement. This article describes the value of data mining for school personnel, finance departments, teacher evaluations, and in the classroom. It suggests that much could be learned about district practices if one…
Multi-user data acquisition environment

International Nuclear Information System (INIS)

Storch, N.A.

1983-01-01

The typical data acquisition environment involves data collection and monitoring by a single user. However, in order to support experiments on the Mars facility at Lawrence Livermore National Laboratory, we have had to create a multi-user data acquisition environment where any user can control the data acquisition and several users can monitor and analyze data being collected in real time. This paper describes how we accomplished this on an HP A600 computer. It focuses on the overall system description and user communication with the tasks within the system. Our current implementation is one phase of a long-term software development project
Data acquisition and analysis at the Structural Biology Center

International Nuclear Information System (INIS)

Westbrook, M.L.; Coleman, T.A.; Daly, R.T.; Pflugrath, J.W.

1996-01-01

The Structural Biology Center (SBC), a national user facility for macromolecular crystallography located at Argonne National Laboratory's Advanced Photon Source, is currently being built and commissioned. SBC facilities include a bending-magnet beamline, an insertion-device beamline, laboratory and office space adjacent to the beamlines, and associated instrumentation, experimental apparatus, and facilities. SBC technical facilities will support anomalous dispersion phasing experiments, data collection from microcrystals, data collection from crystals with large molecular structures and rapid data collection from multiple related crystal structures for protein engineering and drug design. The SBC Computing Systems and Software Engineering Group is tasked with developing the SBC Control System, which includes computing systems, network, and software. The emphasis of SBC Control System development has been to provide efficient and convenient beamline control, data acquisition, and data analysis for maximal facility and experimenter productivity. This paper describes the SBC Control System development, specifically data acquisition and analysis at the SBC, and the development methods used to meet this goal

Exploring the Integration of Data Mining and Data Visualization

Science.gov (United States)

Zhang, Yi

2011-01-01

Due to the rapid advances in computing and sensing technologies, enormous amounts of data are being generated everyday in various applications. The integration of data mining and data visualization has been widely used to analyze these massive and complex data sets to discover hidden patterns. For both data mining and visualization to be…
Software Switching for High Throughput Data Acquisition Networks

CERN Document Server

AUTHOR|(CDS)2089787; Lehmann Miotto, Giovanna

The bursty many-to-one communication pattern, typical for data acquisition systems, is particularly demanding for commodity TCP/IP and Ethernet technologies. The problem arising from this pattern is widely known in the literature as \\emph{incast} and can be observed as TCP throughput collapse. It is a result of overloading the switch buffers, when a specific node in a network requests data from multiple sources. This will become even more demanding for future upgrades of the experiments at the Large Hadron Collider at CERN. It is questionable whether commodity TCP/IP and Ethernet technologies in their current form will be still able to effectively adapt to bursty traffic without losing packets due to the scarcity of buffers in the networking hardware. This thesis provides an analysis of TCP/IP performance in data acquisition networks and presents a novel approach to incast congestion in these networks based on software-based packet forwarding. Our first contribution lies in confirming the strong analogies bet...
Data Mining and Privacy of Social Network Sites' Users: Implications of the Data Mining Problem.

Science.gov (United States)

Al-Saggaf, Yeslam; Islam, Md Zahidul

2015-08-01

This paper explores the potential of data mining as a technique that could be used by malicious data miners to threaten the privacy of social network sites (SNS) users. It applies a data mining algorithm to a real dataset to provide empirically-based evidence of the ease with which characteristics about the SNS users can be discovered and used in a way that could invade their privacy. One major contribution of this article is the use of the decision forest data mining algorithm (SysFor) to the context of SNS, which does not only build a decision tree but rather a forest allowing the exploration of more logic rules from a dataset. One logic rule that SysFor built in this study, for example, revealed that anyone having a profile picture showing just the face or a picture showing a family is less likely to be lonely. Another contribution of this article is the discussion of the implications of the data mining problem for governments, businesses, developers and the SNS users themselves.
Analysis of the planned post-mining landscape of MIBRAG's open-cast mines with regard to a possible environmental impact of alteration processes in mixed dumps

Energy Technology Data Exchange (ETDEWEB)

Jolas, P.; Hofmann, B. [Mitteldeutsche Braunkohlengesellschaft, Theissen (Germany)

2010-07-01

There has been an increasing body of knowledge with regard to hydro- and geochemical alteration processes in overburden dumps and their impact on groundwater quality in lignite mining and reclamation operations associated with post-mining landscapes in Germany. The operators of the MIBRAG mines have examined issues regarding alteration processes and how they affect the environment and which opportunities exist to actively influence the dumping process. The objectives were to counteract any possible negative impact of the alteration processes. Special emphasis was on the impact caused by oxidation of sulfur containing minerals. This paper presented an analysis of the situation at United Schleenhain Mine and how it reflects on the work to date for MIBRAG's mines. A future outlook was also presented. Specifically, the paper discussed the development of the United Schleenhain mine and the post-mining landscape. The potential for discharge of substances was also evaluated along with acidification. 1 tab., 5 figs.
Data mining in e-commerce: A survey

Indian Academy of Sciences (India)

R. Narasimhan (Krishtel eMaging) 1461 1996 Oct 15 13:05:22

it is only apposite to seek the services of data mining to make (business) sense out of these data sets. Data mining ..... for the simple reason that for practical purposes, it is sufficient to include snapshots of data taken at say, weekly ..... of the mining environment and the expenses the user is willing to incur). The authors have.
Ensemble Data Mining Methods

Science.gov (United States)

Oza, Nikunj C.

2004-01-01

Ensemble Data Mining Methods, also known as Committee Methods or Model Combiners, are machine learning methods that leverage the power of multiple models to achieve better prediction accuracy than any of the individual models could on their own. The basic goal when designing an ensemble is the same as when establishing a committee of people: each member of the committee should be as competent as possible, but the members should be complementary to one another. If the members are not complementary, Le., if they always agree, then the committee is unnecessary---any one member is sufficient. If the members are complementary, then when one or a few members make an error, the probability is high that the remaining members can correct this error. Research in ensemble methods has largely revolved around designing ensembles consisting of competent yet complementary models.
Data Mining Mining Data: MSHA Enforcement Efforts, Underground Coal Mine Safety, and New Health Policy Implications

OpenAIRE

Thomas J. Kniesner; John D. Leeth

2003-01-01

Studies of industrial safety regulations, Occupational Safety and Health Administration (OSHA) in particular, often find little effect on worker safety. Critics of the regulatory approach argue that safety standards have little to do with industrial injuries and defenders of the regulatory approach cite infrequent inspections and low fines for violating safety standards. We use recently assembled data from the Mine Safety and Health Administration (MSHA) concerning underground coal mine produ...
Earthworms drive succession of both plant and Collembola communities in post-mining sites

Science.gov (United States)

Mudrák, Ondřej; Uteseny, Karoline; Frouz, Jan

2016-04-01

Previous field observations indicated that earthworms promote late-successional plant species and reduce collembolan numbers at post-mining sites in the Sokolov coal mining district (Czech Republic). Here, we established a laboratory pot experiment to test the effect of earthworms (Aporrectodea caliginosa Savigny and Lumbricus rubellus Hoffm.) and litter of low, medium, and high quality (the grass Calamagrostis epigejos, the willow Salix caprea, and the alder Alnus glutinosa, respectively) on late successional plants (grasses Arrhenatherum elatius and Agrostis capillaris, legumes Lotus corniculatus and Trifolium medium, and non-leguminous dicots Centaurea jacea and Plantago lanceolata) in spoil substrate originating from Sokolov post-mining sites and naturally inhabited by abundant numbers of Collembola. The earthworms increased plant biomass, especially that of the large-seeded A. elatius, but reduced the number of plant individuals, mainly that of the small-seeded A. capillaris and both legumes. Litter quality affected plant biomass, which was highest with S. caprea litter, but did not change the number of plant individuals. Litter quality did not modify the effect of earthworms on plants; the effect of litter quality and earthworms was only additive. Species composition of Collembola community was altered by litter quality, but earthworms reduced the number of individuals, increased the number of species, and increased species evenness consistently across the litter qualities. Because the results of this experiment were consistent with the field observations, we conclude that earthworms help drive succession of both plant and Collembola communities on post-mining sites.
Site-specific climate analysis elucidates revegetation challenges for post-mining landscapes in eastern Australia

Directory of Open Access Journals (Sweden)

P. Audet

2013-10-01

Full Text Available In eastern Australia, the availability of water is critical for the successful rehabilitation of post-mining landscapes and climatic characteristics of this diverse geographical region are closely defined by factors such as erratic rainfall and periods of drought and flooding. Despite this, specific metrics of climate patterning are seldom incorporated into the initial design of current post-mining land rehabilitation strategies. Our study proposes that a few common rainfall parameters can be combined and rated using arbitrary rainfall thresholds to characterise bioregional climate sensitivity relevant to the rehabilitation these landscapes. This approach included assessments of annual rainfall depth, average recurrence interval of prolonged low intensity rainfall, average recurrence intervals of short or prolonged high intensity events, median period without rain (or water-deficit and standard deviation for this period in order to address climatic factors such as total water availability, seasonality and intensity – which were selected as potential proxies of both short- and long-term biological sensitivity to climate within the context of post-disturbance ecological development and recovery. Following our survey of available climate data, we derived site "climate sensitivity" indexes and compared the performance of 9 ongoing mine sites: Weipa, Mt. Isa and Cloncurry, Eromanga, Kidston, the Bowen Basin (Curragh, Tarong, North Stradbroke Island, and the Newnes Plateau. The sites were then ranked from most-to-least sensitive and compared with natural bioregional patterns of vegetation density using mean NDVI. It was determined that regular rainfall and relatively short periods of water-deficit were key characteristics of sites having less sensitivity to climate – as found among the relatively more temperate inland mining locations. Whereas, high rainfall variability, frequently occurring high intensity events, and (or prolonged seasonal
Site-specific climate analysis elucidates revegetation challenges for post-mining landscapes in eastern Australia

Science.gov (United States)

Audet, P.; Arnold, S.; Lechner, A. M.; Baumgartl, T.

2013-10-01

In eastern Australia, the availability of water is critical for the successful rehabilitation of post-mining landscapes and climatic characteristics of this diverse geographical region are closely defined by factors such as erratic rainfall and periods of drought and flooding. Despite this, specific metrics of climate patterning are seldom incorporated into the initial design of current post-mining land rehabilitation strategies. Our study proposes that a few common rainfall parameters can be combined and rated using arbitrary rainfall thresholds to characterise bioregional climate sensitivity relevant to the rehabilitation these landscapes. This approach included assessments of annual rainfall depth, average recurrence interval of prolonged low intensity rainfall, average recurrence intervals of short or prolonged high intensity events, median period without rain (or water-deficit) and standard deviation for this period in order to address climatic factors such as total water availability, seasonality and intensity - which were selected as potential proxies of both short- and long-term biological sensitivity to climate within the context of post-disturbance ecological development and recovery. Following our survey of available climate data, we derived site "climate sensitivity" indexes and compared the performance of 9 ongoing mine sites: Weipa, Mt. Isa and Cloncurry, Eromanga, Kidston, the Bowen Basin (Curragh), Tarong, North Stradbroke Island, and the Newnes Plateau. The sites were then ranked from most-to-least sensitive and compared with natural bioregional patterns of vegetation density using mean NDVI. It was determined that regular rainfall and relatively short periods of water-deficit were key characteristics of sites having less sensitivity to climate - as found among the relatively more temperate inland mining locations. Whereas, high rainfall variability, frequently occurring high intensity events, and (or) prolonged seasonal drought were primary
Program design of data acquisition in Windows

International Nuclear Information System (INIS)

Cai Jianxin; Yan Huawen

2004-01-01

Several methods for the design of data acquisition program based on Microsoft Windows are introduced. Then their respective advantages and disadvantages are totally analyzed. At the same time, the data acquisition modes applicable to each method are also pointed out. It is convenient for data acquisition programmers to develop data acquisition systems. (authors)
Educational data mining and learning analytics

OpenAIRE

Vera Hernández, Joan Carles

2017-01-01

Treball basat en Educational Data Mining & Learning Analitics d'anàlisi de la matriculació dels alumnes i el seu impacte sobre la decisió de tornar-se a matricular. Trabajo basado en Educational Data Mining & Learning Analytics análisis de la matriculación de los alumnos y su impacto sobre la decisión de volverse a matricular. Work based on Educational Data Mining & Learning Analytics analysis of student enrollment and its impact on the decision to re-enroll.
Cognition and Knowledge Sharing in Post-acquisition Integration

DEFF Research Database (Denmark)

Jaura, Manya; Michailova, Snejina

2014-01-01

conducted with ten respondents in four Indian IT companies that have acquired firms abroad. Findings: The authors find evidence for supporting the negative effect of in- and out-groups differentiation and the positive effect of interpersonal interaction on knowledge sharing among employees of the acquired...... of organisational objectives in a post-acquisition context. Managers should understand that the knowledge their employees possess is a strategic asset, and therefore how they use it is influential in attaining organisational goals in general, and acquisition integration objectives in particular. The creation...... of task- and project-related communities or groups can help in establishing a shared organisational identity, especially after the turbulent event of one company acquiring another one. The creation of communities or groups where socialisation is encouraged can lead to employees interacting with one...
MouseMine: a new data warehouse for MGI.

Science.gov (United States)

Motenko, H; Neuhauser, S B; O'Keefe, M; Richardson, J E

2015-08-01

MouseMine (www.mousemine.org) is a new data warehouse for accessing mouse data from Mouse Genome Informatics (MGI). Based on the InterMine software framework, MouseMine supports powerful query, reporting, and analysis capabilities, the ability to save and combine results from different queries, easy integration into larger workflows, and a comprehensive Web Services layer. Through MouseMine, users can access a significant portion of MGI data in new and useful ways. Importantly, MouseMine is also a member of a growing community of online data resources based on InterMine, including those established by other model organism databases. Adopting common interfaces and collaborating on data representation standards are critical to fostering cross-species data analysis. This paper presents a general introduction to MouseMine, presents examples of its use, and discusses the potential for further integration into the MGI interface.
Development and Integration of a Data Acquisition System for SST-1 Phase-1 Plasma Diagnostics

International Nuclear Information System (INIS)

Srivastava, Amit K; Sharma, Manika; Mansuri, Imran; Sharma, Atish; Raval, Tushar; Pradhan, Subrata

2012-01-01

Long pulse (of the order of 1000 s or more) SST-1 tokamak experiments demand a data acquisition system that is capable of acquiring data from various diagnostics channels without losing useful data (and hence physics information) while avoiding unnecessary generation of a large volume data. SST-1 Phase-1 tokamak operation has been envisaged with data acquisition of several essential diagnostics channels. These channels demand data acquisition at a sampling rate ranging from 1 kilo samples per second (KSPS) to 1 mega samples per second (MSPS). Considering the technical characteristics and requirements of the diagnostics, a data acquisition system based on PXI and CAMAC has been developed for SST-1 plasma diagnostics. Both these data acquisition systems are scalable. Present data acquisition needs involving slow plasma diagnostics are catered by the PXI based data acquisition system. On the other hand, CAMAC data acquisition hardware meets all requirements of the SST-1 Phase-1 fast plasma diagnostics channels. A graphical user interface for both data acquisition systems (PXI and CAMAC) has been developed using LabVIEW application development software. The collected data on the local hard disk are directly streaming to the central server through a dedicated network for post-shot data analysis. This paper describes the development and integration of the data acquisition system for SST-1 Phase-1 plasma diagnostics. The integrated testing of the developed data acquisition system has been performed using SST-1 central control and diagnostics signal conditioning units. In the absence of plasma shots, the integrated testing of the data acquisition system for the initial diagnostics of SST-1 Phase-1 operation has been performed with simulated physical signals. The primary engineering objective of this integrated testing is to validate the performance of the developed data acquisition system under simulated conditions close to that of actual tokamak operation. The data
Seismic data acquisition systems

International Nuclear Information System (INIS)

Kolvankar, V.G.; Nadre, V.N.; Rao, D.S.

1989-01-01

Details of seismic data acquisition systems developed at the Bhabha Atomic Research Centre, Bombay are reported. The seismic signals acquired belong to different signal bandwidths in the band from 0.02 Hz to 250 Hz. All these acquisition systems are built around a unique technique of recording multichannel data on to a single track of an audio tape and in digital form. Techniques of how these signals in different bands of frequencies were acquired and recorded are described. Method of detecting seismic signals and its performance is also discussed. Seismic signals acquired in different set-ups are illustrated. Time indexing systems for different set-ups and multichannel waveform display systems which form essential part of the data acquisition systems are also discussed. (author). 13 refs., 6 figs., 1 tab
A survey of temporal data mining

Indian Academy of Sciences (India)

Data mining is concerned with analysing large volumes of (often unstructured) data to automatically discover interesting regularities or relationships which in turn lead to better understanding of the underlying processes. The ﬁeld of temporal data mining is concerned with such analysis in the case of ordered data streams ...
30 CFR 879.12 - Procedures for acquisition.

Science.gov (United States)

2010-07-01

....12 Mineral Resources OFFICE OF SURFACE MINING RECLAMATION AND ENFORCEMENT, DEPARTMENT OF THE INTERIOR ABANDONED MINE LAND RECLAMATION ACQUISITION, MANAGEMENT, AND DISPOSITION OF LANDS AND WATER § 879.12... adversely affected by past mining. (b) When practical, acquisition shall be by purchase from a willing...
Data mining methods and applications

CERN Document Server

Lawrence, Kenneth D; Klimberg, Ronald K

2007-01-01

With today's information explosion, many organizations are now able to access a wealth of valuable data. Unfortunately, most of these organizations find they are ill-equipped to organize this information, let alone put it to work for them. Gain a Competitive Advantage Employ data mining in research and forecasting Build models with data management tools and methodology optimization Gain sophisticated breakdowns and complex analysis through multivariate, evolutionary, and neural net methodsLearn how to classify data and maintain qualityTransform Data into Business Acumen Data Mining Methods and
Software upgradation of PXI based data acquisition for Aditya experiments

International Nuclear Information System (INIS)

Panchal, Vipul K.; Chavda, Chhaya; Patel, Vijay; Patel, Narendra; Ghosh, Joydeep

2015-01-01

Aditya Data Acquisition and Control System is designed to acquire data from diagnostics like Loop Voltage, Rogowski, Magnetic probes, X-rays etc and for control of gas feed, gate valve control, trigger pulse generation etc. CAMAC based data acquisition system was updated with PXI based Multifunction modules. The System is interfaced using optical connectivity with PC using PCI based controller module. Data is acquired using LabVIEW graphical user interface (GUI) and stored in server. The present GUI based application does not have features like module parameters configuration, analysis, webcasting etc. So a new application software using LabVIEW is being developed with features for individual module support considering programmable channel configuration - sampling rate, number of pre and post trigger samples, number of active channel selection etc. It would also have facility of using multi-functionality of timer and counter. The software would be scalable considering more modules, channels and crates along with security of different access level of user privileges. (author)

The Strengthening of Geological Infrastructure, Research and Data Acquisition - Using Gis in Ivory Coast Gold Mines

Directory of Open Access Journals (Sweden)

Kouame Kouame Joseph Arthur

2017-01-01

Full Text Available The artisanal gold mining in Ivory Coast has become a key problem in the mining sector. A diverse group of people in Ivory Coast, including the young and the old, are all engaged in these activities that are reportedly better than agricultural inputs. However, it is still a high-risk activity that leads to pollution, environmental degradation and the loss of human life. About ten people die each year in the gold mines. This paper focuses on gold mine safety by using the Geographic Information System (GIS as a major solution to solve the artisanal gold mines problem, and also seeks to promote the mining industry in Ivory Coast.
Utilizing coal remaining resources and post-mining land use planning based on GIS-based optimization method : study case at PT Adaro coal mine in South Kalimantan

Directory of Open Access Journals (Sweden)

Mohamad Anis

2017-06-01

Full Text Available Coal mining activities may cause a series of environmental and socio-economic issues in communities around the mining area. Mining can become an obstacle to environmental sustainability and a major hidden danger to the security of the local ecology. Therefore, the coal mining industry should follow some specific principles and factors in achieving sustainable development. These factors include geological conditions, land use, mining technology, environmental sustainability policies and government regulations, socio-economic factors, as well as sustainability optimization for post-mining land use. Resources of the remains of the coal which is defined as the last remaining condition of the resources and reserves of coal when the coal companies have already completed the life of the mine or the expiration of the licensing contract (in accordance with government permission. This research uses approch of knowledge-driven GIS based methods mainly Analytical Hierarchy Process (AHP and Fuzzy logic for utilizing coal remaining resources and post-mining land use planning. The mining area selected for this study belongs to a PKP2B (Work Agreement for Coal Mining company named Adaro Indonesia (PT Adaro. The result shows that geologically the existing formation is dominated by Coal Bearing Formation (Warukin Formation which allows the presence of remains coal resource potential after the lifetime of mine, and the suitability of rubber plantation for the optimization of land use in all mining sites and also in some disposal places in conservation areas and protected forests.
Virtual Observatories, Data Mining, and Astroinformatics

Science.gov (United States)

Borne, Kirk

The historical, current, and future trends in knowledge discovery from data in astronomy are presented here. The story begins with a brief history of data gathering and data organization. A description of the development ofnew information science technologies for astronomical discovery is then presented. Among these are e-Science and the virtual observatory, with its data discovery, access, display, and integration protocols; astroinformatics and data mining for exploratory data analysis, information extraction, and knowledge discovery from distributed data collections; new sky surveys' databases, including rich multivariate observational parameter sets for large numbers of objects; and the emerging discipline of data-oriented astronomical research, called astroinformatics. Astroinformatics is described as the fourth paradigm of astronomical research, following the three traditional research methodologies: observation, theory, and computation/modeling. Astroinformatics research areas include machine learning, data mining, visualization, statistics, semantic science, and scientific data management.Each of these areas is now an active research discipline, with significantscience-enabling applications in astronomy. Research challenges and sample research scenarios are presented in these areas, in addition to sample algorithms for data-oriented research. These information science technologies enable scientific knowledge discovery from the increasingly large and complex data collections in astronomy. The education and training of the modern astronomy student must consequently include skill development in these areas, whose practitioners have traditionally been limited to applied mathematicians, computer scientists, and statisticians. Modern astronomical researchers must cross these traditional discipline boundaries, thereby borrowing the best of breed methodologies from multiple disciplines. In the era of large sky surveys and numerous large telescopes, the potential
Data Mining for Intrusion Detection

Science.gov (United States)

Singhal, Anoop; Jajodia, Sushil

Data Mining Techniques have been successfully applied in many different fields including marketing, manufacturing, fraud detection and network management. Over the past years there is a lot of interest in security technologies such as intrusion detection, cryptography, authentication and firewalls. This chapter discusses the application of Data Mining techniques to computer security. Conclusions are drawn and directions for future research are suggested.
Automated system for data acquisition and monitoring

Directory of Open Access Journals (Sweden)

Borza Sorin

2017-01-01

Full Text Available The Environmental management has become, with the development of human society a very important issue. There have been multiple systems that automatically monitors the environment. In this paper we propose a system that integrates GIS software and data acquisition software. In addition the proposed system implements new AHP multicriteria method that can get an answer online on each pollutant influence on limited geographical area in which the monitors. Factors pollutants of limited geographical areas are taken automatically by specific sensors through acquisition board. Labview software, with virtual instrument created by transferring them into a database Access. Access database they are taken up by software Geomedia Professional and processed using multi-criteria method AHP, so that at any moment, their influence on the environment and classify these influences, can be plotted on the screen monitoring system. The system allows, the automatic collection of data, the memorization and the generation of GIS elements. The research presented in this paper were aimed at implementing multi-criteria methods in GIS software.
Data Mining Tools for Malware Detection

CERN Document Server

Masud, Mehedy; Thuraisingham, Bhavani; Andreasson, Kim J

2011-01-01

Although the use of data mining for security and malware detection is quickly on the rise, most books on the subject provide high-level theoretical discussions to the near exclusion of the practical aspects. Breaking the mold, Data Mining Tools for Malware Detection provides a step-by-step breakdown of how to develop data mining tools for malware detection. Integrating theory with practical techniques and experimental results, it focuses on malware detection applications for email worms, malicious code, remote exploits, and botnets. The authors describe the systems they have designed and devel
A survey of temporal data mining

Indian Academy of Sciences (India)

other subtle relationships in the data using a combination of techniques from ... stamped list of items bought by customers lends itself to data mining analysis that ...... Frequent episode mining can be used here as part of an alarm management.
IT Data Mining Tool Uses in Aerospace

Science.gov (United States)

Monroe, Gilena A.; Freeman, Kenneth; Jones, Kevin L.

2012-01-01

Data mining has a broad spectrum of uses throughout the realms of aerospace and information technology. Each of these areas has useful methods for processing, distributing, and storing its corresponding data. This paper focuses on ways to leverage the data mining tools and resources used in NASA's information technology area to meet the similar data mining needs of aviation and aerospace domains. This paper details the searching, alerting, reporting, and application functionalities of the Splunk system, used by NASA's Security Operations Center (SOC), and their potential shared solutions to address aircraft and spacecraft flight and ground systems data mining requirements. This paper also touches on capacity and security requirements when addressing sizeable amounts of data across a large data infrastructure.
Integration of Multiple Genomic and Phenotype Data to Infer Novel miRNA-Disease Associations.

Science.gov (United States)

Shi, Hongbo; Zhang, Guangde; Zhou, Meng; Cheng, Liang; Yang, Haixiu; Wang, Jing; Sun, Jie; Wang, Zhenzhen

2016-01-01

MicroRNAs (miRNAs) play an important role in the development and progression of human diseases. The identification of disease-associated miRNAs will be helpful for understanding the molecular mechanisms of diseases at the post-transcriptional level. Based on different types of genomic data sources, computational methods for miRNA-disease association prediction have been proposed. However, individual source of genomic data tends to be incomplete and noisy; therefore, the integration of various types of genomic data for inferring reliable miRNA-disease associations is urgently needed. In this study, we present a computational framework, CHNmiRD, for identifying miRNA-disease associations by integrating multiple genomic and phenotype data, including protein-protein interaction data, gene ontology data, experimentally verified miRNA-target relationships, disease phenotype information and known miRNA-disease connections. The performance of CHNmiRD was evaluated by experimentally verified miRNA-disease associations, which achieved an area under the ROC curve (AUC) of 0.834 for 5-fold cross-validation. In particular, CHNmiRD displayed excellent performance for diseases without any known related miRNAs. The results of case studies for three human diseases (glioblastoma, myocardial infarction and type 1 diabetes) showed that all of the top 10 ranked miRNAs having no known associations with these three diseases in existing miRNA-disease databases were directly or indirectly confirmed by our latest literature mining. All these results demonstrated the reliability and efficiency of CHNmiRD, and it is anticipated that CHNmiRD will serve as a powerful bioinformatics method for mining novel disease-related miRNAs and providing a new perspective into molecular mechanisms underlying human diseases at the post-transcriptional level. CHNmiRD is freely available at http://www.bio-bigdata.com/CHNmiRD.
Integration of Multiple Genomic and Phenotype Data to Infer Novel miRNA-Disease Associations.

Directory of Open Access Journals (Sweden)

Hongbo Shi

Full Text Available MicroRNAs (miRNAs play an important role in the development and progression of human diseases. The identification of disease-associated miRNAs will be helpful for understanding the molecular mechanisms of diseases at the post-transcriptional level. Based on different types of genomic data sources, computational methods for miRNA-disease association prediction have been proposed. However, individual source of genomic data tends to be incomplete and noisy; therefore, the integration of various types of genomic data for inferring reliable miRNA-disease associations is urgently needed. In this study, we present a computational framework, CHNmiRD, for identifying miRNA-disease associations by integrating multiple genomic and phenotype data, including protein-protein interaction data, gene ontology data, experimentally verified miRNA-target relationships, disease phenotype information and known miRNA-disease connections. The performance of CHNmiRD was evaluated by experimentally verified miRNA-disease associations, which achieved an area under the ROC curve (AUC of 0.834 for 5-fold cross-validation. In particular, CHNmiRD displayed excellent performance for diseases without any known related miRNAs. The results of case studies for three human diseases (glioblastoma, myocardial infarction and type 1 diabetes showed that all of the top 10 ranked miRNAs having no known associations with these three diseases in existing miRNA-disease databases were directly or indirectly confirmed by our latest literature mining. All these results demonstrated the reliability and efficiency of CHNmiRD, and it is anticipated that CHNmiRD will serve as a powerful bioinformatics method for mining novel disease-related miRNAs and providing a new perspective into molecular mechanisms underlying human diseases at the post-transcriptional level. CHNmiRD is freely available at http://www.bio-bigdata.com/CHNmiRD.
toxoMine: an integrated omics data warehouse for Toxoplasma gondii systems biology research.

Science.gov (United States)

Rhee, David B; Croken, Matthew McKnight; Shieh, Kevin R; Sullivan, Julie; Micklem, Gos; Kim, Kami; Golden, Aaron

2015-01-01

Toxoplasma gondii (T. gondii) is an obligate intracellular parasite that must monitor for changes in the host environment and respond accordingly; however, it is still not fully known which genetic or epigenetic factors are involved in regulating virulence traits of T. gondii. There are on-going efforts to elucidate the mechanisms regulating the stage transition process via the application of high-throughput epigenomics, genomics and proteomics techniques. Given the range of experimental conditions and the typical yield from such high-throughput techniques, a new challenge arises: how to effectively collect, organize and disseminate the generated data for subsequent data analysis. Here, we describe toxoMine, which provides a powerful interface to support sophisticated integrative exploration of high-throughput experimental data and metadata, providing researchers with a more tractable means toward understanding how genetic and/or epigenetic factors play a coordinated role in determining pathogenicity of T. gondii. As a data warehouse, toxoMine allows integration of high-throughput data sets with public T. gondii data. toxoMine is also able to execute complex queries involving multiple data sets with straightforward user interaction. Furthermore, toxoMine allows users to define their own parameters during the search process that gives users near-limitless search and query capabilities. The interoperability feature also allows users to query and examine data available in other InterMine systems, which would effectively augment the search scope beyond what is available to toxoMine. toxoMine complements the major community database ToxoDB by providing a data warehouse that enables more extensive integrative studies for T. gondii. Given all these factors, we believe it will become an indispensable resource to the greater infectious disease research community. © The Author(s) 2015. Published by Oxford University Press.
Temporal trends in erosion and hydrology for a post-mining landform at Ranger mine, Northern Territory. Supervising Scientist report 165

International Nuclear Information System (INIS)

Moliere, D.R.; Evans, K.G.; Saynor, M.J.; Willgoose, G.R.

2002-01-01

An important part of rehabilitation planning for mines is the design of a stable landform for waste rock dumps or spoil piles, at the completion of mining, which minimise erosion and environmental impact offsite. To successfully incorporate landform designs in planning, there is a need to be able to predict the surface stability of the final landform using erosion and landform evolution modelling techniques. In the long term, weathering, soil forming processes, ecosystem development and even climate change may affect the surface characteristics, and hence the stability, of the rehabilitated landform. In this study, changes to the surface characteristics of a landform in time can be quantified in terms of erosion parameters. Since a prediction of the stability of the rehabilitated landform is required over the long term, temporal changes in these erosion parameters are incorporated into landform evolution modelling of a post-mining landform. The landform evolution model SIBERIA was used to predict the stability of the proposed rehabilitated landform at Ranger Mine, Northern Territory. The data collection sites were considered to be representative of the hydrology and erosion characteristics that would exist on the WRD at Ranger at various stages after rehabilitation. This study uses measured site data from landforms with hydrology and erosion properties similar to those likely to develop on Ranger at various times after rehabilitation to assess the effect of temporal change on landform evolution model input parameters. Section 2 documents the process of SIBERIA input parameter derivation and landform evolution modelling using collected site rainfall, runoff and sediment loss data. This section is based on the detailed descriptions of the process given in Willgoose and Riley (1998) and Evans et al( 1998). In section 3, monitoring data, collected from sites with properties similar to those likely to develop on the proposed above-grade landform at Ranger at various
Text mining of web-based medical content

CERN Document Server

Neustein, Amy

2014-01-01

Text Mining of Web-Based Medical Content examines web mining for extracting useful information that can be used for treating and monitoring the healthcare of patients. This work provides methodological approaches to designing mapping tools that exploit data found in social media postings. Specific linguistic features of medical postings are analyzed vis-a-vis available data extraction tools for culling useful information.
The Evaluation on Data Mining Methods of Horizontal Bar Training Based on BP Neural Network

Directory of Open Access Journals (Sweden)

Zhang Yanhui

2015-01-01

Full Text Available With the rapid development of science and technology, data analysis has become an indispensable part of people’s work and life. Horizontal bar training has multiple categories. It is an emphasis for the re-search of related workers that categories of the training and match should be reduced. The application of data mining methods is discussed based on the problem of reducing categories of horizontal bar training. The BP neural network is applied to the cluster analysis and the principal component analysis, which are used to evaluate horizontal bar training. Two kinds of data mining methods are analyzed from two aspects, namely the operational convenience of data mining and the rationality of results. It turns out that the principal component analysis is more suitable for data processing of horizontal bar training.
Applications of Data Mining in Higher Education

OpenAIRE

Monika Goyal; Rajan Vohra

2012-01-01

Data analysis plays an important role for decision support irrespective of type of industry like any manufacturing unit and educations system. There are many domains in which data mining techniques plays an important role. This paper proposes the use of data mining techniques to improve the efficiency of higher education institution. If data mining techniques such as clustering, decision tree and association are applied to higher education processes, it would help to improve students performa...
Comparative analysis of data mining techniques for business data

Science.gov (United States)

Jamil, Jastini Mohd; Shaharanee, Izwan Nizal Mohd

2014-12-01

Data mining is the process of employing one or more computer learning techniques to automatically analyze and extract knowledge from data contained within a database. Companies are using this tool to further understand their customers, to design targeted sales and marketing campaigns, to predict what product customers will buy and the frequency of purchase, and to spot trends in customer preferences that can lead to new product development. In this paper, we conduct a systematic approach to explore several of data mining techniques in business application. The experimental result reveals that all data mining techniques accomplish their goals perfectly, but each of the technique has its own characteristics and specification that demonstrate their accuracy, proficiency and preference.
Software tool for data mining and its applications

Science.gov (United States)

Yang, Jie; Ye, Chenzhou; Chen, Nianyi

2002-03-01

A software tool for data mining is introduced, which integrates pattern recognition (PCA, Fisher, clustering, hyperenvelop, regression), artificial intelligence (knowledge representation, decision trees), statistical learning (rough set, support vector machine), computational intelligence (neural network, genetic algorithm, fuzzy systems). It consists of nine function models: pattern recognition, decision trees, association rule, fuzzy rule, neural network, genetic algorithm, Hyper Envelop, support vector machine, visualization. The principle and knowledge representation of some function models of data mining are described. The software tool of data mining is realized by Visual C++ under Windows 2000. Nonmonotony in data mining is dealt with by concept hierarchy and layered mining. The software tool of data mining has satisfactorily applied in the prediction of regularities of the formation of ternary intermetallic compounds in alloy systems, and diagnosis of brain glioma.
Development of a Data Acquisition System for the BaBar CP Violation Experiment

International Nuclear Information System (INIS)

Claus, Richard

1999-01-01

Experiences developing data acquisition system for the BaBar CP violation experiment located at the Stanford Linear Accelerator Center are presented. The BaBar detector consists of multiple independent subdetectors joined with a data acquisition system consisting of a large number of embedded PowerPC single board computers residing in VME crates. The data acquisition software is layered on the VxWorks real-time operating system. It is partitionable to allow subsystems (as well as test stands) to operate independently. Data is assimilated into events through a combination of shared memory and a high performance network. This system presents data to a UNIX farm via a high speed non-blocking ethernet switch at a rate of 2 KHz. Topics such as bootstrapping and loading 200 processors, NFS file access for these processors and software development and deployment are discussed
Development of a Data Acquisition System for the BaBar CP Violation Experiment

CERN Document Server

Scott, I; Grosso, P; Hamilton, R T; Huffer, M E; O'Grady, C P; Russell, J J

1999-01-01

Experiences developing data acquisition system for the BaBar CP violation experiment located at the Stanford Linear Accelerator Center are presented. The BaBar detector consists of multiple independent subdetectors joined with a data acquisition system consisting of a large number of embedded PowerPC single board computers residing in VME crates. The data acquisition software is layered on the VxWorks real-time operating system. It is partitionable to allow subsystems (as well as test stands) to operate independently. Data is assimilated into events through a combination of shared memory and a high performance network. This system presents data to a UNIX farm via a high speed non-blocking ethernet switch at a rate of 2 KHz. Topics such as bootstrapping and loading 200 processors, NFS file access for these processors and software development and deployment are discussed.
Utility Independent Privacy Preserving Data Mining - Horizontally Partitioned Data

Directory of Open Access Journals (Sweden)

E Poovammal

2010-06-01

Full Text Available Micro data is a valuable source of information for research. However, publishing data about individuals for research purposes, without revealing sensitive information, is an important problem. The main objective of privacy preserving data mining algorithms is to obtain accurate results/rules by analyzing the maximum possible amount of data without unintended information disclosure. Data sets for analysis may be in a centralized server or in a distributed environment. In a distributed environment, the data may be horizontally or vertically partitioned. We have developed a simple technique by which horizontally partitioned data can be used for any type of mining task without information loss. The partitioned sensitive data at 'm' different sites are transformed using a mapping table or graded grouping technique, depending on the data type. This transformed data set is given to a third party for analysis. This may not be a trusted party, but it is still allowed to perform mining operations on the data set and to release the results to all the 'm' parties. The results are interpreted among the 'm' parties involved in the data sharing. The experiments conducted on real data sets prove that our proposed simple transformation procedure preserves one hundred percent of the performance of any data mining algorithm as compared to the original data set while preserving privacy.

Deep Trek Re-configurable Processor for Data Acquisition (RPDA)

Energy Technology Data Exchange (ETDEWEB)

Bruce Ohme; Michael Johnson

2009-06-30

This report summarizes technical progress achieved during the cooperative research agreement between Honeywell and U.S. Department of Energy to develop a high-temperature Re-configurable Processor for Data Acquisition (RPDA). The RPDA development has incorporated multiple high-temperature (225C) electronic components within a compact co-fired ceramic Multi-Chip-Module (MCM) package. This assembly is suitable for use in down-hole oil and gas applications. The RPDA module is programmable to support a wide range of functionality. Specifically this project has demonstrated functional integrity of the RPDA package and internal components, as well as functional integrity of the RPDA configured to operate as a Multi-Channel Data Acquisition Controller. This report reviews the design considerations, electrical hardware design, MCM package design, considerations for manufacturing assembly, test and screening, and results from prototype assembly and characterization testing.
Assessment of the suitability of trees for brownfields reuse in the post-mining landscape

Science.gov (United States)

Mec, J.; Lokajickova, B.; Sotkova, N.; Svehlakova, H.; Stalmachova, B.

2017-10-01

The post-mining landscape of Upper Silesian is deterioration of the original landscape caused by underground coal mining. There are huge ecosystems changes, which have been reclaimed by nature-friendly procedures. The aim of the work is to evaluate the suitability of selected trees for reuse of brownfields in this landscape and proposals for reclamation in the interest areas of Upper Silesian.
Soil microfungi in two post-mining chronosequences with different vegetation types

Czech Academy of Sciences Publication Activity Database

Nováková, Alena

2001-01-01

Roč. 9, č. 4 (2001), s. 351-358 ISSN 1061-2971 R&D Projects: GA MŠk ME 076 Institutional research plan: CEZ:AV0Z6066911 Keywords : soil microfungi * frequency of species occurrence * post-mining dumps Subject RIV: EH - Ecology, Behaviour Impact factor: 1.011, year: 2001
Data preprocessing for data mining

OpenAIRE

Ren, Yifei

2013-01-01

People have increasing amounts data in the current prosperous information age. In order to improve competitive power and work efficiency, discovering knowledge from data is becoming more and more important. Data mining, as an emerging interdisciplinary applications field, plays a significant role in various trades’ and industries' decision making. However, it is known that original data is always dirty and not suitable for further analysis which have become a major obstacle of finding knowled...
Physics Mining of Multi-Source Data Sets

Science.gov (United States)

Helly, John; Karimabadi, Homa; Sipes, Tamara

2012-01-01

Powerful new parallel data mining algorithms can produce diagnostic and prognostic numerical models and analyses from observational data. These techniques yield higher-resolution measures than ever before of environmental parameters by fusing synoptic imagery and time-series measurements. These techniques are general and relevant to observational data, including raster, vector, and scalar, and can be applied in all Earth- and environmental science domains. Because they can be highly automated and are parallel, they scale to large spatial domains and are well suited to change and gap detection. This makes it possible to analyze spatial and temporal gaps in information, and facilitates within-mission replanning to optimize the allocation of observational resources. The basis of the innovation is the extension of a recently developed set of algorithms packaged into MineTool to multi-variate time-series data. MineTool is unique in that it automates the various steps of the data mining process, thus making it amenable to autonomous analysis of large data sets. Unlike techniques such as Artificial Neural Nets, which yield a blackbox solution, MineTool's outcome is always an analytical model in parametric form that expresses the output in terms of the input variables. This has the advantage that the derived equation can then be used to gain insight into the physical relevance and relative importance of the parameters and coefficients in the model. This is referred to as physics-mining of data. The capabilities of MineTool are extended to include both supervised and unsupervised algorithms, handle multi-type data sets, and parallelize it.
Combined Acquisition/Processing For Data Reduction

Science.gov (United States)

Kruger, Robert A.

1982-01-01

Digital image processing systems necessarily consist of three components: acquisition, storage/retrieval and processing. The acquisition component requires the greatest data handling rates. By coupling together the acquisition witn some online hardwired processing, data rates and capacities for short term storage can be reduced. Furthermore, long term storage requirements can be reduced further by appropriate processing and editing of image data contained in short term memory. The net result could be reduced performance requirements for mass storage, processing and communication systems. Reduced amounts of data also snouid speed later data analysis and diagnostic decision making.
Data acquisition instruments: Psychopharmacology

Energy Technology Data Exchange (ETDEWEB)

Hartley, D.S. III

1998-01-01

This report contains the results of a Direct Assistance Project performed by Lockheed Martin Energy Systems, Inc., for Dr. K. O. Jobson. The purpose of the project was to perform preliminary analysis of the data acquisition instruments used in the field of psychiatry, with the goal of identifying commonalities of data and strategies for handling and using the data in the most advantageous fashion. Data acquisition instruments from 12 sources were provided by Dr. Jobson. Several commonalities were identified and a potentially useful data strategy is reported here. Analysis of the information collected for utility in performing diagnoses is recommended. In addition, further work is recommended to refine the commonalities into a directly useful computer systems structure.
Big data mining analysis method based on cloud computing

Science.gov (United States)

Cai, Qing Qiu; Cui, Hong Gang; Tang, Hao

2017-08-01

Information explosion era, large data super-large, discrete and non-(semi) structured features have gone far beyond the traditional data management can carry the scope of the way. With the arrival of the cloud computing era, cloud computing provides a new technical way to analyze the massive data mining, which can effectively solve the problem that the traditional data mining method cannot adapt to massive data mining. This paper introduces the meaning and characteristics of cloud computing, analyzes the advantages of using cloud computing technology to realize data mining, designs the mining algorithm of association rules based on MapReduce parallel processing architecture, and carries out the experimental verification. The algorithm of parallel association rule mining based on cloud computing platform can greatly improve the execution speed of data mining.
Possibility of Integrated Data Mining of Clinical Data

Directory of Open Access Journals (Sweden)

Akinori Abe

2007-03-01

Full Text Available In this paper, we introduce integrated data mining. Because of recent rapid progress in medical science as well as clinical diagnosis and treatment, integrated and cooperative research among medical researchers, biology, engineering, cultural science, and sociology is required. Therefore, we propose a framework called Cyber Integrated Medical Infrastructure (CIMI. Within this framework, we can deal with various types of data and consequently need to integrate those data prior to analysis. In this study, for medical science, we analyze the features and relationships among various types of data and show the possibility of integrated data mining.
Data acquisition techniques using PC

CERN Document Server

Austerlitz, Howard

1991-01-01

Data Acquisition Techniques Using Personal Computers contains all the information required by a technical professional (engineer, scientist, technician) to implement a PC-based acquisition system. Including both basic tutorial information as well as some advanced topics, this work is suitable as a reference book for engineers or as a supplemental text for engineering students. It gives the reader enough understanding of the topics to implement a data acquisition system based on commercial products. A reader can alternatively learn how to custom build hardware or write his or her own software.
Data Mining and Analysis

Science.gov (United States)

Samms, Kevin O.

2015-01-01

The Data Mining project seeks to bring the capability of data visualization to NASA anomaly and problem reporting systems for the purpose of improving data trending, evaluations, and analyses. Currently NASA systems are tailored to meet the specific needs of its organizations. This tailoring has led to a variety of nomenclatures and levels of annotation for procedures, parts, and anomalies making difficult the realization of the common causes for anomalies. Making significant observations and realizing the connection between these causes without a common way to view large data sets is difficult to impossible. In the first phase of the Data Mining project a portal was created to present a common visualization of normalized sensitive data to customers with the appropriate security access. The tool of the visualization itself was also developed and fine-tuned. In the second phase of the project we took on the difficult task of searching and analyzing the target data set for common causes between anomalies. In the final part of the second phase we have learned more about how much of the analysis work will be the job of the Data Mining team, how to perform that work, and how that work may be used by different customers in different ways. In this paper I detail how our perspective has changed after gaining more insight into how the customers wish to interact with the output and how that has changed the product.
A Novel Visual Data Mining Module for the Geographical Information System gvSIG

Directory of Open Access Journals (Sweden)

Romel Vázquez-Rodríguez

2013-01-01

Full Text Available The exploration of large GIS models containing spatio-temporal information is a challenge. In this paper we propose the integration of scientific visualization (ScVis techniques into geographic information systems (GIS as an alternative for the visual analysis of data. Providing GIS with such tools improves the analysis and understanding of datasets with very low spatial density and allows to find correlations between variables in time and space. In this regard, we present a new visual data mining tool for the GIS gvSIG. This tool has been implemented as a gvSIG module and contains several ScVis techniques for multiparameter data with a wide range of possibilities to explore interactively the data. The developed module is a powerful visual data mining and data visualization tool to obtain knowledge from multiple datasets in time and space. A real case study with meteorological data from Villa Clara province (Cuba is presented, where the implemented visualization techniques were used to analyze the available datasets. Although it is tested with meteorological data, the developed module is of general application in the sense that it can be used in multiple application fields related with Earth Sciences.
Pattern-based framework for data acquisition systems

International Nuclear Information System (INIS)

Padmini, S.; Diwakar, M.P.; Nair, Preetha; Mathew, R.

2004-01-01

The data acquisition framework implements a reusable abstract architectural design for use in the development of data acquisition systems. The framework is being used to build Flux Mapping system (FMs) for TAPS III-IV and RRS Data Acquisition System for Dhruva reactor
Post-rehabilitation environmental hazard of Cu, Zn, As and Pb at the derelict Conrad Mine, eastern Australia

International Nuclear Information System (INIS)

Gore, Damian B.; Preston, Nicholas J.; Fryirs, Kirstie A.

2007-01-01

A post-rehabilitation audit of the derelict Conrad base metal mine, eastern Australia, indicates ongoing environmental hazard regarding acid mine drainage and concentrations of arsenic and lead to 3 wt% in the soil and sediment. In order to rehabilitate remote contaminated sites effectively, on-site analyses should be carried out to ensure that the materials used to rehabilitate the site are not contaminant-bearing. Understanding the geomorphic setting of the rehabilitated areas is also important in understanding where, and for what period, contaminated materials might be stored in fluvial systems downstream of mine workings. Chemical and geomorphic audits should form a fundamental part of all rehabilitation works to ensure favourable environmental outcomes. - Post-rehabilitation audit of mine reveals ongoing As and Pb environmental hazard enhanced by acid drainage and site geomorphology
The effect of soil macrofauna on water regime of post mining soils

Czech Academy of Sciences Publication Activity Database

Frouz, Jan; Kuráž, V.

2008-01-01

Roč. 10, - (2008) ISSN 1029-7006. [EGU General Assembly 2008. 13.04.2008-18.04.2008, Vienna] Institutional research plan: CEZ:AV0Z60660521 Keywords : soil macrofauna * water regime * post mining soil s Subject RIV: EH - Ecology, Behaviour
Development of a data mining and imaging informatics display tool for a multiple sclerosis e-folder system

Science.gov (United States)

Liu, Margaret; Loo, Jerry; Ma, Kevin; Liu, Brent

2011-03-01

Multiple sclerosis (MS) is a debilitating autoimmune disease of the central nervous system that damages axonal pathways through inflammation and demyelination. In order to address the need for a centralized application to manage and study MS patients, the MS e-Folder - a web-based, disease-specific electronic medical record system - was developed. The e-Folder has a PHP and MySQL based graphical user interface (GUI) that can serve as both a tool for clinician decision support and a data mining tool for researchers. This web-based GUI gives the e-Folder a user friendly interface that can be securely accessed through the internet and requires minimal software installation on the client side. The e-Folder GUI displays and queries patient medical records--including demographic data, social history, past medical history, and past MS history. In addition, DICOM format imaging data, and computer aided detection (CAD) results from a lesion load algorithm are also displayed. The GUI interface is dynamic and allows manipulation of the DICOM images, such as zoom, pan, and scrolling, and the ability to rotate 3D images. Given the complexity of clinical management and the need to bolster research in MS, the MS e-Folder system will improve patient care and provide MS researchers with a function-rich patient data hub.
Mining Product Data Models: A Case Study

Directory of Open Access Journals (Sweden)

Cristina-Claudia DOLEAN

2014-01-01

Full Text Available This paper presents two case studies used to prove the validity of some data-flow mining algorithms. We proposed the data-flow mining algorithms because most part of mining algorithms focuses on the control-flow perspective. First case study uses event logs generated by an ERP system (Navision after we set several trackers on the data elements needed in the process analyzed; while the second case study uses the event logs generated by YAWL system. We offered a general solution of data-flow model extraction from different data sources. In order to apply the data-flow mining algorithms the event logs must comply a certain format (using InputOutput extension. But to respect this format, a set of conversion tools is needed. We depicted the conversion tools used and how we got the data-flow models. Moreover, the data-flow model is compared to the control-flow model.
Evaluating remedial alternatives for an acid mine drainage stream: A model post audit

Science.gov (United States)

Runkel, Robert L.; Kimball, Briant A.; Walton-Day, Katherine; Verplanck, Philip L.; Broshears, Robert E.

2012-01-01

A post audit for a reactive transport model used to evaluate acid mine drainage treatment systems is presented herein. The post audit is based on a paired synoptic approach in which hydrogeochemical data are collected at low (existing conditions) and elevated (following treatment) pH. Data obtained under existing, low-pH conditions are used for calibration, and the resultant model is used to predict metal concentrations observed following treatment. Predictions for Al, As, Fe, H+, and Pb accurately reproduce the observed reduction in dissolved concentrations afforded by the treatment system, and the information provided in regard to standard attainment is also accurate (predictions correctly indicate attainment or nonattainment of water quality standards for 19 of 25 cases). Errors associated with Cd, Cu, and Zn are attributed to misspecification of sorbent mass (precipitated Fe). In addition to these specific results, the post audit provides insight in regard to calibration and sensitivity analysis that is contrary to conventional wisdom. Steps taken during the calibration process to improve simulations of As sorption were ultimately detrimental to the predictive results, for example, and the sensitivity analysis failed to bracket observed metal concentrations.
Evaluating remedial alternatives for an acid mine drainage stream: a model post audit.

Science.gov (United States)

Runkel, Robert L; Kimball, Briant A; Walton-Day, Katherine; Verplanck, Philip L; Broshears, Robert E

2012-01-03

A post audit for a reactive transport model used to evaluate acid mine drainage treatment systems is presented herein. The post audit is based on a paired synoptic approach in which hydrogeochemical data are collected at low (existing conditions) and elevated (following treatment) pH. Data obtained under existing, low-pH conditions are used for calibration, and the resultant model is used to predict metal concentrations observed following treatment. Predictions for Al, As, Fe, H(+), and Pb accurately reproduce the observed reduction in dissolved concentrations afforded by the treatment system, and the information provided in regard to standard attainment is also accurate (predictions correctly indicate attainment or nonattainment of water quality standards for 19 of 25 cases). Errors associated with Cd, Cu, and Zn are attributed to misspecification of sorbent mass (precipitated Fe). In addition to these specific results, the post audit provides insight in regard to calibration and sensitivity analysis that is contrary to conventional wisdom. Steps taken during the calibration process to improve simulations of As sorption were ultimately detrimental to the predictive results, for example, and the sensitivity analysis failed to bracket observed metal concentrations.
Data acquisition systems at Fermilab

International Nuclear Information System (INIS)

Votava, M.

1999-01-01

Experiments at Fermilab require an ongoing program of development for high speed, distributed data acquisition systems. The physics program at the lab has recently started the operation of a Fixed Target run in which experiments are running the DART[1] data acquisition system. The CDF and D experiments are preparing for the start of the next Collider run in mid 2000. Each will read out on the order of 1 million detector channels. In parallel, future experiments such as BTeV R ampersand D and Minos have already started prototype and test beam work. BTeV in particular has challenging data acquisition system requirements with an input rate of 1500 Gbytes/sec into Level 1 buffers and a logging rate of 200 Mbytes/sec. This paper will present a general overview of these data acquisition systems on three fronts those currently in use, those to be deployed for the Collider Run in 2000, and those proposed for future experiments. It will primarily focus on the CDF and D architectures and tools

Instrumentation, control and data acquisition system with multiple configurations for test in nuclear environment

Energy Technology Data Exchange (ETDEWEB)

Monti, Chiara, E-mail: chiara.monti@enea.it; Neri, Carlo; Pollastrone, Fabio

2015-10-15

Highlights: • ENEA developed and characterized a first prototype of the In-Vessel Viewing System (IVVS) probe for ITER. • Piezo motor technology to be used in IVVS probe was tested in neutrons, gamma radiations, high temperature, vacuum and high magnetic fields. • A general architecture of the Data Acquisition and Control System (DACS) was defined and then specialized for each test. • The test campaign has validated instrumentation solutions, which can be effectively used in final IVVS implementation or other ITER diagnostics or applications. - Abstract: The In-Vessel Viewing System is a 3D laser scanning system which will be used to inspect the blanket first wall in ITER. To make the IVVS probe design compatible with the harsh environmental conditions present in ITER, a test campaign was performed in 2012–2013 to verify the adequacy of the main components of the IVVS probe. The IVVS components inspected were an optical encoder, passive components and two customized ultrasonic piezoceramic motors that were instrumented with various sensors. A general architecture of the Data Acquisition and Control System (DACS) was defined and then specialized for each test. To be suitable for this test campaign, the DACS had to host various I/O modules and to properly interface the driver of the customized piezo motors, in order to permit the full control of the test and the acquisition of experimental data. This paper presents the instrumentation solutions designed and implemented for different facilities constraints and the related DACS developed in four specialized versions for the described test campaign.
Applying Data-mining techniques to study drought periods in Spain

Science.gov (United States)

Belda, F.; Penades, M. C.

2010-09-01

Data-mining is a technique that it can be used to interact with large databases and to help in the discovery relations between parameters by extracting information from massive and multiple data archives. Drought affects many economic and social sectors, from agricultural to transportation, going through urban water deficit and the development of modern industries. With these problems and drought geographical and temporal distribution it's difficult to find a single definition of drought. Improving the understanding of the knowledge of climatic index is necessary to reduce the impacts of drought and to facilitate quick decisions regarding this problem. The main objective is to analyze drought periods from 1950 to 2009 in Spain. We use several kinds of information, different formats, sources and transmission mode. We use satellite-based Vegetation Index, dryness index for several temporal periods. We use daily and monthly precipitation and temperature data and soil moisture data from numerical weather model. We calculate mainly Standardized Precipitation Index (SPI) that it has been used amply in the bibliography. We use OLAP-Mining techniques to discovery of association rules between remote-sensing, numerical weather model and climatic index. Time series Data- Mining techniques organize data as a sequence of events, with each event having a time of recurrence, to cluster the data into groups of records or cluster with similar characteristics. Prior climatological classification is necessary if we want to study drought periods over all Spain.
Modeling and Analysis of Integrated Bathymetric and Geodetic Data for Inventory Surveys of Mining Water Reservoirs

Science.gov (United States)

Ochałek, Agnieszka; Lipecki, Tomasz; Jaśkowski, Wojciech; Jabłoński, Mateusz

2018-03-01

The significant part of the hydrography is bathymetry, which is the empirical part of it. Bathymetry is the study of underwater depth of waterways and reservoirs, and graphic presentation of measured data in form of bathymetric maps, cross-sections and three-dimensional bottom models. The bathymetric measurements are based on using Global Positioning System and devices for hydrographic measurements - an echo sounder and a side sonar scanner. In this research authors focused on introducing the case of obtaining and processing the bathymetrical data, building numerical bottom models of two post-mining reclaimed water reservoirs: Dwudniaki Lake in Wierzchosławice and flooded quarry in Zabierzów. The report includes also analysing data from still operating mining water reservoirs located in Poland to depict how bathymetry can be used in mining industry. The significant issue is an integration of bathymetrical data and geodetic data from tachymetry, terrestrial laser scanning measurements.
Extending DART to meet the data acquisition needs of future experiments at Fermilab

International Nuclear Information System (INIS)

Oleynik, Gene; Pordes, Ruth; Barsotti, Ed

1996-01-01

The DART project at Fermilab is a major collaboration to develop a data acquisition system for multiple experiments. The initial implementation of DART has concentrated on providing working data acquisition systems for the (now eight) collaborating experiments in the next Fixed Target Run. In this paper we discuss aspects of the architecture of DART and how these will allow it to be extended to meet the expected needs of future experiments at Fermilab. We also discuss some ongoing developments within the Fermilab Computing Division towards these new implementations. (author)
Extending DART to meet the data acquisition needs of future experiments at Fermilab

International Nuclear Information System (INIS)

Oleynik, G.; Pordes, R.; Barsotti, E.

1995-10-01

The DART project at Fermilab is a major collaboration to develop a data acquisition system for multiple experiments. The initial implementation of DART has concentrated on providing working data acquisition systems for the (now eight) collaborating experiments in the next Fixed Target Run. In this paper we discuss aspects of the architecture of DART and how these will allow it to be extended to meet the expected needs of future experiments at Fermilab. We also discuss some ongoing developments within the Fermilab Computing Division towards these new implementations
Process mining : data science in action

NARCIS (Netherlands)

Van der Aalst, W.M.P.

2016-01-01

This is the second edition of Wil van der Aalst’s seminal book on process mining, which now discusses the field also in the broader context of data science and big data approaches. It includes several additions and updates, e.g. on inductive mining techniques, the notion of alignments, a
HiQ International Company: A Case Study of Operational Effectiveness post Merger and Acquisition

OpenAIRE

Belle Selene Xia

2014-01-01

Mergers and acquisitions occur in a dynamic process in which resource allocation and organization structures are restructured in response to new challenges. Analyzing mergers and acquisitions in the technology sector using the empirical case of HiQ International Ab shows that management efficiency post merger and acquisition requires the reallocation of resources where traditions and culture are broken resulting in numerous side effects of change. In addition, the management morale in the bus...
Data Mining and Machine Learning in Astronomy

Science.gov (United States)

Ball, Nicholas M.; Brunner, Robert J.

We review the current state of data mining and machine learning in astronomy. Data Mining can have a somewhat mixed connotation from the point of view of a researcher in this field. If used correctly, it can be a powerful approach, holding the potential to fully exploit the exponentially increasing amount of available data, promising great scientific advance. However, if misused, it can be little more than the black box application of complex computing algorithms that may give little physical insight, and provide questionable results. Here, we give an overview of the entire data mining process, from data collection through to the interpretation of results. We cover common machine learning algorithms, such as artificial neural networks and support vector machines, applications from a broad range of astronomy, emphasizing those in which data mining techniques directly contributed to improving science, and important current and future directions, including probability density functions, parallel algorithms, Peta-Scale computing, and the time domain. We conclude that, so long as one carefully selects an appropriate algorithm and is guided by the astronomical problem at hand, data mining can be very much the powerful tool, and not the questionable black box.
Working group report: methane emissions from coal mining

International Nuclear Information System (INIS)

Kruger, D.

1993-01-01

The process of coalification inherently generates methane and other byproducts. The amount of methane released during coal mining is a function of coal rank and depth, gas content, and mining methods, as well as other factors such as moisture. In most underground mines, methane is removed by drawing large quantities of air through the mine releasing the air into the atmosphere. In surface mines, exposed coal faces and surfaces, as well as areas of coal rubble created by blasting operations are believed to be the major sources of methane. A portion of the methane emitted from coal mining comes from post-mining activities such as coal processing, transportation, and utilisation. Some methane is also released from coal waste piles and abandoned mines. This paper highlights difficulties with previous methane emission studies namely: absence of data on which to base estimates; use of national data to develop global estimates; failure to include all possible emission sources; overreliance on statistical estimation methodologies. It recommends a 'tiered' approach for the estimation of emissions from underground mines, surface mines and post-mining activities. For each source, two or more approaches (or 'tiers') are presented, with the first tier requiring basic and readily available data and higher tiers requiring additional data. 29 refs., 3 tabs
Coal Mines Security System

OpenAIRE

Ankita Guhe; Shruti Deshmukh; Bhagyashree Borekar; Apoorva Kailaswar; Milind E.Rane

2012-01-01

Geological circumstances of mine seem to be extremely complicated and there are many hidden troubles. Coal is wrongly lifted by the musclemen from coal stocks, coal washeries, coal transfer and loading points and also in the transport routes by malfunctioning the weighing of trucks. CIL —Coal India Ltd is under the control of mafia and a large number of irregularities can be contributed to coal mafia. An Intelligent Coal Mine Security System using data acquisition method utilizes sensor, auto...
A smart modules network for real time data acquisition: application to biomedical research.

Science.gov (United States)

Logier, R; De jonckheere, J; Dassonneville, A; Chaud, P; Jeanne, M

2009-01-01

Healthcare monitoring applications require the measurement and the analysis of multiple physiological data. In the field of biomedical research, these data are issued from different devices involving data centralization and synchronization difficulties. In this paper, we describe a smart hardware modules network for biomedical data real time acquisition. This toolkit, composed of multiple electronic modules, allows users to acquire and transmit all kind of biomedical signals and parameters. These highly efficient hardware modules have been developed and tested especially for biomedical studies and used in a large number of clinical investigations.
DATA MINING AND APPLICATION OF IT TO CAPITAL MARKETS

Directory of Open Access Journals (Sweden)

Cenk AKKAYA

2011-07-01

Full Text Available Nowadays with the development of technology importance given to knowledge increases gradually. Data mining enables to form forecasts and models regarding future by making use of past data. Any method which helps to discover data can be used as a data mining method. Enterprises gain important competitive advantage by data mining methods. Data mining is used in different fields. In finance field it is a specially used in financial performance applications, guessing the enterprise bankruptcies and failures, determining transaction manipulation, determining financial risk management, determining customer profile and depth management. It can be costly, risky and time consuming for enterprises to gain knowledge. Thus today enterprises use data mining as an innovative competitive mean. The aim of the study is to determine the importance of data mining applications to capital markets.
The integrated workstation, a realtime data acquisition, analysis and display system

International Nuclear Information System (INIS)

Treadway, T.R. III.

1991-05-01

The Integrated Workstation was developed at Lawrence Livermore National Laboratory to consolidate the data from many widely dispersed systems in order to provide an overall indication of the enrichment performance of the Atomic Vapor Laser Isotope Separation experiments. In order to accomplish this task a Hewlett Packard 9000/835 turboSRX was employed to acquire over 150 analog input signals. Following the data acquisition, a spreadsheet-type analysis package and interpreter was used to derive 300 additional values. These values were the results of applying physics models to the raw data. Following the calculations were plotted and archived for post-run analysis and report generation. Both the modeling calculations, and real-time plot configurations can be dynamically reconfigured as needed. Typical sustained data acquisition and display rates of the system was 1 Hz. However rates exceeding 2.5 Hz have been obtained. This paper will discuss the instrumentation, architecture, implementation, usage, and results of this system in a set of experiments that occurred in 1989. 2 figs
Data Mining Practical Machine Learning Tools and Techniques

CERN Document Server

Witten, Ian H; Hall, Mark A

2011-01-01

Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place
Data Mining and Statistics for Decision Making

CERN Document Server

Tufféry, Stéphane

2011-01-01

Data mining is the process of automatically searching large volumes of data for models and patterns using computational techniques from statistics, machine learning and information theory; it is the ideal tool for such an extraction of knowledge. Data mining is usually associated with a business or an organization's need to identify trends and profiles, allowing, for example, retailers to discover patterns on which to base marketing objectives. This book looks at both classical and recent techniques of data mining, such as clustering, discriminant analysis, logistic regression, generalized lin
Analysis of post-mining excavations as places for municipal waste

Science.gov (United States)

Górniak-Zimroz, Justyna

2018-01-01

Waste management planning is an interdisciplinary task covering a wide range of issues including costs, legal requirements, spatial planning, environmental protection, geography, demographics, and techniques used in collecting, transporting, processing and disposing of waste. Designing and analyzing this issue is difficult and requires the use of advanced analysis methods and tools available in GIS geographic information systems containing readily available graphical and descriptive databases, data analysis tools providing expert decision support while selecting the best-designed alternative, and simulation models that allow the user to simulate many variants of waste management together with graphical visualization of the results of performed analyzes. As part of the research study, there have been works undertaken concerning the use of multi-criteria data analysis in waste management in areas located in southwestern Poland. These works have proposed the inclusion in waste management of post-mining excavations as places for the final or temporary collection of waste assessed in terms of their suitability with the tools available in GIS systems.
Mobile field data acquisition in geosciences

Science.gov (United States)

Golodoniuc, Pavel; Klump, Jens; Reid, Nathan; Gray, David

2016-04-01

module also features an interactive GIS component allowing to enter field observations as annotations to a map. The open communication protocols and file formats used by FAIMS modules allow easy integration with existing spatial data infrastructures and third-party applications, such as ArcGIS. The remoteness of the focus areas in the Capricorn region required reliable mechanisms for data replication and an added level of redundancy. This was achieved through the use of the FAIMS Server without adding a tightly coupled dependency on it - the mobile devices could continue to work independently in the case the server fails. To support collaborative fieldwork, "FAIMS on a Truck" offers networked collaboration within a field team using mobile applications as asynchronous rich clients. The framework runs on compatible Android devices (e.g., tablets, smart phones) with the network infrastructure supported by a FAIMS Server. The server component is installed in a field vehicle to provide data synchronisation between multiple mobile devices, backup and data transfer. The data entry process was streamlined and followed the workflow that field crews were accustomed to with added data validation capabilities. The use of a common platform allowed us to adopt the framework within multiple disciplines, improve data acquisition times, and reduce human-introduced errors. We continue to work with other research groups and continue to explore the possibilities to adopt the technology in other applications, e.g., agriculture.
Abundance of arbuscular mychorrizal fungi in rehabilitation area of nickel post-mining land of Sorowako, South Sulawesi

Science.gov (United States)

Akib, M. A.; Mustari, K.; Kuswinanti, T.; Syaiful, S. A.

2018-05-01

Acceleration management of land rehabilitation in nickel post-mining in Sorowako has been main attention of Vale Indonesia. This acceleration can be done by utilizing of natural resources, especially indigenous endomycorrhiza. Endomycorrhiza also called arbuscular mycorrhizal has got a lot of attention for its ability to form a mutualistic symbiosis with 80% - 96% of plant species. This study aims to determine the dominance of indigenous endomycorrhiza spores and its potential to accelerate the management of land rehabilitation post-mining of nickel, which is carried out in three stages; sampling rhizosphere, trapping spores, isolation and identification of the arbuscular mycorrhizal spores types. The results showed that the dominance of indigenous endomycorrhiza were Acalauspora sp (75.1%), Gigaspora sp (19.4%) and Glomus sp (5.6%). Research on the effectiveness of indigenous endomycorrhiza using Acalauspora sp in land rehabilitation of nickel post-mining is still ongoing.
EBT data acquisition and analysis system

International Nuclear Information System (INIS)

Burris, R.D.; Greenwood, D.E.; Stanton, J.S.; Geoffroy, K.A.

1980-10-01

This document describes the design and implementation of a data acquisition and analysis system for the EBT fusion experiment. The system includes data acquisition on five computers, automatic transmission of that data to a large, central data base, and a powerful data retrieval system. The system is flexible and easy to use, and it provides a fully documented record of the experiments
Data Mining and Data Fusion for Enhanced Decision Support

Energy Technology Data Exchange (ETDEWEB)

Khan, Shiraj [ORNL; Ganguly, Auroop R [ORNL; Gupta, Amar [University of Arizona

2008-01-01

The process of Data Mining converts information to knowledge by utilizing tools from the disciplines of computational statistics, database technologies, machine learning, signal processing, nonlinear dynamics, process modeling, simulation, and allied disciplines. Data Mining allows business problems to be analyzed from diverse perspectives, including dimensionality reduction, correlation and co-occurrence, clustering and classification, regression and forecasting, anomaly detection, and change analysis. The predictive insights generated from Data Mining can be further utilized through real-time analysis and decision sciences, as well as through human-driven analysis based on management by exceptions or by objectives, to generate actionable knowledge. The tools that enable the transformation of raw data to actionable predictive insights are collectively referred as Decision Support tools. This chapter presents a new formalization of the decision process, leading to a new Decision Superiority model, partially motivated by the Joint Directors of Laboratories (JDL) Data Fusion Model. In addition, it examines the growing importance of Data Fusion concepts.

The Top Ten Algorithms in Data Mining

CERN Document Server

Wu, Xindong

2009-01-01

From classification and clustering to statistical learning, association analysis, and link mining, this book covers the most important topics in data mining research. It presents the ten most influential algorithms used in the data mining community today. Each chapter provides a detailed description of the algorithm, a discussion of available software implementation, advanced topics, and exercises. With a simple data set, examples illustrate how each algorithm works and highlight the overall performance of each algorithm in a real-world application. Featuring contributions from leading researc
Modeling and Analysis of Integrated Bathymetric and Geodetic Data for Inventory Surveys of Mining Water Reservoirs

Directory of Open Access Journals (Sweden)

Ochałek Agnieszka

2018-01-01

Full Text Available The significant part of the hydrography is bathymetry, which is the empirical part of it. Bathymetry is the study of underwater depth of waterways and reservoirs, and graphic presentation of measured data in form of bathymetric maps, cross-sections and three-dimensional bottom models. The bathymetric measurements are based on using Global Positioning System and devices for hydrographic measurements – an echo sounder and a side sonar scanner. In this research authors focused on introducing the case of obtaining and processing the bathymetrical data, building numerical bottom models of two post-mining reclaimed water reservoirs: Dwudniaki Lake in Wierzchosławice and flooded quarry in Zabierzów. The report includes also analysing data from still operating mining water reservoirs located in Poland to depict how bathymetry can be used in mining industry. The significant issue is an integration of bathymetrical data and geodetic data from tachymetry, terrestrial laser scanning measurements.
Research on Customer Value Based on Extension Data Mining

Science.gov (United States)

Chun-Yan, Yang; Wei-Hua, Li

Extenics is a new discipline for dealing with contradiction problems with formulize model. Extension data mining (EDM) is a product combining Extenics with data mining. It explores to acquire the knowledge based on extension transformations, which is called extension knowledge (EK), taking advantage of extension methods and data mining technology. EK includes extensible classification knowledge, conductive knowledge and so on. Extension data mining technology (EDMT) is a new data mining technology that mining EK in databases or data warehouse. Customer value (CV) can weigh the essentiality of customer relationship for an enterprise according to an enterprise as a subject of tasting value and customers as objects of tasting value at the same time. CV varies continually. Mining the changing knowledge of CV in databases using EDMT, including quantitative change knowledge and qualitative change knowledge, can provide a foundation for that an enterprise decides the strategy of customer relationship management (CRM). It can also provide a new idea for studying CV.
Automatic detection of referral patients due to retinal pathologies through data mining.

Science.gov (United States)

Quellec, Gwenolé; Lamard, Mathieu; Erginay, Ali; Chabouis, Agnès; Massin, Pascale; Cochener, Béatrice; Cazuguel, Guy

2016-04-01

With the increased prevalence of retinal pathologies, automating the detection of these pathologies is becoming more and more relevant. In the past few years, many algorithms have been developed for the automated detection of a specific pathology, typically diabetic retinopathy, using eye fundus photography. No matter how good these algorithms are, we believe many clinicians would not use automatic detection tools focusing on a single pathology and ignoring any other pathology present in the patient's retinas. To solve this issue, an algorithm for characterizing the appearance of abnormal retinas, as well as the appearance of the normal ones, is presented. This algorithm does not focus on individual images: it considers examination records consisting of multiple photographs of each retina, together with contextual information about the patient. Specifically, it relies on data mining in order to learn diagnosis rules from characterizations of fundus examination records. The main novelty is that the content of examination records (images and context) is characterized at multiple levels of spatial and lexical granularity: 1) spatial flexibility is ensured by an adaptive decomposition of composite retinal images into a cascade of regions, 2) lexical granularity is ensured by an adaptive decomposition of the feature space into a cascade of visual words. This multigranular representation allows for great flexibility in automatically characterizing normality and abnormality: it is possible to generate diagnosis rules whose precision and generalization ability can be traded off depending on data availability. A variation on usual data mining algorithms, originally designed to mine static data, is proposed so that contextual and visual data at adaptive granularity levels can be mined. This framework was evaluated in e-ophtha, a dataset of 25,702 examination records from the OPHDIAT screening network, as well as in the publicly-available Messidor dataset. It was successfully
Data Acquisition Backbone Core DABC

International Nuclear Information System (INIS)

Adamczewski, J; Essel, H G; Kurz, N; Linev, S

2008-01-01

For the new experiments at FAIR new concepts of data acquisition systems have to be developed like the distribution of self-triggered, time stamped data streams over high performance networks for event building. The Data Acquisition Backbone Core (DABC) is a software package currently under development for FAIR detector tests, readout components test, and data flow investigations. All kinds of data channels (front-end systems) are connected by program plug-ins into functional components of DABC like data input, combiner, scheduler, event builder, analysis and storage components. After detailed simulations real tests of event building over a switched network (InfiniBand clusters with up to 110 nodes) have been performed. With the DABC software more than 900 MByte/s input and output per node can be achieved meeting the most demanding requirements. The software is ready for the implementation of various test beds needed for the final design of data acquisition systems at FAIR. The development of key components is supported by the FutureDAQ project of the European Union (FP6 I3HP JRA1)
Optimising post-mining soil conditions to maximise restoration success in a biodiverse semiarid environment

Science.gov (United States)

Muñoz-Rojas, Miriam; Erickson, Todd; Merritt, David; Dixon, Kingsley

2014-05-01

The original topsoil of mine degraded areas is frequently lost or damaged, which together with the absence of soil forming materials is a major constraint for seed germination and establishment in post-mining restoration. Thus, management of the available topsoil and the use of alternative growth media are critical to improve restoration areas disturbed through mining. Here we are developing laboratory and field trials to define the optimal range for physical and chemical properties of potentially suitable natural and 're-made' soil substrates and growth medium for 20 selected native plant species from the mining intensive Pilbara region of Western Australia. In this semiarid area, water is a limiting factor for seedling establishment, which is compounded by the lack of organic matter of post-disturbance soils. Therefore, particular attention is given to indicators of soil biological activity such as soil respiration, and hydrological soil properties such as water holding capacity, infiltration, hydraulic conductivity and soil water repellence. This research is part of a broader multi-study approach, the Restoration Seedbank Initiative project, a partnership between The University of Western Australia, BHP Billiton Iron Ore, and Kings Park and Botanic Garden to develop the science and underpinning knowledge to achieve biodiverse restoration in the Pilbara region, where land areas disturbed by mining exceed 40,000 ha. Achieving restoration success is critical as the Pilbara region is an ancient landscape with diverse geology and high levels of regional and local endemism in plants and animals.
Advanced Data Mining of Leukemia Cells Micro-Arrays

Directory of Open Access Journals (Sweden)

Richard S. Segall

2009-12-01

Full Text Available This paper provides continuation and extensions of previous research by Segall and Pierce (2009a that discussed data mining for micro-array databases of Leukemia cells for primarily self-organized maps (SOM. As Segall and Pierce (2009a and Segall and Pierce (2009b the results of applying data mining are shown and discussed for the data categories of microarray databases of HL60, Jurkat, NB4 and U937 Leukemia cells that are also described in this article. First, a background section is provided on the work of others pertaining to the applications of data mining to micro-array databases of Leukemia cells and micro-array databases in general. As noted in predecessor article by Segall and Pierce (2009a, micro-array databases are one of the most popular functional genomics tools in use today. This research in this paper is intended to use advanced data mining technologies for better interpretations and knowledge discovery as generated by the patterns of gene expressions of HL60, Jurkat, NB4 and U937 Leukemia cells. The advanced data mining performed entailed using other data mining tools such as cubic clustering criterion, variable importance rankings, decision trees, and more detailed examinations of data mining statistics and study of other self-organized maps (SOM clustering regions of workspace as generated by SAS Enterprise Miner version 4. Conclusions and future directions of the research are also presented.
Data mining applications in the context of casemix.

Science.gov (United States)

Koh, H C; Leong, S K

2001-07-01

In October 1999, the Singapore Government introduced casemix-based funding to public hospitals. The casemix approach to health care funding is expected to yield significant benefits, including equity and rationality in financing health care, the use of comparative casemix data for quality improvement activities, and the provision of information that enables hospitals to understand their cost behaviour and reinforces the drive for more cost-efficient services. However, there is some concern about the "quicker and sicker" syndrome (that is, the rapid discharge of patients with little regard for the quality of outcome). As it is likely that consequences of premature discharges will be reflected in the readmission data, an analysis of possible systematic patterns in readmission data can provide useful insight into the "quicker and sicker" syndrome. This paper explores potential data mining applications in the context of casemix by using readmission data as an illustration. In particular, it illustrates how data mining can be used to better understand readmission data and to detect systematic patterns, if any. From a technical perspective, data mining (which is capable of analysing complex non-linear and interaction relationships) supplements and complements traditional statistical methods in data analysis. From an applications perspective, data mining provides the technology and methodology to analyse mass volume of data to detect hidden patterns in data. Using readmission data as an illustrative data mining application, this paper explores potential data mining applications in the general casemix context.
Data-Acquisition Systems for Fusion Devices

NARCIS (Netherlands)

van Haren, P. C.; Oomens, N. A.

1993-01-01

During the last two decades, computerized data acquisition systems (DASs) have been applied at magnetic confinement fusion devices. Present-day data acquisition is done by means of distributed computer systems and transient recorders in CAMAC systems. The development of DASs has been technology
Data acquisition upgrade in the RFX experiment

International Nuclear Information System (INIS)

Barana, O.; Luchetta, A.; Manduchi, G.; Taliercio, C.

2005-01-01

The control and data acquisition system of RFX has been completely renewed and is currently under re-commissioning. Most data acquisition is now performed by means of CompactPCI (CPCI) devices supervised by Pentium PCs running Linux. Real-time control systems, implemented using VME and PowerPCs running VxWorks, also produce data that are then acquired by the data acquisition system. The older CAMAC systems have only been retained for existing diagnostics. New diagnostic systems will employ either CompactPCI data acquisition or custom solutions, usually running under Windows, due to the fact that the drivers for the used devices are normally available for this platform. Despite the variety of hardware and software platforms involved in data acquisition, the same software package is used for all components, thus providing a uniform view of the system. Such functionality is provided by the MDSplus software. MDSplus is now available for a variety of platforms, and includes several Java components that are platform-independent
Study of customer acquisition support system for mobile operators

Institute of Scientific and Technical Information of China (English)

无

2007-01-01

The mobile operators are struggling for improving the market share and the revenues.One important method is to acquire the potential customers from the competitors.This article presents a whole acquisition process and an integrated framework for customer acquisition support system (CASS).The core of the system is the customer acquisition identification models which are built based on data mining technologies.The CASS can automate the acquisition process and decrease the cost and implement precise marketing strategy for mobile operators.
Future data acquisition at ISIS

International Nuclear Information System (INIS)

Pulford, W.C.A.; Quinton, S.P.H.; Johnson, M.W.; Norris, J.

1989-01-01

Over the past year ISIS beam intensity has increased steadily to 100 microamps during periods of good running. With the instrument users finding it comparatively easy to set up data-collection runs, we are facing an ever increasing volume of incoming data. Greatly improved detector technology, mainly involving large areas of zinc sulfide phosphor, are expected to contribute much to the capacity of new diffractometers as well as provide an enhancement path for many of the existing ones. It is clear that we are fast reaching the point where if we continue to use our current technology data collection techniques, our computer systems will no longer be able to migrate the data to long-term storage, let alone enable their analysis at a speed compatible with continuous use of the ISIS instruments. The most effect method to improve this situation is to reduce the volume of data flowing between the data acquisition electronics and the front end minicomputers, and to provide facilities to monitor data acquisition within the data acquisition electronics. Processing power must be incorporated closer to the point of data collection. Ways of doing this are discussed and evaluated. (author)
Recurrent process mining with live event data

NARCIS (Netherlands)

Syamsiyah, A.; van Dongen, B.F.; van der Aalst, W.M.P.; Teniente, E.; Weidlich, M.

2018-01-01

In organizations, process mining activities are typically performed in a recurrent fashion, e.g. once a week, an event log is extracted from the information systems and a process mining tool is used to analyze the process’ characteristics. Typically, process mining tools import the data from a
DIII-D Thomson Scattering Diagnostic Data Acquisition, Processing and Analysis Software

International Nuclear Information System (INIS)

Middaugh, K.R.; Bray, B.D.; Hsieh, C.L.; McHarg, B.B.Jr.; Penaflor, B.G.

1999-01-01

One of the diagnostic systems critical to the success of the DIII-D tokamak experiment is the Thomson scattering diagnostic. This diagnostic is unique in that it measures local electron temperature and density: (1) at multiple locations within the tokamak plasma; and (2) at different times throughout the plasma duration. Thomson ''raw'' data are digitized signals of scattered light, measured at different times and locations, from the laser beam paths fired into the plasma. Real-time acquisition of this data is performed by specialized hardware. Once obtained, the raw data are processed into meaningful temperature and density values which can be analyzed for measurement quality. This paper will provide an overview of the entire Thomson scattering diagnostic software and will focus on the data acquisition, processing, and analysis software implementation. The software falls into three general categories: (1) Set-up and Control: Initializes and controls all Thomson hardware and software, synchronizes with other DIII-D computers, and invokes other Thomson software as appropriate. (2) Data Acquisition and Processing: Obtains raw measured data from memory and processes it into temperature and density values. (3) Analysis: Provides a graphical user interface in which to perform analysis and sophisticated plotting of analysis parameters
Application and Exploration of Big Data Mining in Clinical Medicine.

Science.gov (United States)

Zhang, Yue; Guo, Shu-Li; Han, Li-Na; Li, Tie-Ling

2016-03-20

To review theories and technologies of big data mining and their application in clinical medicine. Literatures published in English or Chinese regarding theories and technologies of big data mining and the concrete applications of data mining technology in clinical medicine were obtained from PubMed and Chinese Hospital Knowledge Database from 1975 to 2015. Original articles regarding big data mining theory/technology and big data mining's application in the medical field were selected. This review characterized the basic theories and technologies of big data mining including fuzzy theory, rough set theory, cloud theory, Dempster-Shafer theory, artificial neural network, genetic algorithm, inductive learning theory, Bayesian network, decision tree, pattern recognition, high-performance computing, and statistical analysis. The application of big data mining in clinical medicine was analyzed in the fields of disease risk assessment, clinical decision support, prediction of disease development, guidance of rational use of drugs, medical management, and evidence-based medicine. Big data mining has the potential to play an important role in clinical medicine.
Data Acquisition and Real-Time Systems.

Science.gov (United States)

Lawrence, D. E., Ed.; Fenwick, P. M., Ed.

The first group of papers starts with a tutorial paper which surveys the methods used in data acquisition systems. Other papers in this group describe: (1) some problems involved in the computer acquisition of high-speed randomly-occurring data and the protection of this data from accidental corruption, (2) an input/output bus to allow an IBM…
Robust processing of mining subsidence monitoring data

Energy Technology Data Exchange (ETDEWEB)

Mingzhong, Wang; Guogang, Huang [Pingdingshan Mining Bureau (China); Yunjia, Wang; Guogangli, [China Univ. of Mining and Technology, Xuzhou (China)

1997-12-31

Since China began to do research on mining subsidence in 1950s, more than one thousand lines have been observed. Yet, monitoring data sometimes contain quite a lot of outliers because of the limit of observation and geological mining conditions. In China, nowdays, the method of processing mining subsidence monitoring data is based on the principle of the least square method. It is possible to produce lower accuracy, less reliability, or even errors. For reason given above, the authors, according to Chinese actual situation, have done some research work on the robust processing of mining subsidence monitoring data in respect of how to get prediction parameters. The authors have derived related formulas, designed some computational programmes, done a great quantity of actual calculation and simulation, and achieved good results. (orig.)
Robust processing of mining subsidence monitoring data

Energy Technology Data Exchange (ETDEWEB)

Wang Mingzhong; Huang Guogang [Pingdingshan Mining Bureau (China); Wang Yunjia; Guogangli [China Univ. of Mining and Technology, Xuzhou (China)

1996-12-31

Since China began to do research on mining subsidence in 1950s, more than one thousand lines have been observed. Yet, monitoring data sometimes contain quite a lot of outliers because of the limit of observation and geological mining conditions. In China, nowdays, the method of processing mining subsidence monitoring data is based on the principle of the least square method. It is possible to produce lower accuracy, less reliability, or even errors. For reason given above, the authors, according to Chinese actual situation, have done some research work on the robust processing of mining subsidence monitoring data in respect of how to get prediction parameters. The authors have derived related formulas, designed some computational programmes, done a great quantity of actual calculation and simulation, and achieved good results. (orig.)
''Big Dee'' upgrade of the Doublet III diagnostic data acquisition computer system

International Nuclear Information System (INIS)

Mcharg, B.B.

1983-01-01

The ''Big Dee'' upgrade of the Doublet III tokamak facility will begin operation in 1986 with an initial quantity of data expected to be 10 megabytes per shot and eventually attaining 20-25 megabytes per shot. This is in comparison to the 4-5 megabytes of data currently acquired. To handle this greater quantity of data and to serve physics needs for significantly improved between-shot processing of data will require a substantial upgrade of the existing data acquisition system. The key points of the philosophy that have been adopted for the upgraded system to handle the greater quantity of data are (1) preserve existing hardware, (2) preserve existing software; (3) configure the system in a modular fashion; and (4) distribute the data acquisition over multiple computers. The existing system using ModComp CLASSIC 16 bit minicomputers is capable of handling 5 megabytes of data per shot
Big Dee upgrade of the Doublet III diagnostic data acquisition computer system

International Nuclear Information System (INIS)

McHarg, B.B. Jr.

1983-12-01

The Big Dee upgrade of the Doublet III tokamak facility will begin operation in 1986 with an initial quantity of data expected to be 10 megabytes per shot and eventually attaining 20 to 25 megabytes per shot. This is in comparison to the 4 to 5 megabytes of data currently acquired. To handle this greater quantity of data and to serve physics needs for significantly improved between-shot processing of data will require a substantial upgrade of the existing data acquisition system. The key points of the philosophy that have been adopted for the upgraded system to handle the greater quantity of data are (1) preserve existing hardware; (2) preserve existing software; (3) configure the system in a modular fashion; and (4) distribute the data acquisition over multiple computers. The existing system using ModComp CLASSIC 16 bit minicomputers is capable of handling 5 megabytes of data per shot

On data mining in context : cases, fusion and evaluation

NARCIS (Netherlands)

Putten, Petrus Wilhelmus Henricus van der

2010-01-01

Data mining can be seen as a process, with modeling as the core step. However, other steps such as planning, data preparation, evaluation and deployment are of key importance for applications. This thesis studies data mining in the context of these other steps with the goal of improving data mining
A VMEbus general-purpose data acquisition system

International Nuclear Information System (INIS)

Ninane, A.; Nemry, M.; Martou, J.L.; Somers, F.

1992-01-01

We present a general-purpose, VMEbus based, multiprocessor data acquisition and monitoring system. Events, handled by a master CPU, are kept at the disposal of data storage and monitoring processes which can run on distinct processors. They access either the complete set of data or a fraction of them, minimizing the acquisition dead-time. The system is built with the VxWorks 5.0 real time kernel to which we have added device drivers for data acquisition and monitoring. The acquisition is controlled and the data are displayed on a workstation. The user interface is written in C ++ and re-uses the classes of the Interviews and the NIH libraries. The communication between the control workstation and the VMEbus processors is made through SUN RPCs on an Ethernet link. The system will be used for, CAMAC based, data acquisition for nuclear physics experiments as well as for the VXI data taking with the 4π configuration (100 neutron detectors) of the Brussels-Caen-Louvian-Strasbourg DEMON collaboration. (author)
Model-based Sensor Data Acquisition and Management

OpenAIRE

Aggarwal, Charu C.; Sathe, Saket; Papaioannou, Thanasis G.; Jeung, Ho Young; Aberer, Karl

2012-01-01

In recent years, due to the proliferation of sensor networks, there has been a genuine need of researching techniques for sensor data acquisition and management. To this end, a large number of techniques have emerged that advocate model-based sensor data acquisition and management. These techniques use mathematical models for performing various, day-to-day tasks involved in managing sensor data. In this chapter, we survey the state-of-the-art techniques for model-based sensor data acquisition...
TFTR diagnostic control and data acquisition system

International Nuclear Information System (INIS)

Sauthoff, N.R.; Daniels, R.E.; PPL Computer Division

1985-01-01

General computerized control and data-handling support for TFTR diagnostics is presented within the context of the Central Instrumentation, Control and Data Acquisition (CICADA) System. Procedures, hardware, the interactive man--machine interface, event-driven task scheduling, system-wide arming and data acquisition, and a hierarchical data base of raw data and results are described. Similarities in data structures involved in control, monitoring, and data acquisition afford a simplification of the system functions, based on ''groups'' of devices. Emphases and optimizations appropriate for fusion diagnostic system designs are provided. An off-line data reduction computer system is under development
Warehousing Structured and Unstructured Data for Data Mining.

Science.gov (United States)

Miller, L. L.; Honavar, Vasant; Barta, Tom

1997-01-01

Describes an extensible object-oriented view system that supports the integration of both structured and unstructured data sources in either the multidatabase or data warehouse environment. Discusses related work and data mining issues. (AEF)
A way toward analyzing high-content bioimage data by means of semantic annotation and visual data mining

Science.gov (United States)

Herold, Julia; Abouna, Sylvie; Zhou, Luxian; Pelengaris, Stella; Epstein, David B. A.; Khan, Michael; Nattkemper, Tim W.

2009-02-01

In the last years, bioimaging has turned from qualitative measurements towards a high-throughput and highcontent modality, providing multiple variables for each biological sample analyzed. We present a system which combines machine learning based semantic image annotation and visual data mining to analyze such new multivariate bioimage data. Machine learning is employed for automatic semantic annotation of regions of interest. The annotation is the prerequisite for a biological object-oriented exploration of the feature space derived from the image variables. With the aid of visual data mining, the obtained data can be explored simultaneously in the image as well as in the feature domain. Especially when little is known of the underlying data, for example in the case of exploring the effects of a drug treatment, visual data mining can greatly aid the process of data evaluation. We demonstrate how our system is used for image evaluation to obtain information relevant to diabetes study and screening of new anti-diabetes treatments. Cells of the Islet of Langerhans and whole pancreas in pancreas tissue samples are annotated and object specific molecular features are extracted from aligned multichannel fluorescence images. These are interactively evaluated for cell type classification in order to determine the cell number and mass. Only few parameters need to be specified which makes it usable also for non computer experts and allows for high-throughput analysis.
WEKA-G: Parallel data mining on computational grids

Directory of Open Access Journals (Sweden)

PIMENTA, A.

2009-12-01

Full Text Available Data mining is a technology that can extract useful information from large amounts of data. However, mining a database often requires a high computational power. To resolve this problem, this paper presents a tool (Weka-G, which runs in parallel algorithms used in the mining process data. As the environment for doing so, we use a computational grid by adding several features within a WAN.
Application and Exploration of Big Data Mining in Clinical Medicine

Science.gov (United States)

Zhang, Yue; Guo, Shu-Li; Han, Li-Na; Li, Tie-Ling

2016-01-01

Objective: To review theories and technologies of big data mining and their application in clinical medicine. Data Sources: Literatures published in English or Chinese regarding theories and technologies of big data mining and the concrete applications of data mining technology in clinical medicine were obtained from PubMed and Chinese Hospital Knowledge Database from 1975 to 2015. Study Selection: Original articles regarding big data mining theory/technology and big data mining's application in the medical field were selected. Results: This review characterized the basic theories and technologies of big data mining including fuzzy theory, rough set theory, cloud theory, Dempster–Shafer theory, artificial neural network, genetic algorithm, inductive learning theory, Bayesian network, decision tree, pattern recognition, high-performance computing, and statistical analysis. The application of big data mining in clinical medicine was analyzed in the fields of disease risk assessment, clinical decision support, prediction of disease development, guidance of rational use of drugs, medical management, and evidence-based medicine. Conclusion: Big data mining has the potential to play an important role in clinical medicine. PMID:26960378
Usage of Data Mining at Financial Decision Making

Directory of Open Access Journals (Sweden)

Levent BORAN

2014-06-01

Full Text Available The knowledge age requires controlling every kind of information. Recognition of patterns in data may provide previously unknown and useful information that can provide competitive advantages. If related techniques are applied on financial statements, it is possible to acquire valuable information about companies’ financial situations. It is considered that data mining could be an alternative of common financial analysis techniques such as vertical analysis, horizontal analysis, trend analysis and ratio analysis. Against existing financial analysis methods, data mining provides some advantages, which are ability of manipulation of huge data and competence of obtaining previously unknown information. There exist two major constraints of data mining implementation that are lack of experts on both data mining and related domains and cost of computer software and hardware used.
Fuzzy linear model for production optimization of mining systems with multiple entities

Science.gov (United States)

Vujic, Slobodan; Benovic, Tomo; Miljanovic, Igor; Hudej, Marjan; Milutinovic, Aleksandar; Pavlovic, Petar

2011-12-01

Planning and production optimization within multiple mines or several work sites (entities) mining systems by using fuzzy linear programming (LP) was studied. LP is the most commonly used operations research methods in mining engineering. After the introductory review of properties and limitations of applying LP, short reviews of the general settings of deterministic and fuzzy LP models are presented. With the purpose of comparative analysis, the application of both LP models is presented using the example of the Bauxite Basin Niksic with five mines. After the assessment, LP is an efficient mathematical modeling tool in production planning and solving many other single-criteria optimization problems of mining engineering. After the comparison of advantages and deficiencies of both deterministic and fuzzy LP models, the conclusion presents benefits of the fuzzy LP model but is also stating that seeking the optimal plan of production means to accomplish the overall analysis that will encompass the LP model approaches.
LEGS data acquisition facility

International Nuclear Information System (INIS)

LeVine, M.J.

1985-01-01

The data acquisition facility for the LEGS medium energy photonuclear beam line is composed of an auxiliary crate controller (ACC) acting as a front-end processor, loosely coupled to a time-sharing host computer based on a UNIX-like environment. The ACC services all real-time demands in the CAMAC crate: it responds to LAMs generated by data acquisition modules, to keyboard commands, and it refreshes the graphics display at frequent intervals. The host processor is needed only for printing histograms and recording event buffers on magnetic tape. The host also provides the environment for software development. The CAMAC crate is interfaced by a VERSAbus CAMAC branch driver
Short-term scheduling of an open-pit mine with multiple objectives

Science.gov (United States)

Blom, Michelle; Pearce, Adrian R.; Stuckey, Peter J.

2017-05-01

This article presents a novel algorithm for the generation of multiple short-term production schedules for an open-pit mine, in which several objectives, of varying priority, characterize the quality of each solution. A short-term schedule selects regions of a mine site, known as 'blocks', to be extracted in each week of a planning horizon (typically spanning 13 weeks). Existing tools for constructing these schedules use greedy heuristics, with little optimization. To construct a single schedule in which infrastructure is sufficiently utilized, with production grades consistently close to a desired target, a planner must often run these heuristics many times, adjusting parameters after each iteration. A planner's intuition and experience can evaluate the relative quality and mineability of different schedules in a way that is difficult to automate. Of interest to a short-term planner is the generation of multiple schedules, extracting available ore and waste in varying sequences, which can then be manually compared. This article presents a tool in which multiple, diverse, short-term schedules are constructed, meeting a range of common objectives without the need for iterative parameter adjustment.
Decision mining revisited - Discovering overlapping rules

NARCIS (Netherlands)

Mannhardt, Felix; De Leoni, Massimiliano; Reijers, Hajo A.; Van Der Aalst, Wil M P

2016-01-01

Decision mining enriches process models with rules underlying decisions in processes using historical process execution data. Choices between multiple activities are specified through rules defined over process data. Existing decision mining methods focus on discovering mutually-exclusive rules,
Decision Mining Revisited - Discovering Overlapping Rules

NARCIS (Netherlands)

Mannhardt, F.; De Leoni, M.; Reijers, H.A.; van der Aalst, W.M.P.; Nurcan, S.; Soffer, P.; Bajec, M.; Eder, J.

2016-01-01

Decision mining enriches process models with rules underlying decisions in processes using historical process execution data. Choices between multiple activities are specified through rules defined over process data. Existing decision mining methods focus on discovering mutually-exclusive rules,
A Data Preparation Methodology in Data Mining Applied to Mortality Population Databases.

Science.gov (United States)

Pérez, Joaquín; Iturbide, Emmanuel; Olivares, Víctor; Hidalgo, Miguel; Martínez, Alicia; Almanza, Nelva

2015-11-01

It is known that the data preparation phase is the most time consuming in the data mining process, using up to 50% or up to 70% of the total project time. Currently, data mining methodologies are of general purpose and one of their limitations is that they do not provide a guide about what particular task to develop in a specific domain. This paper shows a new data preparation methodology oriented to the epidemiological domain in which we have identified two sets of tasks: General Data Preparation and Specific Data Preparation. For both sets, the Cross-Industry Standard Process for Data Mining (CRISP-DM) is adopted as a guideline. The main contribution of our methodology is fourteen specialized tasks concerning such domain. To validate the proposed methodology, we developed a data mining system and the entire process was applied to real mortality databases. The results were encouraging because it was observed that the use of the methodology reduced some of the time consuming tasks and the data mining system showed findings of unknown and potentially useful patterns for the public health services in Mexico.
The meteorological data acquisition system

International Nuclear Information System (INIS)

Bouharrour, S.; Thomas, P.

1975-07-01

The 200 m meteorological tower of the Karlsruhe Nuclear Research Center has been equipped with 45 instruments measuring the meteorological parameters near the ground level. Frequent inquiry of the instruments implies data acquisition with on-line data reduction. This task is fulfilled by some peripheral units controlled by a PDP-8/I. This report presents details of the hardware configuration and a short description of the software configuration of the meteorological data acquisition system. The report also serves as an instruction for maintenance and repair work to be carried out at the system. (orig.) [de
Spatiotemporal Data Mining: A Computational Perspective

Directory of Open Access Journals (Sweden)

Shashi Shekhar

2015-10-01

Full Text Available Explosive growth in geospatial and temporal data as well as the emergence of new technologies emphasize the need for automated discovery of spatiotemporal knowledge. Spatiotemporal data mining studies the process of discovering interesting and previously unknown, but potentially useful patterns from large spatiotemporal databases. It has broad application domains including ecology and environmental management, public safety, transportation, earth science, epidemiology, and climatology. The complexity of spatiotemporal data and intrinsic relationships limits the usefulness of conventional data science techniques for extracting spatiotemporal patterns. In this survey, we review recent computational techniques and tools in spatiotemporal data mining, focusing on several major pattern families: spatiotemporal outlier, spatiotemporal coupling and tele-coupling, spatiotemporal prediction, spatiotemporal partitioning and summarization, spatiotemporal hotspots, and change detection. Compared with other surveys in the literature, this paper emphasizes the statistical foundations of spatiotemporal data mining and provides comprehensive coverage of computational approaches for various pattern families. ISPRS Int. J. Geo-Inf. 2015, 4 2307 We also list popular software tools for spatiotemporal data analysis. The survey concludes with a look at future research needs.
Data Acquisition System On Beta Installation

International Nuclear Information System (INIS)

Abtokhi, Ahmad; Nurhanan; Sudarno; Sumarno, Edy

2000-01-01

Data acquisition system is needed on every installation. This is important used to monitoring and processing data to get information desired. This system applied to β installation which is facility to carry out experiments on accident condition like as reflooding phenomena in test section. The 16 exp.th channel data acquisition system is drived by ADC 0804 and programme application DELPHI
Monitoring of the mercury mining site Almadén implementing remote sensing technologies.

Science.gov (United States)

Schmid, Thomas; Rico, Celia; Rodríguez-Rastrero, Manuel; José Sierra, María; Javier Díaz-Puente, Fco; Pelayo, Marta; Millán, Rocio

2013-08-01

The Almadén area in Spain has a long history of mercury mining with prolonged human-induced activities that are related to mineral extraction and metallurgical processes before the closure of the mines and a more recent post period dominated by projects that reclaim the mine dumps and tailings and recuperating the entire mining area. Furthermore, socio-economic alternatives such as crop cultivation, livestock breeding and tourism are increasing in the area. Up till now, only scattered information on these activities is available from specific studies. However, improved acquisition systems using satellite borne data in the last decades opens up new possibilities to periodically study an area of interest. Therefore, comparing the influence of these activities on the environment and monitoring their impact on the ecosystem vastly improves decision making for the public policy makers to implement appropriate land management measures and control environmental degradation. The objective of this work is to monitor environmental changes affected by human-induced activities within the Almadén area occurring before, during and after the mine closure over a period of nearly three decades. To achieve this, data from numerous sources at different spatial scales and time periods are implemented into a methodology based on advanced remote sensing techniques. This includes field spectroradiometry measurements, laboratory analyses and satellite borne data of different surface covers to detect land cover and use changes throughout the mining area. Finally, monitoring results show that the distribution of areas affected by mercury mining is rapidly diminishing since activities ceased and that rehabilitated mining areas form a new landscape. This refers to mine tailings that have been sealed and revegetated as well as an open pit mine that has been converted to an "artificial" lake surface. Implementing a methodology based on remote sensing techniques that integrate data from
Using Data Mining to Teach Applied Statistics and Correlation

Science.gov (United States)

Hartnett, Jessica L.

2016-01-01

This article describes two class activities that introduce the concept of data mining and very basic data mining analyses. Assessment data suggest that students learned some of the conceptual basics of data mining, understood some of the ethical concerns related to the practice, and were able to perform correlations via the Statistical Package for…

Quantification of Lysine Acetylation and Succinylation Stoichiometry in Proteins Using Mass Spectrometric Data-Independent Acquisitions (SWATH)

Science.gov (United States)

Meyer, Jesse G.; D'Souza, Alexandria K.; Sorensen, Dylan J.; Rardin, Matthew J.; Wolfe, Alan J.; Gibson, Bradford W.; Schilling, Birgit

2016-11-01

Post-translational modification of lysine residues by NƐ-acylation is an important regulator of protein function. Many large-scale protein acylation studies have assessed relative changes of lysine acylation sites after antibody enrichment using mass spectrometry-based proteomics. Although relative acylation fold-changes are important, this does not reveal site occupancy, or stoichiometry, of individual modification sites, which is critical to understand functional consequences. Recently, methods for determining lysine acetylation stoichiometry have been proposed based on ratiometric analysis of endogenous levels to those introduced after quantitative per-acetylation of proteins using stable isotope-labeled acetic anhydride. However, in our hands, we find that these methods can overestimate acetylation stoichiometries because of signal interferences when endogenous levels of acylation are very low, which is especially problematic when using MS1 scans for quantification. In this study, we sought to improve the accuracy of determining acylation stoichiometry using data-independent acquisition (DIA). Specifically, we use SWATH acquisition to comprehensively collect both precursor and fragment ion intensity data. The use of fragment ions for stoichiometry quantification not only reduces interferences but also allows for determination of site-level stoichiometry from peptides with multiple lysine residues. We also demonstrate the novel extension of this method to measurements of succinylation stoichiometry using deuterium-labeled succinic anhydride. Proof of principle SWATH acquisition studies were first performed using bovine serum albumin for both acetylation and succinylation occupancy measurements, followed by the analysis of more complex samples of E. coli cell lysates. Although overall site occupancy was low (<1%), some proteins contained lysines with relatively high acetylation occupancy.
High speed data acquisition

International Nuclear Information System (INIS)

Cooper, P.S.

1997-07-01

A general introduction to high speed data acquisition system techniques in modern particle physics experiments is given. Examples are drawn from the SELEX(E78 1) high statistics charmed baryon production and decay experiment now taking data at Fermilab
Data Mining at NASA: From Theory to Applications

Science.gov (United States)

Srivastava, Ashok N.

2009-01-01

This slide presentation demonstrates the data mining/machine learning capabilities of NASA Ames and Intelligent Data Understanding (IDU) group. This will encompass the work done recently in the group by various group members. The IDU group develops novel algorithms to detect, classify, and predict events in large data streams for scientific and engineering systems. This presentation for Knowledge Discovery and Data Mining 2009 is to demonstrate the data mining/machine learning capabilities of NASA Ames and IDU group. This will encompass the work done re cently in the group by various group members.
Data mining for the social sciences an introduction

CERN Document Server

Attewell, Paul

2015-01-01

We live in a world of big data: the amount of information collected on human behavior each day is staggering, and exponentially greater than at any time in the past. Additionally, powerful algorithms are capable of churning through seas of data to uncover patterns. Providing a simple and accessible introduction to data mining, Paul Attewell and David B. Monaghan discuss how data mining substantially differs from conventional statistical modeling familiar to most social scientists. The authors also empower social scientists to tap into these new resources and incorporate data mining
Privacy Preserving Distributed Data Mining

Data.gov (United States)

National Aeronautics and Space Administration — Distributed data mining from privacy-sensitive multi-party data is likely to play an important role in the next generation of integrated vehicle health monitoring...
AGS BOOSTER BEAM POSITION, TUNE, AND LONGITUDINAL PROFILE DATA ACQUISITION SYSTEM

International Nuclear Information System (INIS)

BROWN, K.A.; AHRENS, L.; SEVERINO, F; SMITH, K.; WILINSKI, M

2003-01-01

In this paper we will describe a data acquisition system designed and developed for the AGS Booster. The system was motivated by the need to get high quality beam diagnostics from the AGS Booster. This was accomplished by locating the electronics and digital data acquisition close to the Booster ring, to minimize loss of bandwidth in the original signals. In addition we had to develop the system rapidly and at a low cost. The system consists of a Lecroy digital oscilloscope which is interfaced through a National Instruments LabView(trademark) server application, developed for this project. This allows multiple client applications to time share the scope without interfering with each other. We will present a description of the system design along with example clients that we have implemented
Statistical and Machine-Learning Data Mining Techniques for Better Predictive Modeling and Analysis of Big Data

CERN Document Server

Ratner, Bruce

2011-01-01

The second edition of a bestseller, Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data is still the only book, to date, to distinguish between statistical data mining and machine-learning data mining. The first edition, titled Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data, contained 17 chapters of innovative and practical statistical data mining techniques. In this second edition, renamed to reflect the increased coverage of machine-learning data mining techniques, the author has
Improving diagnostic accuracy using agent-based distributed data mining system.

Science.gov (United States)

Sridhar, S

2013-09-01

The use of data mining techniques to improve the diagnostic system accuracy is investigated in this paper. The data mining algorithms aim to discover patterns and extract useful knowledge from facts recorded in databases. Generally, the expert systems are constructed for automating diagnostic procedures. The learning component uses the data mining algorithms to extract the expert system rules from the database automatically. Learning algorithms can assist the clinicians in extracting knowledge automatically. As the number and variety of data sources is dramatically increasing, another way to acquire knowledge from databases is to apply various data mining algorithms that extract knowledge from data. As data sets are inherently distributed, the distributed system uses agents to transport the trained classifiers and uses meta learning to combine the knowledge. Commonsense reasoning is also used in association with distributed data mining to obtain better results. Combining human expert knowledge and data mining knowledge improves the performance of the diagnostic system. This work suggests a framework of combining the human knowledge and knowledge gained by better data mining algorithms on a renal and gallstone data set.
Data acquisition for experiments with multi-detector arrays

Indian Academy of Sciences (India)

Experiments with multi-detector arrays have special requirements and place higher demands on computer data acquisition systems. In this contribution we discuss data acquisition systems with special emphasis on multi-detector arrays and in particular we describe a new data acquisition system, AMPS which we have ...
PROGRAMS WITH DATA MINING CAPABILITIES

Directory of Open Access Journals (Sweden)

Ciobanu Dumitru

2012-03-01

Full Text Available The fact that the Internet has become a commodity in the world has created a framework for anew economy. Traditional businesses migrate to this new environment that offers many features and options atrelatively low prices. However competitiveness is fierce and successful Internet business is tied to rigorous use of allavailable information. The information is often hidden in data and for their retrieval is necessary to use softwarecapable of applying data mining algorithms and techniques. In this paper we want to review some of the programswith data mining capabilities currently available in this area.We also propose some classifications of this softwareto assist those who wish to use such software.
Front-end data processing the SLD data acquisition system

International Nuclear Information System (INIS)

Nielsen, B.S.

1986-07-01

The data acquisition system for the SLD detector will make extensive use of parallel at the front-end level. Fastbus acquisition modules are being built with powerful processing capabilities for calibration, data reduction and further pre-processing of the large amount of analog data handled by each module. This paper describes the read-out electronics chain and data pre-processing system adapted for most of the detector channels, exemplified by the central drift chamber waveform digitization and processing system
Five channel data acquisition system for tracer studies

International Nuclear Information System (INIS)

Narender Reddy, J.; Dhananjay Reddy, Y.; Dheeraj Reddy, J.

2001-01-01

Radioactive tracers are being used by many modern industries for trouble shooting, process control/quality control and optimization in the process plants. A five channel data acquisition system which has five independent scintillation detector based channels for data acquisition has been developed and made available. This system can be used for tracer studies involving Mean residence time, Resident time distribution and other similar parameters involving tracer movement. System developed can acquire data with dwell times ranging from 10 m sec to 100 sec into each channel and has a capacity to acquire data into 10K channels. Each channel electronics, has a 1x1 NaI Scintillation Detector probe, HV, AMP SCA, micro-controller based data acquisition card with independent dot matrix LCD display for visualization. Extensive use of serial bus (I 2 C, microwire) compatible devices has been incorporated in the design. Data acquisition is initiated simultaneously into all the channels. System design permits delayed/prompt data acquisition selectively. Dual counter switching technique has been employed to achieve faster dwell times for data acquisition. (author)
The Chateau de Cristal data acquisition system

International Nuclear Information System (INIS)

Villard, M.M.

1987-05-01

This data acquisition system is built on several dedicated data transfer busses: ADC data readout through the FERA bus, parallel data processing in two VME crates. High data rates and selectivities are performed via this acquisition structure and new developed processing units. The system modularity allows various experiments with additional detectors
Data Mining Solutions for the Business Environment

OpenAIRE

Ruxandra-Stefania PETRE

2013-01-01

Over the past years, data mining became a matter of considerable importance due to the large amounts of data available in the applications belonging to various domains. Data mining, a dynamic and fast-expanding field, that applies advanced data analysis techniques, from statistics, machine learning, database systems or artificial intelligence, in order to discover relevant patterns, trends and relations contained within the data, information impossible to observe using other techniques. The p...
Data Analysis and Data Mining: Current Issues in Biomedical Informatics

Science.gov (United States)

Bellazzi, Riccardo; Diomidous, Marianna; Sarkar, Indra Neil; Takabayashi, Katsuhiko; Ziegler, Andreas; McCray, Alexa T.

2011-01-01

Summary Background Medicine and biomedical sciences have become data-intensive fields, which, at the same time, enable the application of data-driven approaches and require sophisticated data analysis and data mining methods. Biomedical informatics provides a proper interdisciplinary context to integrate data and knowledge when processing available information, with the aim of giving effective decision-making support in clinics and translational research. Objectives To reflect on different perspectives related to the role of data analysis and data mining in biomedical informatics. Methods On the occasion of the 50th year of Methods of Information in Medicine a symposium was organized, that reflected on opportunities, challenges and priorities of organizing, representing and analysing data, information and knowledge in biomedicine and health care. The contributions of experts with a variety of backgrounds in the area of biomedical data analysis have been collected as one outcome of this symposium, in order to provide a broad, though coherent, overview of some of the most interesting aspects of the field. Results The paper presents sections on data accumulation and data-driven approaches in medical informatics, data and knowledge integration, statistical issues for the evaluation of data mining models, translational bioinformatics and bioinformatics aspects of genetic epidemiology. Conclusions Biomedical informatics represents a natural framework to properly and effectively apply data analysis and data mining methods in a decision-making context. In the future, it will be necessary to preserve the inclusive nature of the field and to foster an increasing sharing of data and methods between researchers. PMID:22146916
Cultural Context In Process Of Mining Data From Social Media – Recommendations Based On Literature Review

Directory of Open Access Journals (Sweden)

Joanna Michalak

2017-05-01

Full Text Available Social media is nothing else than a modern communication channel that carry a lot of advantages, such as their reach or range. Social media has such a big power of its reach that a single post, tweet, or "broad" start to matter globally. With globalization, we have seen an increase in usage of social media everywhere. This means that communication is being conducted across the borders or different countries, continents or even cultures. It is an desirable effect, however the social media user across the world differs in respect to their culture and data shows that significant differences exist in a way people in the world social media. However, in order to be well prepared to dig in social media, the question should be post whether the cultural context affects the activity of users. If so, it is appropriate to prepare data filters to include some specific criteria. In first part authors apply the Cross - Industry Standard Process for Data Mining (CRISP-DM in social media data to specify the process of data analysis. Second part focuses on recommendations about cultural context in mining social media.
Analysis of post-mining excavations as places for municipal waste

Directory of Open Access Journals (Sweden)

Górniak-Zimroz Justyna

2018-01-01

Full Text Available Waste management planning is an interdisciplinary task covering a wide range of issues including costs, legal requirements, spatial planning, environmental protection, geography, demographics, and techniques used in collecting, transporting, processing and disposing of waste. Designing and analyzing this issue is difficult and requires the use of advanced analysis methods and tools available in GIS geographic information systems containing readily available graphical and descriptive databases, data analysis tools providing expert decision support while selecting the best-designed alternative, and simulation models that allow the user to simulate many variants of waste management together with graphical visualization of the results of performed analyzes. As part of the research study, there have been works undertaken concerning the use of multi-criteria data analysis in waste management in areas located in southwestern Poland. These works have proposed the inclusion in waste management of post-mining excavations as places for the final or temporary collection of waste assessed in terms of their suitability with the tools available in GIS systems.
Earthworms drive succession of both plant and Collembola communities in post-mining sites

Czech Academy of Sciences Publication Activity Database

Mudrák, Ondřej; Uteseny, Karoline; Frouz, Jan

2016-01-01

Roč. 18, April (2016), EGU2016-8464 ISSN 1607-7962. [European Geosciences Union General Assembly 2016. 17.04.2016-22.04.2016, Vienna] Institutional support: RVO:60077344 ; RVO:67985939 Keywords : earthworms * succession * plant communities * Collembola communities * post-mining sites Subject RIV: DF - Soil Science
Multiple-Ring Digital Communication Network

Science.gov (United States)

Kirkham, Harold

1992-01-01

Optical-fiber digital communication network to support data-acquisition and control functions of electric-power-distribution networks. Optical-fiber links of communication network follow power-distribution routes. Since fiber crosses open power switches, communication network includes multiple interconnected loops with occasional spurs. At each intersection node is needed. Nodes of communication network include power-distribution substations and power-controlling units. In addition to serving data acquisition and control functions, each node acts as repeater, passing on messages to next node(s). Multiple-ring communication network operates on new AbNET protocol and features fiber-optic communication.
Multipass mining sequence room closures: In situ data report

International Nuclear Information System (INIS)

Munson, D.E.; Jones, R.L.; Northrop-Salazar, C.L.; Woerner, S.J.

1992-12-01

During the construction of the Thermal/Structural In Situ Test Rooms at the Waste Isolation Pilot Plant (WIPP) facility, measurements of the salt displacements were obtained at very early times, essentially concurrent with the mining activity. This was accomplished by emplacing manually read closure gage stations directly at the mining face, actually between the face and the mining machine, immediately upon mining of the intended gage location. Typically, these mining sequence closure measurements were taken within one hour of mining of the location and within one meter of the mining face. Readings were taken at these gage stations as the multipass mining continued, with the gage station reestablished as each successive mining pass destroyed the earlier gage points. Data reduction yields the displacement history during the mining operation. These early mining sequence closure data, when combined with the later data of the permanently emplaced closure gages, gives the total time-dependent closure displacements of the test rooms. This complete closure history is an essential part of assuring that the in situ test databases will provide an adequate basis for validation of the predictive technology of salt creep behavior, as required by the WIPP technology development program for disposal of radioactive waste in bedded salt

Educational data mining applications and trends

CERN Document Server

2014-01-01

This book is devoted to the Educational Data Mining arena. It highlights works that show relevant proposals, developments, and achievements that shape trends and inspire future research. After a rigorous revision process sixteen manuscripts were accepted and organized into four parts as follows: · Profile: The first part embraces three chapters oriented to: 1) describe the nature of educational data mining (EDM); 2) describe how to pre-process raw data to facilitate data mining (DM); 3) explain how EDM supports government policies to enhance education. · Student modeling: The second part contains five chapters concerned with: 4) explore the factors having an impact on the students academic success; 5) detect student's personality and behaviors in an educational game; 6) predict students performance to adjust content and strategies; 7) identify students who will most benefit from tutor support; 8) hypothesize the student answer correctness based on eye metrics and mouse click. · As...
Using multi-relational data mining to discriminate blended therapy efficiency on patients based on log data

Directory of Open Access Journals (Sweden)

Artur Rocha

2018-06-01

Full Text Available Introduction: Clinical trials of blended Internet-based treatments deliver a wealth of data from various sources, such as self-report questionnaires, diagnostic interviews, treatment platform log files and Ecological Momentary Assessments (EMA. Mining these complex data for clinically relevant patterns is a daunting task for which no definitive best method exists. In this paper, we explore the expressive power of the multi-relational Inductive Logic Programming (ILP data mining approach, using combined trial data of the EU E-COMPARED depression trial. Methods: We explored the capability of ILP to handle and combine (implicit multiple relationships in the E-COMPARED data. This data set has the following features that favor ILP analysis: 1 Time reasoning is involved; 2 there is a reasonable amount of explicit useful relations to be analyzed; 3 ILP is capable of building comprehensible models that might be perceived as putative explanations by domain experts; 4 both numerical and statistical models may coexist within ILP models if necessary. In our analyses, we focused on scores of the PHQ-8 self-report questionnaire (which taps depressive symptom severity, and on EMA of mood and various other clinically relevant factors. Both measures were administered during treatment, which lasted between 9 to 16 weeks. Results: E-COMPARED trial data revealed different individual improvement patterns: PHQ-8 scores suggested that some individuals improved quickly during the first weeks of the treatment, while others improved at a (much slower pace, or not at all. Combining self-reported Ecological Momentary Assessments (EMA, PHQ-8 scores and log data about the usage of the ICT4D platform in the context of blended care, we set out to unveil possible causes for these different trajectories. Discussion: This work complements other studies into alternative data mining approaches to E-COMPARED trial data analysis, which are all aimed to identify clinically
Data mining in healthcare: decision making and precision

Directory of Open Access Journals (Sweden)

Ionuţ ŢĂRANU

2016-05-01

Full Text Available The trend of application of data mining in healthcare today is increased because the health sector is rich with information and data mining has become a necessity. Healthcare organizations generate and collect large volumes of information to a daily basis. Use of information technology enables automation of data mining and knowledge that help bring some interesting patterns which means eliminating manual tasks and easy data extraction directly from electronic records, electronic transfer system that will secure medical records, save lives and reduce the cost of medical services as well as enabling early detection of infectious diseases on the basis of advanced data collection. Data mining can enable healthcare organizations to anticipate trends in the patient's medical condition and behaviour proved by analysis of prospects different and by making connections between seemingly unrelated information. The raw data from healthcare organizations are voluminous and heterogeneous. It needs to be collected and stored in organized form and their integration allows the formation unite medical information system. Data mining in health offers unlimited possibilities for analyzing different data models less visible or hidden to common analysis techniques. These patterns can be used by healthcare practitioners to make forecasts, put diagnoses, and set treatments for patients in healthcare organizations.
Large Data Set Mining

NARCIS (Netherlands)

Leemans, I.B.; Broomhall, Susan

2017-01-01

Digital emotion research has yet to make history. Until now large data set mining has not been a very active ﬁeld of research in early modern emotion studies. This is indeed surprising since ﬁrst, the early modern ﬁeld has such rich, copyright-free, digitized data sets and second, emotion studies
Analyzing large gene expression and methylation data profiles using StatBicRM: statistical biclustering-based rule mining.

Directory of Open Access Journals (Sweden)

Ujjwal Maulik

Full Text Available Microarray and beadchip are two most efficient techniques for measuring gene expression and methylation data in bioinformatics. Biclustering deals with the simultaneous clustering of genes and samples. In this article, we propose a computational rule mining framework, StatBicRM (i.e., statistical biclustering-based rule mining to identify special type of rules and potential biomarkers using integrated approaches of statistical and binary inclusion-maximal biclustering techniques from the biological datasets. At first, a novel statistical strategy has been utilized to eliminate the insignificant/low-significant/redundant genes in such way that significance level must satisfy the data distribution property (viz., either normal distribution or non-normal distribution. The data is then discretized and post-discretized, consecutively. Thereafter, the biclustering technique is applied to identify maximal frequent closed homogeneous itemsets. Corresponding special type of rules are then extracted from the selected itemsets. Our proposed rule mining method performs better than the other rule mining algorithms as it generates maximal frequent closed homogeneous itemsets instead of frequent itemsets. Thus, it saves elapsed time, and can work on big dataset. Pathway and Gene Ontology analyses are conducted on the genes of the evolved rules using David database. Frequency analysis of the genes appearing in the evolved rules is performed to determine potential biomarkers. Furthermore, we also classify the data to know how much the evolved rules are able to describe accurately the remaining test (unknown data. Subsequently, we also compare the average classification accuracy, and other related factors with other rule-based classifiers. Statistical significance tests are also performed for verifying the statistical relevance of the comparative results. Here, each of the other rule mining methods or rule-based classifiers is also starting with the same post
Analyzing large gene expression and methylation data profiles using StatBicRM: statistical biclustering-based rule mining.

Science.gov (United States)

Maulik, Ujjwal; Mallik, Saurav; Mukhopadhyay, Anirban; Bandyopadhyay, Sanghamitra

2015-01-01

Microarray and beadchip are two most efficient techniques for measuring gene expression and methylation data in bioinformatics. Biclustering deals with the simultaneous clustering of genes and samples. In this article, we propose a computational rule mining framework, StatBicRM (i.e., statistical biclustering-based rule mining) to identify special type of rules and potential biomarkers using integrated approaches of statistical and binary inclusion-maximal biclustering techniques from the biological datasets. At first, a novel statistical strategy has been utilized to eliminate the insignificant/low-significant/redundant genes in such way that significance level must satisfy the data distribution property (viz., either normal distribution or non-normal distribution). The data is then discretized and post-discretized, consecutively. Thereafter, the biclustering technique is applied to identify maximal frequent closed homogeneous itemsets. Corresponding special type of rules are then extracted from the selected itemsets. Our proposed rule mining method performs better than the other rule mining algorithms as it generates maximal frequent closed homogeneous itemsets instead of frequent itemsets. Thus, it saves elapsed time, and can work on big dataset. Pathway and Gene Ontology analyses are conducted on the genes of the evolved rules using David database. Frequency analysis of the genes appearing in the evolved rules is performed to determine potential biomarkers. Furthermore, we also classify the data to know how much the evolved rules are able to describe accurately the remaining test (unknown) data. Subsequently, we also compare the average classification accuracy, and other related factors with other rule-based classifiers. Statistical significance tests are also performed for verifying the statistical relevance of the comparative results. Here, each of the other rule mining methods or rule-based classifiers is also starting with the same post-discretized data
Applications of Geomatics in Surface Mining

Science.gov (United States)

Blachowski, Jan; Górniak-Zimroz, Justyna; Milczarek, Wojciech; Pactwa, Katarzyna

2017-12-01

In terms of method of extracting mineral from deposit, mining can be classified into: surface, underground, and borehole mining. Surface mining is a form of mining, in which the soil and the rock covering the mineral deposits are removed. Types of surface mining include mainly strip and open-cast methods, as well as quarrying. Tasks associated with surface mining of minerals include: resource estimation and deposit documentation, mine planning and deposit access, mine plant development, extraction of minerals from deposits, mineral and waste processing, reclamation and reclamation of former mining grounds. At each stage of mining, geodata describing changes occurring in space during the entire life cycle of surface mining project should be taken into consideration, i.e. collected, analysed, processed, examined, distributed. These data result from direct (e.g. geodetic) and indirect (i.e. remote or relative) measurements and observations including airborne and satellite methods, geotechnical, geological and hydrogeological data, and data from other types of sensors, e.g. located on mining equipment and infrastructure, mine plans and maps. Management of such vast sources and sets of geodata, as well as information resulting from processing, integrated analysis and examining such data can be facilitated with geomatic solutions. Geomatics is a discipline of gathering, processing, interpreting, storing and delivering spatially referenced information. Thus, geomatics integrates methods and technologies used for collecting, management, processing, visualizing and distributing spatial data. In other words, its meaning covers practically every method and tool from spatial data acquisition to distribution. In this work examples of application of geomatic solutions in surface mining on representative case studies in various stages of mine operation have been presented. These applications include: prospecting and documenting mineral deposits, assessment of land accessibility
Development of a catchment/landscape erosion prediction model (MINErosion 4) for post-mining landscapes in Central Queensland, Australia.

Science.gov (United States)

Khalifa, Ashraf; Yu, Bofu; Ghadiri, Hossain; Carroll, Chris; So, Hwat-Bing

2010-05-01

Open-cut coal mining in Central Queensland involves the breaking up of overburden that overlies the coal seams using explosives, followed by removal with draglines which results in the formation of extensive overburden spoil-piles with steep slopes at the angle of repose (approximately 75 % or 37o). These spoil-piles are found in long multiple rows, with heights of up to 60 or 70 m above the original landscapes. They are generally highly saline and dispersive and hence highly erosive. Legislation requires that these spoil-piles be rehabilitated into a stable self sustaining ecosystem with no off-site pollution. The first stage in the rehabilitation of these landscapes is the lowering of slopes to create a landscape that is stable against geotechnical failure and erosion. This is followed by revegetation generally with grasses as pioneer vegetation to further reduce erosion and a mixture of native shrubs and trees. Minimizing erosion and excessive on-site discharges of sediment into the working areas may result in the temporary cessation of mining operation with significant financial consequences, while off site discharges may breach the mining lease conditions. The average cost of rehabilitation is approximately 22,000 per ha. With more than 50,000 ha of such spoil-piles in Queensland at present, the total cost of rehabilitation facing the industry is very high. Most of this comprised the cost of reshaping the landscape, largely associated with the amount of material movement necessary to achieve the desired landscape. Since soil and spoil-piles vary greatly in their erodibilities, a hillslope erosion model MINErosion 3 (this conference) was developed to determine a cost effective combination of slope length, slope gradient and vegetation that will result in acceptable rates of erosion. This model was useful to determine the design parameters for the construction of a suitable post-mining landscape that meets the required erosion criteria. However, the mining
Design of data warehouse in teaching state based on OLAP and data mining

Science.gov (United States)

Zhou, Lijuan; Wu, Minhua; Li, Shuang

2009-04-01

The data warehouse and the data mining technology is one of information technology research hot topics. At present the data warehouse and the data mining technology in aspects and so on commercial, financial industry as well as enterprise's production, market marketing obtained the widespread application, but is relatively less in educational fields' application. Over the years, the teaching and management have been accumulating large amounts of data in colleges and universities, while the data can not be effectively used, in the light of social needs of the university development and the current status of data management, the establishment of data warehouse in university state, the better use of existing data, and on the basis dealing with a higher level of disposal --data mining are particularly important. In this paper, starting from the decision-making needs design data warehouse structure of university teaching state, and then through the design structure and data extraction, loading, conversion create a data warehouse model, finally make use of association rule mining algorithm for data mining, to get effective results applied in practice. Based on the data analysis and mining, get a lot of valuable information, which can be used to guide teaching management, thereby improving the quality of teaching and promoting teaching devotion in universities and enhancing teaching infrastructure. At the same time it can provide detailed, multi-dimensional information for universities assessment and higher education research.
Expressive power of an algebra for data mining

NARCIS (Netherlands)

Calders, T.; Lakshmanan, L.V.S.; Ng, R.T.; Paredaens, J.

2006-01-01

The relational data model has simple and clear foundations on which significant theoretical and systems research has flourished. By contrast, most research on data mining has focused on algorithmic issues. A major open question is: what's an appropriate foundation for data mining, which can
The data acquisition and interlock system for Tore Supra infrared imaging

International Nuclear Information System (INIS)

Moulin, D.; Balorin, C.; Buravand, Y.; Caulier, G.; Ducobu, L.; Guilhem, D.; Jouve, M.; Roche, H.

2003-01-01

The data acquisition for the infrared measurement system on Tore-Supra is a key element in ensuring the supervision of the new actively-cooled plasma facing components of the CIEL project. It will allow us to follow the thermal evolution of components of Tore-Supra, in particular the toroidal pumped limiter (LPT) (360 deg.-15 m long) and the five additional heating launchers. When fully installed, the infrared measurement system will be composed of 12 digital 16-bit infrared cameras. They cover a 100-1200 deg.C temperature range and each picture has a definition of 320x240 pixels with a 20 ms time resolution. The objectives of the data acquisition system is real-time recording and analysis of each view element for further post-pulse analysis in order to understand the physics phenomenon and ensure the supervision of the plasma facing components and also to be part of the global feedback control system of Tore Supra
A Data Mining Classification Approach for Behavioral Malware Detection

Directory of Open Access Journals (Sweden)

Monire Norouzi

2016-01-01

Full Text Available Data mining techniques have numerous applications in malware detection. Classification method is one of the most popular data mining techniques. In this paper we present a data mining classification approach to detect malware behavior. We proposed different classification methods in order to detect malware based on the feature and behavior of each malware. A dynamic analysis method has been presented for identifying the malware features. A suggested program has been presented for converting a malware behavior executive history XML file to a suitable WEKA tool input. To illustrate the performance efficiency as well as training data and test, we apply the proposed approaches to a real case study data set using WEKA tool. The evaluation results demonstrated the availability of the proposed data mining approach. Also our proposed data mining approach is more efficient for detecting malware and behavioral classification of malware can be useful to detect malware in a behavioral antivirus.
Data acquisition system for radiographic imaging

International Nuclear Information System (INIS)

Lanza, R.C.; Votano, J.R.; Russ, T.

1992-01-01

This patent describes a continuous data acquisition system for radiographic imaging without interrupting acquisition activity the acquisition system. It comprises at least two memory means for storing radiographic data from a radiation detector wherein each of the memory means having a plurality of addressable memory locations and each of the memory means are such that the locations of the memory means correspond to spatial locations in the radiation detector; logic control means for sensing radiographic data transmitted by the radiation detector, for selecting one of the memory means for storage of the data, for transferring data to the selected memory means, and for switching form one memory means to another memory means according to a predefined schedule and according to memory capacity level, the logic control means further comprising a logic device which receives data and increments the contents of locations in a memory means in response to such data; and interface control means for reading data from one or the other memory means when such memory means is not actively acquiring data such that data can be acquired continuously by the system
Data Acquisition and Mass Storage

Science.gov (United States)

Vande Vyvre, P.

2004-08-01

The experiments performed at supercolliders will constitute a new challenge in several disciplines of High Energy Physics and Information Technology. This will definitely be the case for data acquisition and mass storage. The microelectronics, communication, and computing industries are maintaining an exponential increase of the performance of their products. The market of commodity products remains the largest and the most competitive market of technology products. This constitutes a strong incentive to use these commodity products extensively as components to build the data acquisition and computing infrastructures of the future generation of experiments. The present generation of experiments in Europe and in the US already constitutes an important step in this direction. The experience acquired in the design and the construction of the present experiments has to be complemented by a large R&D effort executed with good awareness of industry developments. The future experiments will also be expected to follow major trends of our present world: deliver physics results faster and become more and more visible and accessible. The present evolution of the technologies and the burgeoning of GRID projects indicate that these trends will be made possible. This paper includes a brief overview of the technologies currently used for the different tasks of the experimental data chain: data acquisition, selection, storage, processing, and analysis. The major trends of the computing and networking technologies are then indicated with particular attention paid to their influence on the future experiments. Finally, the vision of future data acquisition and processing systems and their promise for future supercolliders is presented.
MPS Data Acquisition System

International Nuclear Information System (INIS)

Eiseman, S.E.; Miller, W.J.

1975-01-01

A description is given of the data acquisition system used with the multiparticle spectrometer facility at Brookhaven. Detailed information is provided on that part of the system which connects the detectors to the data handler; namely, the detector electronics, device controller, and device port optical isolator
Data mining concepts and techniques

CERN Document Server

Han, Jiawei

2005-01-01

Our ability to generate and collect data has been increasing rapidly. Not only are all of our business, scientific, and government transactions now computerized, but the widespread use of digital cameras, publication tools, and bar codes also generate data. On the collection side, scanned text and image platforms, satellite remote sensing systems, and the World Wide Web have flooded us with a tremendous amount of data. This explosive growth has generated an even more urgent need for new techniques and automated tools that can help us transform this data into useful information and knowledge.Like the first edition, voted the most popular data mining book by KD Nuggets readers, this book explores concepts and techniques for the discovery of patterns hidden in large data sets, focusing on issues relating to their feasibility, usefulness, effectiveness, and scalability. However, since the publication of the first edition, great progress has been made in the development of new data mining methods, systems, and app...
Integrating UNIX workstation into existing online data acquisition systems for Fermilab experiments

International Nuclear Information System (INIS)

Oleynik, G.

1991-03-01

With the availability of cost effective computing prior from multiple vendors of UNIX workstations, experiments at Fermilab are adding such computers to their VMS based online data acquisition systems. In anticipation of this trend, we have extended the software products available in our widely used VAXONLINE and PANDA data acquisition software systems, to provide support for integrating these workstations into existing distributed online systems. The software packages we are providing pave the way for the smooth migration of applications from the current Data Acquisition Host and Monitoring computers running the VMS operating systems, to UNIX based computers of various flavors. We report on software for Online Event Distribution from VAXONLINE and PANDA, integration of Message Reporting Facilities, and a framework under UNIX for experiments to monitor and view the raw event data produced at any level in their DA system. We have developed software that allows host UNIX computers to communicate with intelligent front-end embedded read-out controllers and processor boards running the pSOS operating system. Both RS-232 and Ethernet control paths are supported. This enables calibration and hardware monitoring applications to be migrated to these platforms. 6 refs., 5 figs
Data mining and business analytics with R

CERN Document Server

Ledolter, Johannes

2013-01-01

Collecting, analyzing, and extracting valuable information from a large amount of data requires easily accessible, robust, computational and analytical tools. Data Mining and Business Analytics with R utilizes the open source software R for the analysis, exploration, and simplification of large high-dimensional data sets. As a result, readers are provided with the needed guidance to model and interpret complicated data and become adept at building powerful models for prediction and classification. Highlighting both underlying concepts and practical computational skills, Data Mining
A data acquisition architecture for the SSC

International Nuclear Information System (INIS)

Partridge, R.

1990-01-01

An SSC data acquisition architecture applicable to high-p T detectors is described. The architecture is based upon a small set of design principles that were chosen to simplify communication between data acquisition elements while providing the required level of flexibility and performance. The architecture features an integrated system for data collection, event building, and communication with a large processing farm. The interface to the front end electronics system is also discussed. A set of design parameters is given for a data acquisition system that should meet the needs of high-p T detectors at the SSC
Data-acquisition systems

International Nuclear Information System (INIS)

Cyborski, D.R.; Teh, K.M.

1995-01-01

Up to now, DAPHNE, the data-acquisition system developed for ATLAS, was used routinely for experiments at ATLAS and the Dynamitron. More recently, the Division implemented 2 MSU/DAPHNE systems. The MSU/DAPHNE system is a hybrid data-acquisition system which combines the front-end of the Michigan State University (MSU) DA system with the traditional DAPHNE back-end. The MSU front-end is based on commercially available modules. This alleviates the problems encountered with the DAPHNE front-end which is based on custom designed electronics. The first MSU system was obtained for the APEX experiment and was used there successfully. A second MSU front-end, purchased as a backup for the APEX experiment, was installed as a fully-independent second MSU/DAPHNE system with the procurement of a DEC 3000 Alpha host computer, and was used successfully for data-taking in an experiment at ATLAS. Additional hardware for a third system was bought and will be installed. With the availability of 2 MSU/DAPHNE systems in addition to the existing APEX setup, it is planned that the existing DAPHNE front-end will be decommissioned

Data mining and knowledge discovery for big data methodologies, challenge and opportunities

CERN Document Server

2014-01-01

The field of data mining has made significant and far-reaching advances over the past three decades. Because of its potential power for solving complex problems, data mining has been successfully applied to diverse areas such as business, engineering, social media, and biological science. Many of these applications search for patterns in complex structural information. In biomedicine for example, modeling complex biological systems requires linking knowledge across many levels of science, from genes to disease. Further, the data characteristics of the problems have also grown from static to dynamic and spatiotemporal, complete to incomplete, and centralized to distributed, and grow in their scope and size (this is known as big data). The effective integration of big data for decision-making also requires privacy preservation. The contributions to this monograph summarize the advances of data mining in the respective fields. This volume consists of nine chapters that address subjects ranging from mining da...
Data acquisition for PLT

International Nuclear Information System (INIS)

Thompson, P.A.

1975-01-01

DA/PLT, the data acquisition system for the Princeton Large Torus (PLT) fusion research device, consists of a PDP-10 host computer, five satellite PDP-11s connected to the host by a special high-speed interface, miscellaneous other minicomputers and commercially supplied instruments, and much PPPL produced hardware. The software consists of the standard PDP-10 monitor with local modifications and the special systems and applications programs to customize the DA/PLT for the specific job of supporting data acquisition, analysis, display, and archiving, with concurrent off-line analysis, program development, and, in the background, general batch and timesharing. Some details of the over-all architecture are presented, along with a status report of the different PLT experiments being supported
Spatio-Temporal Data Mining for Location-Based Services

DEFF Research Database (Denmark)

Gidofalvi, Gyozo

. The objectives of the presented thesis are three-fold. First, to extend popular data mining methods to the spatio-temporal domain. Second, to demonstrate the usefulness of the extended methods and the derived knowledge in promising LBS examples. Finally, to eliminate privacy concerns in connection with spatio......-temporal data mining by devising systems for privacy-preserving location data collection and mining.......Location-Based Services (LBS) are continuously gaining popularity. Innovative LBSes integrate knowledge about the users into the service. Such knowledge can be derived by analyzing the location data of users. Such data contain two unique dimensions, space and time, which need to be analyzed...
Reverse time migration of multiples for OBS data

KAUST Repository

Zhang, Dongliang

2014-01-01

Reverse time migration of multiples (RTMM) is applied to OBS data with sparse receiver spacing. RTMM for OBS data unlike a marine streamer acquisition is implemented in the common receiver gathers (CRG) and provides a wider and denser illumination for each CRG than the conventional RTM of primaries. Hence, while the the conventional RTM image contains strong aliasing artifacts due to a sparser receiver interval, the RTMM image suffers from this artifacts less. This benefit of RTMM is demonstrated with numerical test on the Marmousi model for sparsely sampled OBS data.
Reverse time migration of multiples for OBS data

KAUST Repository

Zhang, Dongliang

2014-08-05

Reverse time migration of multiples (RTMM) is applied to OBS data with sparse receiver spacing. RTMM for OBS data unlike a marine streamer acquisition is implemented in the common receiver gathers (CRG) and provides a wider and denser illumination for each CRG than the conventional RTM of primaries. Hence, while the the conventional RTM image contains strong aliasing artifacts due to a sparser receiver interval, the RTMM image suffers from this artifacts less. This benefit of RTMM is demonstrated with numerical test on the Marmousi model for sparsely sampled OBS data.
76 FR 14637 - State Medicaid Fraud Control Units; Data Mining

Science.gov (United States)

2011-03-17

...] State Medicaid Fraud Control Units; Data Mining AGENCY: Office of Inspector General (OIG), HHS. ACTION... and analyzing State Medicaid claims data, known as data mining. To support and modernize MFCU efforts... (FFP) in the costs of defined data mining activities under specified conditions. In addition, we...
data mining in distributed database

International Nuclear Information System (INIS)

Ghunaim, A.A.A.

2007-01-01

as we march into the age of digital information, the collection and the storage of large quantities of data is increased, and the problem of data overload looms ominously ahead. it is estimated today that the volume of data stored by a company doubles every year but the amount of meaningful information is decreases rapidly. the ability to analyze and understand massive datasets lags far behind the ability to gather and store the data. the unbridled growth of data will inevitably lead to a situation in which it is increasingly difficult to access the desired information; it will always be like looking for a needle in a haystack, and where only the amount of hay will be growing all the time . so, a new generation of computational techniques and tools is required to analyze and understand the rapidly growing volumes of data . and, because the information technology (it) has become a strategic weapon in the modern life, it is needed to use a new decision support tools to be an international powerful competitor.data mining is one of these tools and its methods make it possible to extract decisive knowledge needed by an enterprise and it means that it concerned with inferring models from data , including statistical pattern recognition, applied statistics, machine learning , and neural networks. data mining is a tool for increasing productivity of people trying to build predictive models. data mining techniques have been applied successfully to several real world problem domains; but the application in the nuclear reactors field has only little attention . one of the main reasons, is the difficulty in obtaining the data sets
Web Mining of Hotel Customer Survey Data

Directory of Open Access Journals (Sweden)

Richard S. Segall

2008-12-01

Full Text Available This paper provides an extensive literature review and list of references on the background of web mining as applied specifically to hotel customer survey data. This research applies the techniques of web mining to actual text of written comments for hotel customers using Megaputer PolyAnalyst®. Web mining functionalities utilized include those such as clustering, link analysis, key word and phrase extraction, taxonomy, and dimension matrices. This paper provides screen shots of the web mining applications using Megaputer PolyAnalyst®. Conclusions and future directions of the research are presented.
Primary succession of soil rotifers in clays of brown coal post-mining dumps

Czech Academy of Sciences Publication Activity Database

Devetter, Miloslav; Frouz, J.

2011-01-01

Roč. 96, č. 2 (2011), s. 164-174 ISSN 1434-2944 R&D Projects: GA MŠk 2B08023 Institutional research plan: CEZ:AV0Z60660521 Keywords : soil rotifers * post mining dumps * primary succession Subject RIV: EH - Ecology, Behaviour Impact factor: 1.190, year: 2011
The Resource Manager the ATLAS Trigger and Data Acquisition System

CERN Document Server

AUTHOR|(INSPIRE)INSPIRE-00210579; The ATLAS collaboration; Avolio, Giuseppe; Lehmann Miotto, Giovanna; Soloviev, Igor

2017-01-01

The Resource Manager is one of the core components of the Data Acquisition system of the ATLAS experiment at the LHC. The Resource Manager marshals the right for applications to access resources which may exist in multiple but limited copies, in order to avoid conflicts due to program faults or operator errors. The access to resources is managed in a manner similar to what a lock manager would do in other software systems. All the available resources and their association to software processes are described in the Data Acquisition configuration database. The Resource Manager is queried about the availability of resources every time an application needs to be started. The Resource Manager’s design is based on a client-server model, hence it consists of two components: the Resource Manager “server” application and the “client” shared library. The Resource Manager server implements all the needed functionalities, while the Resource Manager client library provides remote access to the “server” (i.e....
Decision Mining Revisited – Discovering Overlapping Rules

NARCIS (Netherlands)

Mannhardt, F.; de Leoni, M.; Reijers, H.A.; van der Aalst, W.M.P.

2016-01-01

Decision mining enriches process models with rules underlying decisions in processes using historical process execution data. Choices between multiple activities are specified through rules defined over process data. Existing decision mining methods focus on discovering mutually-exclusive rules,
Analisis Data Lulusan dengan Data Mining untuk Mendukung Strategi Promosi Universitas Lancang Kuning

Directory of Open Access Journals (Sweden)

Elvira Asril

2015-11-01

Full Text Available Setiap perusahaan maupun organisasi yang ingin tetap bertahan perlu untuk menentukan strategi promosi yang tepat. Penentuan strategi promosi yang tepat akan dapat mengurangi biaya promosi dan mencapai sasaran promosi yang tepat. Salah satu cara yang dapat dilakukan untuk penentuan strategi promosi adalah dengan menggunakan teknik data mining. Teknik data mining yang digunakan dalam hal ini adalah dengan menggunakan algoritma Clustering K-Means. Clustering merupakan pengelompokkan record, observasi, atau kasus ke dalam kelas-kelas objek yang mirip. K-Means adalah metode klaster data non-hirarkis yang mencoba untuk membagi data ke dalam satu atau lebih klaster. Penelitian dilakukan dengan mengamati beberapa variabel penelitian yang sering dipertimbangkan oleh perguruan tinggi dalam menentukan sasaran promosinya yaitu asal sekolah, daerah, dan jurusan. Hasil penelitian ini adalah berupa pola menarik hasil data mining yang merupakan informasi penting untuk mendukung strategi promosi yang tepat dalam mendapatkan calon mahasiswa baru.Kata kunci: Data Mining, Clustering, K-Means Each company or organization that wants to survive needs to determine appropriate promotional strategies. Determination of appropriate promotional strategies will be able to reduce costs and achieve the goals the promotion of proper promotion. One way that can be done to determine campaign strategy is to use data mining techniques. Data mining techniques used in this case is to use a K-Means clustering algorithm. Clustering is the grouping of records, observation, or in the case of the object classes that are similar. K-Means is a method of non-hierarchical clustering of data that is trying to divide the data into one or more clusters. The study was conducted by observing some of the variables that are often considered by the college in determining the target of promotion that the school of origin, region, and department. Results of this study are interesting pattern of
AZ-101 Mixer Pump Demonstration Data Acquisition System and Gamma Cart Data Acquisition Control System Software Configuration Management Plan

International Nuclear Information System (INIS)

WHITE, D.A.

1999-01-01

This Software Configuration Management Plan (SCMP) provides the instructions for change control of the AZ1101 Mixer Pump Demonstration Data Acquisition System (DAS) and the Sludge Mobilization Cart (Gamma Cart) Data Acquisition and Control System (DACS)
The data acquisition system of ICT

International Nuclear Information System (INIS)

Gao Fuqiang; An Kang; Lu Hua; Cao Peng; Jiang Renqing; Gao Fubing

2008-01-01

The purpose of the design is to develop a data acquisition system which can be used to collect and transmit hundreds of channels of weak light signal data at the same time, so as to meet the need of industrial computer tomography. The system is composed of two parts, detection circuit and acquisition circuit. FPGA and 20 bit integral and conversion chips are the primary chips adopted in detection circuit, while the primary chips for acquisition circuit are FPGA and AMCCS5335. The problems, data jam-up and data drop were solved by using multilevel memorizer. A large number of experiments have proved that this system has very high precision and transmission reliability. The design has been applied in several industrial computer tomography machines produced by the industrial computer tomography research center of Chongqing University, and its effectiveness is well apprised. (authors)
Potential conflicts connected with the recovery of secondary materials from post mining waste dump

Directory of Open Access Journals (Sweden)

Gawor Łukasz

2017-12-01

Full Text Available Coal mine spoil dumping grounds are present in the landscape of every mining region. Although the composition of waste material is in general safe for the environment (sedimentary rocks – sandstones, mudstones and siltstones, there may be up to 10% of coal particles in disposed wastes. The presence of organic material causes self-ignition processes and fire hazards. There is a need and the possibility of the recovery of coal, and which should be conducted according to legal regulations and environmental protection rules. The recovery should also be preceded by a feasibility study, a drilling campaign, laboratory tests and requires different environmental permissions. Recovery processes are connected with the work of a preparation plant, which is usually linked with protests from the local community and potential conflicts. This article presents the most significant hazards to the environment, health and human life connected with the functions associated with the installation of the recovery processes of coal from waste material deposited on the dumps. The methods of reducing these threats are described with regards to legal regulations, particularly law deeds concerning the safe recovery processes and further reclamation and restoration of degraded post-mining dumping grounds. The role and participation of interested community members at the preparation for investment stage as well as the period of realization of the preparation processes is described. The question of re-using and managing the post-mining dumping grounds after completion of the recovery processes is discussed.
Multiagent data warehousing and multiagent data mining for cerebrum/cerebellum modeling

Science.gov (United States)

Zhang, Wen-Ran

2002-03-01

An algorithm named Neighbor-Miner is outlined for multiagent data warehousing and multiagent data mining. The algorithm is defined in an evolving dynamic environment with autonomous or semiautonomous agents. Instead of mining frequent itemsets from customer transactions, the new algorithm discovers new agents and mining agent associations in first-order logic from agent attributes and actions. While the Apriori algorithm uses frequency as a priory threshold, the new algorithm uses agent similarity as priory knowledge. The concept of agent similarity leads to the notions of agent cuboid, orthogonal multiagent data warehousing (MADWH), and multiagent data mining (MADM). Based on agent similarities and action similarities, Neighbor-Miner is proposed and illustrated in a MADWH/MADM approach to cerebrum/cerebellum modeling. It is shown that (1) semiautonomous neurofuzzy agents can be identified for uniped locomotion and gymnastic training based on attribute relevance analysis; (2) new agents can be discovered and agent cuboids can be dynamically constructed in an orthogonal MADWH, which resembles an evolving cerebrum/cerebellum system; and (3) dynamic motion laws can be discovered as association rules in first order logic. Although examples in legged robot gymnastics are used to illustrate the basic ideas, the new approach is generally suitable for a broad category of data mining tasks where knowledge can be discovered collectively by a set of agents from a geographically or geometrically distributed but relevant environment, especially in scientific and engineering data environments.
Detection Model for Seepage Behavior of Earth Dams Based on Data Mining

Directory of Open Access Journals (Sweden)

Zhenxiang Jiang

2018-01-01

Full Text Available Seepage behavior detecting is an important tool for ensuring the safety of earth dams. However, traditional seepage behavior detection methods have used insufficient monitoring data and have mainly focused on single-point measures and local seepage behavior. The seepage behavior of dams is not quantitatively detected based on the monitoring data with multiple measuring points. Therefore, this study uses data mining techniques to analyze the monitoring data and overcome the above-mentioned shortcomings. The massive seepage monitoring data with multiple points are used as the research object. The key information on seepage behavior is extracted using principal component analysis. The correlation between seepage behavior and upstream water level is described as mutual information. A detection model for overall seepage behavior is established. Result shows that the model can completely extract the seepage monitoring data with multiple points and quantitatively detect the overall seepage behavior of earth dams. The proposed method can provide a new and reasonable means of quantitatively detecting the overall seepage behavior of earth dams.
A higher level language data acquisition system (III) - the user data acquisition program

International Nuclear Information System (INIS)

Finn, J.M.; Gulbranson, R.L.; Huang, T.L.

1983-01-01

The nuclear physics group at the University of Illinois has implemented a data acquisition system using modified versions of the Concurrent Pascal and Sequential Pascal languages. The user, a physicist, develops a data acquisition ''operating system'', written in these higher level languages, which is tailored to the planned experiment. The user must include only those system functions which are essential to the task, thus improving efficiency. The user program is constructed from simple modules, mainly consisting of Concurrent Pascal PROCESSes, MONITORs, and CLASSes together with appropriate data type definitions. Entire programs can be put together using ''cut and paste'' techniques. Planned enhancements include the automating of this process. Systems written for the Perkin-Elmer 3220 using this approach can easily exceed 2 kHz data rates for event by event handling; 20 kHz data rates have been achieved by the addition of buffers in the interrupt handling software. These rates have been achieved without the use of special-purpose hardware such as micro-programmed branch drivers. With the addition of such devices even higher data rates should be possible
Mining Staff Assignment Rules from Event-Based Data

NARCIS (Netherlands)

Ly, Linh Thao; Rinderle, Stefanie; Dadam, Peter; Reichert, Manfred; Bussler, Christoph J.; Haller, Armin

2006-01-01

Process mining offers methods and techniques for capturing process behaviour from log data of past process executions. Although many promising approaches on mining the control flow have been published, no attempt has been made to mine the staff assignment situation of business processes. In this
Data warehousing and data mining: A case study

Directory of Open Access Journals (Sweden)

Suknović Milija

2005-01-01

Full Text Available This paper shows design and implementation of data warehouse as well as the use of data mining algorithms for the purpose of knowledge discovery as the basic resource of adequate business decision making process. The project is realized for the needs of Student's Service Department of the Faculty of Organizational Sciences (FOS, University of Belgrade, Serbia and Montenegro. This system represents a good base for analysis and predictions in the following time period for the purpose of quality business decision-making by top management. Thus, the first part of the paper shows the steps in designing and development of data warehouse of the mentioned business system. The second part of the paper shows the implementation of data mining algorithms for the purpose of deducting rules, patterns and knowledge as a resource for support in the process of decision making.

Data-Throughput Enhancement Using Data Mining-Informed Cognitive Radio

Directory of Open Access Journals (Sweden)

Khashayar Kotobi

2015-03-01

Full Text Available We propose the data mining-informed cognitive radio, which uses non-traditional data sources and data-mining techniques for decision making and improving the performance of a wireless network. To date, the application of information other than wireless channel data in cognitive radios has not been significantly studied. We use a novel dataset (Twitter traffic as an indicator of network load in a wireless channel. Using this dataset, we present and test a series of predictive algorithms that show an improvement in wireless channel utilization over traditional collision-detection algorithms. Our results demonstrate the viability of using these novel datasets to inform and create more efficient cognitive radio networks.
Information Mining Technologies to Enable Discovery of Actionable Intelligence to Facilitate Maritime Situational Awareness: I-MINE

Science.gov (United States)

2013-01-01

website). Data mining tools are in-house code developed in Python, C++ and Java . • NGA The National Geospatial-Intelligence Agency (NGA) performs data...as PostgreSQL (with PostGIS), MySQL , Microsoft SQL Server, SQLite, etc. using the appropriate JDBC driver. 14 The documentation and ease to learn are...written in Java that is able to perform various types of regressions, classi- fications, and other data mining tasks. There is also a commercial version
Deriving preference order of post-mining land-uses through MLSA framework: application of an outranking technique

Science.gov (United States)

Soltanmohammadi, Hossein; Osanloo, Morteza; Aghajani Bazzazi, Abbas

2009-08-01

This study intends to take advantage of a previously developed framework for mined land suitability analysis (MLSA) consisted of economical, social, technical and mine site factors to achieve a partial and also a complete pre-order of feasible post-mining land-uses. Analysis by an outranking multi-attribute decision-making (MADM) technique, called PROMETHEE (preference ranking organization method for enrichment evaluation), was taken into consideration because of its clear advantages on the field of MLSA as compared with MADM ranking techniques. Application of the proposed approach on a mined land can be completed through some successive steps. First, performance of the MLSA attributes is scored locally by each individual decision maker (DM). Then the assigned performance scores are normalized and the deviation amplitudes of non-dominated alternatives are calculated. Weights of the attributes are calculated by another MADM technique namely, analytical hierarchy process (AHP) in a separate procedure. Using the Gaussian preference function beside the weights, the preference indexes of the land-use alternatives are obtained. Calculation of the outgoing and entering flows of the alternatives and one by one comparison of these values will lead to partial pre-order of them and calculation of the net flows, will lead to a ranked preference for each land-use. At the final step, utilizing the PROMETHEE group decision support system which incorporates judgments of all the DMs, a consensual ranking can be derived. In this paper, preference order of post-mining land-uses for a hypothetical mined land has been derived according to judgments of one DM to reveal applicability of the proposed approach.
Integrating Data Mining Techniques into Telemedicine Systems

Directory of Open Access Journals (Sweden)

Mihaela GHEORGHE

2014-01-01

Full Text Available The medical system is facing a wide range of challenges nowadays due to changes that are taking place in the global healthcare systems. These challenges are represented mostly by economic constraints (spiraling costs, financial issues, but also, by the increased emphasis on accountability and transparency, changes that were made in the education field, the fact that the biomedical research keeps growing in what concerns the complexities of the specific studies etc. Also the new partnerships that were made in medical care systems and the great advances in IT industry suggest that a predominant paradigm shift is occurring. This needs a focus on interaction, collaboration and increased sharing of information and knowledge, all of these may is in turn be leading healthcare organizations to embrace the techniques of data mining in order to create and sustain optimal healthcare outcomes. Data mining is a domain of great importance nowadays as it provides advanced data analysis techniques for extracting the knowledge from the huge volumes of data collected and stored by every system of a daily basis. In the healthcare organizations data mining can provide valuable information for patient's diagnosis and treatment planning, customer relationship management, organization resources management or fraud detection. In this article we focus on describing the importance of data mining techniques and systems for healthcare organizations with a focus on developing and implementing telemedicine solution in order to improve the healthcare services provided to the patients. We provide architecture for integrating data mining techniques into telemedicine systems and also offer an overview on understanding and improving the implemented solution by using Business Process Management methods.
Data mining in e-commerce: A survey

Indian Academy of Sciences (India)

Data mining has matured as a ﬁeld of basic and applied research in computer science in general and e-commerce in particular. In this paper, we survey some of the recent approaches and architectures where data mining has been applied in the ﬁelds of e-commerce and e-business. Our intent is not to survey the plethora ...
Multi-channel data acquisition system for CT

International Nuclear Information System (INIS)

Cao Fuqiang; He Bin; Liu Guohua; Xu Minjian

2009-01-01

The architecture design and realization of a data acquisition system for multi-channel CT is described. The article introduces the conversion of analog signal to digital signal, the data cache and transmission. This data acquisition system can be widely used in the system which requires the multi-channel, weak current signal detection. (authors)
Contribution to understanding the post-mining landscape - Application of airborn LiDAR and historical maps at the example from Silesian Upland (Poland)

Science.gov (United States)

Gawior, D.; Rutkiewicz, P.; Malik, I.; Wistuba, M.

2017-11-01

LiDAR data provide new insights into the historical development of mining industry recorded in the topography and landscape. In the study on the lead ore mining in the 13th-17th century we identified remnants of mining activity in relief that are normally obscured by dense vegetation. The industry in Tarnowice Plateau was based on exploitation of galena from the bedrock. New technologies, including DEM from airborne LiDAR provide show that present landscape and relief of post-mining area under study developed during several, subsequent phases of exploitation when different techniques of exploitation were used and probably different types of ores were exploited. Study conducted on the Tarnowice Plateau proved that combining GIS visualization techniques with historical maps, among all geological maps, is a promising approach in reconstructing development of anthropogenic relief and landscape..
Data Mining Techniques for Customer Relationship Management

Science.gov (United States)

Guo, Feng; Qin, Huilin

2017-10-01

Data mining have made customer relationship management (CRM) a new area where firms can gain a competitive advantage, and play a key role in the firms’ management decision. In this paper, we first analyze the value and application fields of data mining techniques for CRM, and further explore how data mining applied to Customer churn analysis. A new business culture is developing today. The conventional production centered and sales purposed market strategy is gradually shifting to customer centered and service purposed. Customers’ value orientation is increasingly affecting the firms’. And customer resource has become one of the most important strategic resources. Therefore, understanding customers’ needs and discriminating the most contributed customers has become the driving force of most modern business.
Advances in Machine Learning and Data Mining for Astronomy

Science.gov (United States)

Way, Michael J.; Scargle, Jeffrey D.; Ali, Kamal M.; Srivastava, Ashok N.

2012-03-01

Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines, the material discussed in this text transcends traditional boundaries between various areas in the sciences and computer science. The book's introductory part provides context to issues in the astronomical sciences that are also important to health, social, and physical sciences, particularly probabilistic and statistical aspects of classification and cluster analysis. The next part describes a number of astrophysics case studies that leverage a range of machine learning and data mining technologies. In the last part, developers of algorithms and practitioners of machine learning and data mining show how these tools and techniques are used in astronomical applications. With contributions from leading astronomers and computer scientists, this book is a practical guide to many of the most important developments in machine learning, data mining, and statistics. It explores how these advances can solve current and future problems in astronomy and looks at how they could lead to the creation of entirely new algorithms within the data mining community.
State of art data acquisition system for large volume plasma device

International Nuclear Information System (INIS)

Sugandhi, Ritesh; Srivastava, Pankaj; Sanyasi, Amulya Kumar; Srivastav, Prabhakar; Awasthi, Lalit Mohan; Mattoo, Shiban Krishna; Parmar, Vijay; Makadia, Keyur; Patel, Ishan; Shah, Sandeep

2015-01-01

The Large volume plasma device (LVPD) is a cylindrical device (ϕ = 2m, L = 3m) dedicated for carrying out investigations on plasma physics problems ranging from excitation of whistler structures to plasma turbulence especially, exploring the linear and nonlinear aspects of electron temperature gradient(ETG) driven turbulence, plasma transport over the entire cross section of LVPD. The machine operates in a pulsed mode with repetition cycle of 1 Hz and acquisition pulse length of duration of 15 ms, presently, LVPD has VXI data acquisition system but this is now in phasing out mode because of non-functioning of its various amplifier stages, expandability and unavailability of service support. The VXI system has limited capabilities to meet new experimental requirements in terms of numbers of channel (16), bit resolutions (8 bit), record length (30K points) and calibration support. Recently, integration of new acquisition system for simultaneous sampling of 40 channels of data, collected over multiple time scales with high speed is successfully demonstrated, by configuring latest available hardware and in-house developed software solutions. The operational feasibility provided by LabVIEW platform is not only for operating DAQ system but also for providing controls to various subsystems associated with the device. The new system is based on PXI express instrumentation bus and supersedes the existing VXI based data acquisition system in terms of instrumentation capabilities. This system has capability to measure 32 signals at 60 MHz sampling frequency and 8 signals with 1.25 GHz with 10 bit and 12 bit resolution capability for amplitude measurements. The PXI based system successfully addresses and demonstrate the issues concerning high channel count, high speed data streaming and multiple I/O modules synchronization. The system consists of chassis (NI 1085), 4 high sampling digitizers (NI 5105), 2 very high sampling digitizers (NI 5162), data streaming RAID drive (NI
Mining multi-dimensional data for decision support

Energy Technology Data Exchange (ETDEWEB)

Donato, J.M.; Schryver, J.C.; Hinkel, G.C.; Schmoyer, R.L. Jr. [Oak Ridge National Lab., TN (United States); Grady, N.W.; Leuze, M.R. [Oak Ridge National Lab., TN (United States)]|[Joint Inst. for Computational Science, Knoxville, TN (United States)

1998-06-01

While it is widely recognized that data can be a valuable resource for any organization, extracting information contained within the data is often a difficult problem. Attempts to obtain information from data may be limited by legacy data storage formats, lack of expert knowledge about the data, difficulty in viewing the data, or the volume of data needing to be processed. The rapidly developing field of Data Mining or Knowledge Data Discovery is a blending of Artificial Intelligence, Statistics, and Human-Computer Interaction. Sophisticated data navigation tools to obtain the information needed for decision support do not yet exist. Each data mining task requires a custom solution that depends upon the character and quantity of the data. This paper presents a two-stage approach for handling the prediction of personal bankruptcy using credit card account data, combining decision tree and artificial neural network technologies. Topics to be discussed include the pre-processing of data, including data cleansing, the filtering of data for pertinent records, and the reduction of data for attributes contributing to the prediction of bankruptcy, and the two steps in the mining process itself.
Data acquisition for sensor systems

CERN Document Server

Taylor, H Rosemary

1997-01-01

'Data acquisition' is concerned with taking one or more analogue signals and converting them to digital form with sufficient accu racy and speed to be ready for processing by a computer. The increasing use of computers makes this an expanding field, and it is important that the conversion process is done correctly because information lost at this stage can never be regained, no matter how good the computation. The old saying - garbage in, garbage out - is very relevant to data acquisition, and so every part of the book contains a discussion of errors: where do they come from, how large are they, and what can be done to reduce them? The book aims to treat the data acquisition process in depth with less detailed chapters on the fundamental principles of measure ment, sensors and signal conditioning. There is also a chapter on software packages, which are becoming increasingly popular. This is such a rapidly changing topic that any review of available pro grams is bound to be out of date before the book re...
Data acquisition system for nuclear reactor environment

International Nuclear Information System (INIS)

Tiwari, Akash; Tiwari, Railesha; Tiwari, S.S.; Panday, Lokesh; Suri, Nitin; Chouksey, Abhsek; Singh, Sarvesh Kumar; Dwivedi, Tarun; Agrawal, Ashish; Pandey, Pranav Kumar; Sharma, Brijnandan; Bhatia, Chirag

2004-01-01

We have designed an online real time data acquisition system for nuclear reactor environment monitoring. Data acquisition system has eight channels of analog signals and one channel of pulsed input signal from detectors like GM Tube, or any other similar input. Connectivity between the data acquisition system and environmental parameters monitoring computer is made through a wireless data communication link of 151 MHz/100 mW RF power and 10 km maximum communication range for remote data telemetry. Sensors used are gamma ionizing radiation sensor made from CsI:Tl scintillator, atmospheric pressure sensor with +/-0.1 mbar precision, temperature sensor with +/-l milli degree Celsius precision, relative humidity with +/-0.1RH precision, pulse counts with +/-1 count in 0-10000 Hz count rate measurement precision and +/-1 count is accumulated count measurement precision. The entire data acquisition system and wireless telemetry system is 9 V battery powered and the device is to be fitted on a wireless controlled mobile robot for scanning the nuclear reactor zone from remote. Wireless video camera has been planned for integration into the existing system on a later date for moving the robotics environmental data acquisition system beyond human vision reach. System development cost is Rs.25 Lacs and has been developed for Department of Atomic Energy, Government of India and Indian Defense use. (author)
Extended data acquisition support at GSI

International Nuclear Information System (INIS)

Marinescu, D.C.; Busch, F.; Hultzsch, H.; Lowsky, J.; Richter, M.

1984-01-01

The Experiment Data Acquisition and Analysis System (EDAS) of GSI, designed to support the data processing associated with nuclear physics experiments, provides three modes of operation: real-time, interactive replay and batch replay. The real-time mode is used for data acquisition and data analysis during an experiment performed at the heavy ion accelerator at GSI. An experiment may be performed either in Stand Alone Mode, using only the Experiment Computers, or in Extended Mode using all computing resources available. The Extended Mode combines the advantages of the real-time response of a dedicated minicomputer with the availability of computing resources in a large computing environment. This paper first gives an overview of EDAS and presents the GSI High Speed Data Acquisition Network. Data Acquisition Modes and the Extended Mode are then introduced. The structure of the system components, their implementation and the functions pertinent to the Extended Mode are presented. The control functions of the Experiment Computer sub-system are discussed in detail. Two aspects of the design of the sub-system running on the mainframe are stressed, namely the use of a multi-user installation for real-time processing and the use of a high level programming language, PL/I, as an implementation language for a system which uses parallel processing. The experience accumulated is summarized in a number of conclusions
The BaBar Data Acquisition System

CERN Document Server

Scott, I; Grosso, P; Huffer, M E; O'Grady, C; Russell, J J

1999-01-01

The BaBar experiment at the Stanford Linear Accelerator Center is designed to perform a search for CP violation by ana-lyzing the decays of a very large sample of B and B(Bar) mesons produced at the high luminosity PEP-II accelerator. The data acquisition system must cope with a sustained high event rate, while supporting real time feature extraction and data compression with minimal dead time. The BaBar data acquisition system is based around a common VME interface to the electronics read-out of the separate detec-tor subsystems. Data from the front end electronics is read into commercial VME processors via a custom "Personality Card" and PCI interface. The commercial CPUs run the Tornado operating system to provide a platform for detector subsystem code to perform the necessary data processing. The data is read out via a non-blocking network switch to a farm of commercial UNIX processors. The current implementation of the BaBar data acquisition sys-tem has been shown to sustain a Level 1 trigger rate of 1.3...
New data acquisition system for AMS

Energy Technology Data Exchange (ETDEWEB)

Pfenninger, R. [Paul Scherrer Inst. (PSI), Villigen (Switzerland)

1997-09-01

A new data acquisition system based on a VME front-end computer, a Sun workstation and a PC has been installed. It is used for the acquisition of mainly AMS data, their graphical display, and storage of the data in a Oracle database. The measurement of magazines of 25 sample each is fully automated. Several data parameters such as transmission are regularly checked. In case of problems the operator is informed by optical and/or acoustical signals. Screens are updated automatically after every measurement cycle. (author) 1 fig.
Data acquisition system for LHCb calorimeter

International Nuclear Information System (INIS)

Dai Gang; Gong Guanghua; Shao Beibei

2007-01-01

LHCb Calorimeter system is mainly used to identify and measure the energy of the photon, electron, hadron produced by the collision of proton. TELL1 is a common data acquisition platform based on FPGA for LHCb experiment. It is used to adopt custom data acquisition and process method for every detector and provide the data standard for the CPU matrix. This paper provides a novel DAQ and data process model in VHDL for Calorimeter. According to this model. We have built an effective Calorimeter DAQ system, which would be used in LHCb Experiment. (authors)
Standard values of quality and ore mining costs in management of multi-plant mining company

Energy Technology Data Exchange (ETDEWEB)

Kudelko, Jan [KGHM CUPRUM Research and Development Center, Wroclaw (Poland); Wirth, Herbert [KGHM Polska Miedz S.A., Lubin (Poland)

2010-03-15

Profitability of copper deposit mining depends on three basic variables, electrolytic copper price, manufacturing and selling costs of copper and company property involved in production process. If the company property is adjusted to its tasks then the mining profiability depends on costs of copper mining and selling, because the price is the external variable defined by the market. We can shape the costs in two (complementary) ways, traditionally, reducing the labor, material and power consumption, and by adjusting the quality of mined ore (copper content) to the level required by the current copper prices. Required quality of copper ore in the whole company we determine according to the accepted profitability criteria and then we determine quality standard for individual mines. Algorithms determining the ore quality standard resulting from current market price of copper are presented in the paper. Calculation models for the mined ore quality standards, unit mining costs per one ton of copper, electrolytic copper production and ore output are given. Standards were established for one variable assuming that the other variables are determined in this calculation. Innovative solution, presented in the paper, is the method of decomposition of the company controllable variables into the tasks for individual mines providing reaching the targets to the whole technological circuit. Using the models, having relatively few data, it will be possible to calculate quickly the values which are interesting for managers such as for example the prognosis of rate of return (economic or operational), required copper content in the mined ore for the whole company and individual mines at given rate of return or boundary level of copper content in comparison with cost and production level. Examples of calculation are provided. (orig.)
Data and Statistics on New York's Mining Resources - NYS Dept. of

Science.gov (United States)

): Search DEC D E C banner Home Â» Lands and Waters Â» Mining & Reclamation Â» Data and Statistics on New York's Mining Resources Skip to main navigation Data and Statistics on New York's Mining Resources Statistics on New York's Mining Resources: Mines in New York - Information on active mines in New York State
Design of data acquisition system for GEM detector

International Nuclear Information System (INIS)

Lu Jianliang; Chen Ziyu; Shen Ji; Jin Xi

2011-01-01

It describes the design and realization of the USB 2.0 high speed data acquisition devise which is used in the readout electronics of the GEM (gas electron multiplier) detector. By using of the USB Microcontroller EZ-USB FX2 CY7C68013A, high speed ADC and FPGA, high-speed data rate of data acquisition and transmission was realized. The data rate reaches to 20 MByte/s, meeting the requirements of data acquisition and transmission of the detector. (authors)

48 CFR 227.7103-2 - Acquisition of technical data.

Science.gov (United States)

2010-10-01

... 48 Federal Acquisition Regulations System 3 2010-10-01 2010-10-01 false Acquisition of technical... Rights in Technical Data 227.7103-2 Acquisition of technical data. (a) Contracting officers shall work... personnel are responsible for identifying the Government's minimum needs for technical data. Data needs must...
Marine data users clustering using data mining technique

Directory of Open Access Journals (Sweden)

Farnaz Ghiasi

2015-09-01

Full Text Available The objective of this research is marine data users clustering using data mining technique. To achieve this objective, marine organizations will enable to know their data and users requirements. In this research, CRISP-DM standard model was used to implement the data mining technique. The required data was extracted from 500 marine data users profile database of Iranian National Institute for Oceanography and Atmospheric Sciences (INIOAS from 1386 to 1393. The TwoStep algorithm was used for clustering. In this research, patterns was discovered between marine data users such as student, organization and scientist and their data request (Data source, Data type, Data set, Parameter and Geographic area using clustering for the first time. The most important clusters are: Student with International data source, Chemistry data type, “World Ocean Database” dataset, Persian Gulf geographic area and Organization with Nitrate parameter. Senior managers of the marine organizations will enable to make correct decisions concerning their existing data. They will direct to planning for better data collection in the future. Also data users will guide with respect to their requests. Finally, the valuable suggestions were offered to improve the performance of marine organizations.
BOOK REVIEW EDUCATIONAL DATA MINING: APPLICATIONS AND TRENDS

Directory of Open Access Journals (Sweden)

Aylin OZTURK

2016-04-01

Full Text Available Educational Data Mining (EDM is a developing field based on data mining techniques. EDM emerged as a combination of areas such as machine learning, statistics, computer science, education, cognitive science, and psychometry. EDM focuses on learner characteristics, behaviors, academic achievements, process of learning, educational functionalities, domain knowledge content, assessments, and applications. Educational data mining is defined by Baker (2010 as ‘‘an emerging discipline, concerned with developing methods for exploring the unique types of data that come from educational settings, and using those methods to better understand students, and the settings which they learn in’’. EDM is concerned with improving the learning process and environment.
Data acquisition in nuclear and particle physics

International Nuclear Information System (INIS)

Renk, B.

1993-01-01

An introduction to the methodics of the measurement data acquisition in nuclear and particle physics for students of physics as well as experimental physicists and engineers in research and industry. The contents are: Obtaining of measurement data, digitizing and triggers, memories and microprocessors, bus systems, communication and networks, and examples for data acquisition systems
Mining Educational Data to Analyze the Student Motivation Behavior

OpenAIRE

Kunyanuth Kularbphettong; Cholticha Tongsiri

2012-01-01

The purpose of this research aims to discover the knowledge for analysis student motivation behavior on e-Learning based on Data Mining Techniques, in case of the Information Technology for Communication and Learning Course at Suan Sunandha Rajabhat University. The data mining techniques was applied in this research including association rules, classification techniques. The results showed that using data mining technique can indicate the important variables that influenc...
Data analysis in the post-genome-wide association study era

Directory of Open Access Journals (Sweden)

Qiao-Ling Wang

2016-12-01

Full Text Available Since the first report of a genome-wide association study (GWAS on human age-related macular degeneration, GWAS has successfully been used to discover genetic variants for a variety of complex human diseases and/or traits, and thousands of associated loci have been identified. However, the underlying mechanisms for these loci remain largely unknown. To make these GWAS findings more useful, it is necessary to perform in-depth data mining. The data analysis in the post-GWAS era will include the following aspects: fine-mapping of susceptibility regions to identify susceptibility genes for elucidating the biological mechanism of action; joint analysis of susceptibility genes in different diseases; integration of GWAS, transcriptome, and epigenetic data to analyze expression and methylation quantitative trait loci at the whole-genome level, and find single-nucleotide polymorphisms that influence gene expression and DNA methylation; genome-wide association analysis of disease-related DNA copy number variations. Applying these strategies and methods will serve to strengthen GWAS data to enhance the utility and significance of GWAS in improving understanding of the genetics of complex diseases or traits and translate these findings for clinical applications. Keywords: Genome-wide association study, Data mining, Integrative data analysis, Polymorphism, Copy number variation
Microcontroller-Based Fault Tolerant Data Acquisition System For Air Quality Monitoring And Control Of Environmental Pollution

Directory of Open Access Journals (Sweden)

Tochukwu Chiagunye

2015-08-01

Full Text Available ABSTRACT The design applied Passive fault tolerance to a microcontroller based data acquisition system to achieve the stated considerations where redundant sensors and microcontrollers with associated circuitry were designed and implemented to enable measurement of pollutant concentration information from chimney vents in two industry. Microsoft visual basic was used to develop a data mining tool which implemented an underlying artificial neural network model for forecasting pollutant concentrations for future time periods. The feed forward back propagation method was used to train the ANN model with a training data set while a decision tree algorithm was used to select an optimal output result for the model from its two output neurons.
Acquisitions Everywhere: Modeling an Acquisitions Data Standard to Connect a Distributed Environment

OpenAIRE

Hanson, Eric M.; Lightcap, Paul W.; Miguez, Matthew R.

2016-01-01

Acquisitions functions remain operationally crucial in providing access to paid information resources, but data formats and workflows utilized within library acquisitions remain primarily within the traditional integrated library system (ILS). As libraries have evolved to use distributed systems to manage information resources, so too must acquisitions functions adapt to an environment that may include the ILS, e‐resource management systems (ERMS), institutional repositories (IR), and other d...
Frequent Pattern Mining Algorithms for Data Clustering

DEFF Research Database (Denmark)

Zimek, Arthur; Assent, Ira; Vreeken, Jilles

2014-01-01

that frequent pattern mining was at the cradle of subspace clustering—yet, it quickly developed into an independent research field. In this chapter, we discuss how frequent pattern mining algorithms have been extended and generalized towards the discovery of local clusters in high-dimensional data......Discovering clusters in subspaces, or subspace clustering and related clustering paradigms, is a research field where we find many frequent pattern mining related influences. In fact, as the first algorithms for subspace clustering were based on frequent pattern mining algorithms, it is fair to say....... In particular, we discuss several example algorithms for subspace clustering or projected clustering as well as point out recent research questions and open topics in this area relevant to researchers in either clustering or pattern mining...
Data acquisition techniques

International Nuclear Information System (INIS)

Dougherty, R.C.

1976-01-01

Testing neutron generators and major subassemblies has undergone a transition in the past few years. Digital information is now used for storage and analysis. The key to the change is the availability of a high-speed digitizer system. The status of the Sandia Laboratory data acquisition and handling system as applied to this area is surveyed. 1 figure
Data mining, knowledge discovery and data-driven modelling

NARCIS (Netherlands)

Solomatine, D.P.; Velickov, S.; Bhattacharya, B.; Van der Wal, B.

2003-01-01

The project was aimed at exploring the possibilities of a new paradigm in modelling - data-driven modelling, often referred as "data mining". Several application areas were considered: sedimentation problems in the Port of Rotterdam, automatic soil classification on the basis of cone penetration
A Data Mining Approach for Cardiovascular Diagnosis

Directory of Open Access Journals (Sweden)

Pereira Joana

2017-12-01

Full Text Available The large amounts of data generated by healthcare transactions are too complex and voluminous to be processed and analysed by traditional methods. Data mining can improve decision-making by discovering patterns and trends in large amounts of complex data. In the healthcare industry specifically, data mining can be used to decrease costs by increasing efficiency, improve patient quality of life, and perhaps most importantly, save the lives of more patients. The main goal of this project is to apply data mining techniques in order to make possible the prediction of the degree of disability that patients will present when they leave hospitalization. The clinical data that will compose the data set was obtained from one single hospital and contains information about patients who were hospitalized in Cardio Vascular Disease’s (CVD unit in 2016 for having suffered a cardiovascular accident. To develop this project, it will be used the Waikato Environment for Knowledge Analysis (WEKA machine learning Workbench since this one allows users to quickly try out and compare different machine learning methods on new data sets
Data Mining and Knowledge Management in Higher Education -Potential Applications.

Science.gov (United States)

Luan, Jing

This paper introduces a new decision support tool, data mining, in the context of knowledge management. The most striking features of data mining techniques are clustering and prediction. The clustering aspect of data mining offers comprehensive characteristics analysis of students, while the predicting function estimates the likelihood for a…
Academic Performance: An Approach From Data Mining

Directory of Open Access Journals (Sweden)

David L. La Red Martinez

2012-02-01

Full Text Available The relatively low% of students promoted and regularized in Operating Systems Course of the LSI (Bachelor’s Degree in Information Systems of FaCENA (Faculty of Sciences and Natural Surveying - Facultad de Ciencias Exactas, Naturales y Agrimensura of UNNE (academic success, prompted this work, whose objective is to determine the variables that affect the academic performance, whereas the final status of the student according to the Res. 185/03 CD (scheme for evaluation and promotion: promoted, regular or free1. The variables considered are: status of the student, educational level of parents, secondary education, socio-economic level, and others. Data warehouse (Data Warehouses: DW and data mining (Data Mining: DM techniques were used to search pro.les of students and determine success or failure academic potential situations. Classifications through techniques of clustering according to different criteria have become. Some criteria were the following: mining of classification according to academic program, according to final status of the student, according to importance given to the study, mining of demographic clustering and Kohonen clustering according to final status of the student. Were conducted statistics of partition, detail of partitions, details of clusters, detail of fields and frequency of fields, overall quality of each process and quality detailed (precision, classification, reliability, arrays of confusion, diagrams of gain / elevation, trees, distribution of nodes, of importance of fields, correspondence tables of fields and statistics of cluster. Once certain profiles of students with low academic performance, it may address actions aimed at avoiding potential academic failures. This work aims to provide a brief description of aspects related to the data warehouse built and some processes of data mining developed on the same.
Data Mining Smart Energy Time Series

Directory of Open Access Journals (Sweden)

Janina POPEANGA

2015-07-01

Full Text Available With the advent of smart metering technology the amount of energy data will increase significantly and utilities industry will have to face another big challenge - to find relationships within time-series data and even more - to analyze such huge numbers of time series to find useful patterns and trends with fast or even real-time response. This study makes a small review of the literature in the field, trying to demonstrate how essential is the application of data mining techniques in the time series to make the best use of this large quantity of data, despite all the difficulties. Also, the most important Time Series Data Mining techniques are presented, highlighting their applicability in the energy domain.
Microcontroller based, ore grade measuring portable instruments for uranium mining industry

International Nuclear Information System (INIS)

Dheeraj Reddy, J.; Narender Reddy, J.

2004-01-01

Ore Face Scanning and Bore Hole Logging are important essential activities which are required to be carried out in any Uranium mining industry. Microcontroller based, portable instruments with built-in powerful embedded code for data acquisition (of Radiation counts) and Ore Grade calculations will become a handy measuring tool for miners. Nucleonix Systems has recently developed and made these two portable instruments available to UCIL, which are under use at Jaduguda and Narvapahar mines. Some of the important features of these systems are compact, light weight, portable, hand held, battery powered. Modes of Data Acquisition: CPS, CPM and ORE GRADE. Detector: Sensitive GM Tube. Choice of Adj. TC (Time Constant) in 'ORE GRADE', acquisition mode. Built-in automatic BG (Background) recording and subtraction provided to indicate net CPS, CPM or ore GRADE in PPM. Can store 1000 readings at users choice. Built-in RS232 serial port facilitates data downloading into PC. This paper focuses on design concepts and technical details for the above two products. (author)
Methodologies of Knowledge Discovery from Data and Data Mining Methods in Mechanical Engineering

Directory of Open Access Journals (Sweden)

Rogalewicz Michał

2016-12-01

Full Text Available The paper contains a review of methodologies of a process of knowledge discovery from data and methods of data exploration (Data Mining, which are the most frequently used in mechanical engineering. The methodologies contain various scenarios of data exploring, while DM methods are used in their scope. The paper shows premises for use of DM methods in industry, as well as their advantages and disadvantages. Development of methodologies of knowledge discovery from data is also presented, along with a classification of the most widespread Data Mining methods, divided by type of realized tasks. The paper is summarized by presentation of selected Data Mining applications in mechanical engineering.
Acquisition and Post-Processing of Immunohistochemical Images.

Science.gov (United States)

Sedgewick, Jerry

2017-01-01

Augmentation of digital images is almost always a necessity in order to obtain a reproduction that matches the appearance of the original. However, that augmentation can mislead if it is done incorrectly and not within reasonable limits. When procedures are in place for insuring that originals are archived, and image manipulation steps reported, scientists not only follow good laboratory practices, but avoid ethical issues associated with post processing, and protect their labs from any future allegations of scientific misconduct. Also, when procedures are in place for correct acquisition of images, the extent of post processing is minimized or eliminated. These procedures include white balancing (for brightfield images), keeping tonal values within the dynamic range of the detector, frame averaging to eliminate noise (typically in fluorescence imaging), use of the highest bit depth when a choice is available, flatfield correction, and archiving of the image in a non-lossy format (not JPEG).When post-processing is necessary, the commonly used applications for correction include Photoshop, and ImageJ, but a free program (GIMP) can also be used. Corrections to images include scaling the bit depth to higher and lower ranges, removing color casts from brightfield images, setting brightness and contrast, reducing color noise, reducing "grainy" noise, conversion of pure colors to grayscale, conversion of grayscale to colors typically used in fluorescence imaging, correction of uneven illumination (flatfield correction), merging color images (fluorescence), and extending the depth of focus. These corrections are explained in step-by-step procedures in the chapter that follows.
Data mining-aided materials discovery and optimization

Directory of Open Access Journals (Sweden)

Wencong Lu

2017-09-01

Full Text Available Recent developments in data mining-aided materials discovery and optimization are reviewed in this paper, and an introduction to the materials data mining (MDM process is provided using case studies. Both qualitative and quantitative methods in machine learning can be adopted in the MDM process to accomplish different tasks in materials discovery, design, and optimization. State-of-the-art techniques in data mining-aided materials discovery and optimization are demonstrated by reviewing the controllable synthesis of dendritic Co3O4 superstructures, materials design of layered double hydroxide, battery materials discovery, and thermoelectric materials design. The results of the case studies indicate that MDM is a powerful approach for use in materials discovery and innovation, and will play an important role in the development of the Materials Genome Initiative and Materials Informatics.
PC based 8-parameter data acquisition system

International Nuclear Information System (INIS)

Gupta, J.D.; Naik, K.V.; Jain, S.K.; Pathak, R.V.; Suman, B.

1989-01-01

Multiparameter data acquisition (MPA) systems which analyse nuclear events with respect to more than one property of the event are essential tools for the study of some complex nuclear phenomena requiring analysis of time coincident spectra. For better throughput and accuracy each parameter is digitized by its own ADC. A stand alone low cost IBM PC based 8-parameter data acquisition system developed by the authors makes use of Address Recording technique for acquiring data from eight 12 bit ADC's in the PC Memory. Two memory buffers in the PC memory are used in ping-pong fashion so that data acquisition in one bank and dumping of data onto PC disk from the other bank can proceed simultaneously. Data is acquired in the PC memory through DMA mode for realising high throughput and hardware interrupt is used for switching banks for data acquisition. A comprehensive software package developed in Turbo-Pascal offers a set of menu-driven interactive commands to the user for setting-up system parameters and control of the system. The system is to be used with pelletron accelerator. (author). 5 figs

Fusing Data Mining, Machine Learning and Traditional Statistics to Detect Biomarkers Associated with Depression

OpenAIRE

Dipnall, Joanna F.; Pasco, Julie A.; Berk, Michael; Williams, Lana J.; Dodd, Seetal; Jacka, Felice N.; Meyer, Denny

2016-01-01

Background Atheoretical large-scale data mining techniques using machine learning algorithms have promise in the analysis of large epidemiological datasets. This study illustrates the use of a hybrid methodology for variable selection that took account of missing data and complex survey design to identify key biomarkers associated with depression from a large epidemiological study. Methods The study used a three-step methodology amalgamating multiple imputation, a machine learning boosted reg...
Towards educational data mining: Using data mining methods for automated chat analysis to understand and support inquiry learning processes

OpenAIRE

Anjewierden , Anjo; Kolloffel , Bas; Hulshof , Casper

2007-01-01

In this paper we investigate the application of data mining methods to provide learners with real-time adaptive feedback on the nature and patterns of their on-line communication while learning collaboratively.We derived two models for classifying chat messages using data mining techniques and tested these on an actual data set [16]. The reliability of the classification of chat messages is established by comparing the models performance to that of humans. Results indicate that the classifica...
Monitoring the data flow of LHCb’s data acquisition system

CERN Document Server

Svantesson, David; Rainer, S

2010-01-01

The data acquisition system of the Large Hadron Collider beauty (LHCb) experiment need to read out huge amount of data. Monitoring is done for each subsystem but there exist no system to monitor the overall data flow. The aim of this work has been to design a system where the data rates can be vied continuously and making it possible to do an exact consistency check after the run to ensure no data are lost. This involves collecting and processing all necessary data from each subsystem and integrate it into the experiment control system for displaying it to the operators. The challenges are to communicate and collect data from all stages of the data acquisitions system which uses different techniques and data formats. The size of the system also makes it a challenge to gather all statistics in real time. The system must also be able to support partitioning. The result was to build a data flow monitoring system, that acquire statistics from all stages of the data acquisition, process it and display it in the ex...
Multiple acquisition of magic angle spinning solid-state NMR experiments using one receiver: Application to microcrystalline and membrane protein preparations

Science.gov (United States)

Gopinath, T.; Veglia, Gianluigi

2015-04-01

Solid-state NMR spectroscopy of proteins is a notoriously low-throughput technique. Relatively low-sensitivity and poor resolution of protein samples require long acquisition times for multidimensional NMR experiments. To speed up data acquisition, we developed a family of experiments called Polarization Optimized Experiments (POE), in which we utilized the orphan spin operators that are discarded in classical multidimensional NMR experiments, recovering them to allow simultaneous acquisition of multiple 2D and 3D experiments, all while using conventional probes with spectrometers equipped with one receiver. POE allow the concatenation of multiple 2D or 3D pulse sequences into a single experiment, thus potentially combining all of the aforementioned advances, boosting the capability of ssNMR spectrometers at least two-fold without the addition of any hardware. In this perspective, we describe the first generation of POE, such as dual acquisition MAS (or DUMAS) methods, and then illustrate the evolution of these experiments into MEIOSIS, a method that enables the simultaneous acquisition of multiple 2D and 3D spectra. Using these new pulse schemes for the solid-state NMR investigation of biopolymers makes it possible to obtain sequential resonance assignments, as well as distance restraints, in about half the experimental time. While designed for acquisition of heteronuclei, these new experiments can be easily implemented for proton detection and coupled with other recent advancements, such as dynamic nuclear polarization (DNP), to improve signal to noise. Finally, we illustrate the application of these methods to microcrystalline protein preparations as well as single and multi-span membrane proteins reconstituted in lipid membranes.
An Evaluation of Text Mining Tools as Applied to Selected Scientific and Engineering Literature.

Science.gov (United States)

Trybula, Walter J.; Wyllys, Ronald E.

2000-01-01

Addresses an approach to the discovery of scientific knowledge through an examination of data mining and text mining techniques. Presents the results of experiments that investigated knowledge acquisition from a selected set of technical documents by domain experts. (Contains 15 references.) (Author/LRW)
A Lossless Network for Data Acquisition

OpenAIRE

Jereczek, Grzegorz Edmund; Lehmann Miotto, Giovanna

2016-01-01

The bursty many-to-one communication pattern, typical for data acquisition systems, is particularly demanding for commodity TCP/IP and Ethernet technologies. We expand the study of lossless switching in software running on commercial-off-the-shelf servers, using the ATLAS experiment as a case study. In this paper we extend the popular software switch, Open vSwitch, with a dedicated, throughput-oriented buffering mechanism for data acquisition. We compare the performance under heavy congestion...
NASA Data Acquisition System Software Development for Rocket Propulsion Test Facilities

Science.gov (United States)

Herbert, Phillip W., Sr.; Elliot, Alex C.; Graves, Andrew R.

2015-01-01

Current NASA propulsion test facilities include Stennis Space Center in Mississippi, Marshall Space Flight Center in Alabama, Plum Brook Station in Ohio, and White Sands Test Facility in New Mexico. Within and across these centers, a diverse set of data acquisition systems exist with different hardware and software platforms. The NASA Data Acquisition System (NDAS) is a software suite designed to operate and control many critical aspects of rocket engine testing. The software suite combines real-time data visualization, data recording to a variety formats, short-term and long-term acquisition system calibration capabilities, test stand configuration control, and a variety of data post-processing capabilities. Additionally, data stream conversion functions exist to translate test facility data streams to and from downstream systems, including engine customer systems. The primary design goals for NDAS are flexibility, extensibility, and modularity. Providing a common user interface for a variety of hardware platforms helps drive consistency and error reduction during testing. In addition, with an understanding that test facilities have different requirements and setups, the software is designed to be modular. One engine program may require real-time displays and data recording; others may require more complex data stream conversion, measurement filtering, or test stand configuration management. The NDAS suite allows test facilities to choose which components to use based on their specific needs. The NDAS code is primarily written in LabVIEW, a graphical, data-flow driven language. Although LabVIEW is a general-purpose programming language; large-scale software development in the language is relatively rare compared to more commonly used languages. The NDAS software suite also makes extensive use of a new, advanced development framework called the Actor Framework. The Actor Framework provides a level of code reuse and extensibility that has previously been difficult
Data Mining for Education Decision Support: A Review

Directory of Open Access Journals (Sweden)

Suhirman Suhirman

2014-12-01

Full Text Available Management of higher education must continue to evaluate on an ongoing basis in order to improve the quality of institutions. This will be able to do the necessary evaluation of various data, information, and knowledge of both internal and external institutions. They plan to use more efficiently the collected data, develop tools so that to collect and direct management information, in order to support managerial decision making. The collected data could be utilized to evaluate quality, perform analyses and diagnoses, evaluate dependability to the standards and practices of curricula and syllabi, and suggest alternatives in decision processes. Data minings to support decision making are well suited methods to provide decision support in the education environments, by generating and presenting relevant information and knowledge towards quality improvement of education processes. In educational domain, this information is very useful since it can be used as a base for investigating and enhancing the current educational standards and managements. In this paper, a review on data mining for academic decision support in education field is presented. The details of this paper will review on recent data mining in educational field and outlines future researches in educational data mining.
APLIKASI DATA MINING UNTUK MENAMPILKAN INFORMASI TINGKAT KELULUSAN MAHASISWA

Directory of Open Access Journals (Sweden)

Yuli Asriningtias

2014-01-01

Full Text Available Perguruan tinggi dituntut memiliki keunggulan bersaing dengan memanfaatkan sumber dayanya, termasuk sumber daya manusia dalam hal ini adalah mahasiswa.Tidak semua mahasiswa dapat menyelesaikan study tepat waktu, disamping IPK yang beragam. Lama waktu mahasiswa dalam menempuh studi dan IPK menjadi salah satu faktor tingkat keunggulan sebuah Perguruan Tinggi. Nilai potensi tersebut dapat digali menggunakan teknik data mining.Data mining adalah kegiatan menemukan pola yang menarik dari data dalam jumlah besar, data dapat disimpan dalam database, data warehouse, atau penyimpanan informasi lainnya. Data warehouse merupakan penyimpanan data yang berorientasi objek, terintegrasi, mempunyai variant waktu, dan menyimpan data dalam bentuk nonvolatile sebagai pendukung manejemen dalam proses pengambilan keputusan. Penelitian ini dikembangkan dengan cara menscan data pada database secara langsung sehingga menghasilkan informasi yag dibutuhkan. Aplikasi data mining ini dibangun menggunakan bahasa pemrograman Borland Delphi 7 dan menggunakan database SQL Server 2000 sebagai media penyimpan data. Hasil dari penelitian bahwa dapat diketahui tingkat ketepatan waktu dan nilai kelulusan mahasiswa yang berelasi dengan atribut data masuk mahasiswa. Kata Kunci : Data mining, data warehouse, kelulusan mahasiswa.
Kajian Data Mining Customer Relationship Management pada Lembaga Keuangan Mikro

Directory of Open Access Journals (Sweden)

Tikaridha Hardiani

2016-01-01

Full Text Available Companies are required to be ready to face the competition will be intense with other companies, including micro-finance institutions. Faced more intense competition, has led to many businesses in microfinance institutions find profitable strategy to distinguish from the others. Strategy that can be applied is implementing Customer Relationship Management (CRM and data mining. Data mining can be used to microfinance institutions that have a large enough data. Determine the potential customers with customer segmentation can help the decision-making marketing strategy that will be implemented . This paper discusses several data mining techniques that can be used for customer segmentation. Proposed method of data mining technique is fuzzy clustering with fuzzy C-Means algorithm and fuzzy RFM. Keywords : Customer relationship management; Data mining; Fuzzy clustering; Micro-finance institutions; Fuzzy C-Means; Fuzzy RFM
A large channel count multi client data acquisition system for superconducting magnet system of SST-1

International Nuclear Information System (INIS)

Doshi, K.; Pradhan, S.; Masand, H.; Khristi, Y.; Dhongde, J.; Sharma, A.; Parghi, B.; Varmora, P.; Prasad, U.; Patel, D.

2012-01-01

The magnet system of the Steady-state Superconducting Tokamak-1 at the Institute for Plasma Research, Gandhinagar, India, consists of sixteen Toroidal field and nine Poloidal field Superconducting coils together with a pair of resistive PF coils, an air core ohmic transformer and a pair of vertical field coils. These coils are instrumented with various cryogenic grade sensors and voltage taps to monitor its operating status and health during different operational scenarios. A VME based data acquisition system with remote system architecture is implemented for data acquisition and control of the complete magnet operation. Client-Server based architecture is implemented with remote hardware configuration and continuous online/offline monitoring. A JAVA based platform independent client application is developed for data analysis and data plotting. The server has multiple data pipeline architecture to send data to storage database, online plotting application, numerical display screen, and run time calculation. This paper describes software architecture, design and implementation of the data acquisition system. (author)
Data Mining in Education : A Review on the Knowledge Discovery Perspective

OpenAIRE

Pratiyush Guleria; Manu Sood

2014-01-01

Knowledge Discovery in Databases is the process of finding knowledge in massive amount of data where data mining is the core of this process. Data minin g can be used to mine understandable meaningful patterns from large databases and these patterns ma y then be converted into knowledge.Data mining is t he process of extracting the information and patterns derived by the KDD process which helps in crucial decision-making.Data mining works with data warehou se and...
Development of data acquisition system for CSNS 3He detector

International Nuclear Information System (INIS)

Zhao Dongxu; Zhang Hongyu

2012-01-01

This paper introduces the research and development of data acquisition system of CSNS 3 He detector prototype. This system provides high performance data acquisition capability of CSNS 3 He detector, as well as several performance tests of electronics prototype. This data acquisition system establishes foundation for the later data acquisition development. (authors)
Time Dependent Data Mining in RAVEN

Energy Technology Data Exchange (ETDEWEB)

Cogliati, Joshua Joseph [Idaho National Lab. (INL), Idaho Falls, ID (United States); Chen, Jun [Idaho National Lab. (INL), Idaho Falls, ID (United States); Patel, Japan Ketan [Idaho National Lab. (INL), Idaho Falls, ID (United States); Mandelli, Diego [Idaho National Lab. (INL), Idaho Falls, ID (United States); Maljovec, Daniel Patrick [Idaho National Lab. (INL), Idaho Falls, ID (United States); Alfonsi, Andrea [Idaho National Lab. (INL), Idaho Falls, ID (United States); Talbot, Paul William [Idaho National Lab. (INL), Idaho Falls, ID (United States); Rabiti, Cristian [Idaho National Lab. (INL), Idaho Falls, ID (United States)

2016-09-01

RAVEN is a generic software framework to perform parametric and probabilistic analysis based on the response of complex system codes. The goal of this type of analyses is to understand the response of such systems in particular with respect their probabilistic behavior, to understand their predictability and drivers or lack of thereof. Data mining capabilities are the cornerstones to perform such deep learning of system responses. For this reason static data mining capabilities were added last fiscal year (FY 15). In real applications, when dealing with complex multi-scale, multi-physics systems it seems natural that, during transients, the relevance of the different scales, and physics, would evolve over time. For these reasons the data mining capabilities have been extended allowing their application over time. In this writing it is reported a description of the new RAVEN capabilities implemented with several simple analytical tests to explain their application and highlight the proper implementation. The report concludes with the application of those newly implemented capabilities to the analysis of a simulation performed with the Bison code.
Draft Automatic Data Acquisition System Plan

International Nuclear Information System (INIS)

1987-04-01

This Automatic Data Acquisition System (ADAS) Plan has been prepared in support of the requirement for detailed site characterization of the Deaf Smith County candidate repository site in salt, and describes the data acquisition system which will be used for unattended data collection from the geotechnical instrumentation installed at the site. Section 1.1 discusses the programmatic background to the plan, Section 1.2 presents the scope and purpose of the plan, and the organization of the document is given in Section 1.3. 31 refs., 34 figs., 8 tabs
Do the earthworm caused soil modifications alter course of vegetation succession in post mining sites?

Czech Academy of Sciences Publication Activity Database

Roubíčková, A.; Prach, K.; Kaneda, Satoshi; Mudrák, Ondřej; Frouz, Jan

2008-01-01

Roč. 10, - (2008) ISSN 1029-7006. [EGU General Assembly 2008. 13.04.2008-18.04.2008, Vienna] Institutional research plan: CEZ:AV0Z60660521 Keywords : earthworms * vegetation succession * post mining sites Subject RIV: EH - Ecology, Behaviour
The NUSTAR data acquisition

Energy Technology Data Exchange (ETDEWEB)

Loeher, B.; Toernqvist, H.T. [TU Darmstadt (Germany); GSI (Germany); Agramunt, J. [IFIC, CSIC (Spain); Bendel, M.; Gernhaeuser, R.; Le Bleis, T.; Winkel, M. [TU Muenchen (Germany); Charpy, A.; Heinz, A.; Johansson, H.T. [Chalmers University of Technology (Sweden); Coleman-Smith, P.; Lazarus, I.H.; Pucknell, V.F.E. [STFC Daresbury (United Kingdom); Czermak, A. [IFJ (Poland); Kurz, N.; Nociforo, C.; Pietri, S.; Schaffner, H.; Simon, H. [GSI (Germany); Scheit, H. [TU Darmstadt (Germany); Taieb, J. [CEA (France)

2015-07-01

The diversity of upcoming experiments within the NUSTAR collaboration, including experiments in storage rings, reactions at relativistic energies and high-precision spectroscopy, is reflected in the diversity of the required detection systems. A challenging task is to incorporate the different needs of individual detectors within the unified NUSTAR Data AcQuisition (NDAQ). NDAQ takes up this challenge by providing a high degree of availability via continuously running systems, high flexibility via experiment-specific configuration files for data streams and trigger logic, distributed timestamps and trigger information on km distances, all built on the solid basis of the GSI Multi-Branch System. NDAQ ensures interoperability between individual NUSTAR detectors and allows merging of formerly separate data streams according to the needs of all experiments, increasing reliability in NUSTAR data acquisition. An overview of the NDAQ infrastructure and the current progress is presented. The NUSTAR (NUclear STructure, Astrophysics and Reactions) collaboration represents one of the four pillars motivating the construction of the international FAIR facility. The diversity of NUSTAR experiments, including experiments in storage rings, reactions at relativistic energies and high-precision spectroscopy, is reflected in the diversity of the required detection systems. A challenging task is to incorporate the different needs of individual detectors and components under the umbrella of the unified NUSTAR Data AQuisition (NDAQ) infrastructure. NDAQ takes up this challenge by providing a high degree of availability via continuously running systems, high flexibility via experiment-specific configuration files for data streams and trigger logic, and distributed time stamps and trigger information on km distances, all built on the solid basis of the GSI Multi-Branch System (MBS). NDAQ ensures interoperability between individual NUSTAR detectors and allows merging of formerly separate
Conference data acquisition process at CERN

CERN Multimedia

CERN. Geneva

2002-01-01

Will describe the data acquisition process set up at CERN (and tools developed for this purpose) and propose a MaC capture module based on CERN's experience. Will also focus on the video acquisition and how we can improve it to produce better InDiCo analysis test videos.
Post-Acquisition Release of Glutamate and Norepinephrine in the Amygdala Is Involved in Taste-Aversion Memory Consolidation

Science.gov (United States)

Guzman-Ramos, Kioko; Osorio-Gomez, Daniel; Moreno-Castilla, Perla; Bermudez-Rattoni, Federico

2012-01-01

Amygdala activity mediates the acquisition and consolidation of emotional experiences; we have recently shown that post-acquisition reactivation of this structure is necessary for the long-term storage of conditioned taste aversion (CTA). However, the specific neurotransmitters involved in such reactivation are not known. The aim of the present…
Investigation of subsidence event over multiple seam mining area

International Nuclear Information System (INIS)

Kohli, K.K.

1999-01-01

An investigation was performed to determine the sequence of events which caused the 1987 surface subsidence and related damage to several homes in Walker County, Alabama, USA. Surface affects compared to mine maps indicated the subsidence to be mine related. However, two coal seams had been worked under this area. The upper seam, the American seam, ranged from 250 to 280 feet beneath the surface in the area in question. It was mined-out before 1955 by room-and-pillar method leaving in place narrow-long pillars to support the overburden strata, and abandoned in 1955. The lower seam, the Mary Lee seam, ranged from 650 to 700 feet beneath the surface. The Mary Lee seam had been abandoned in 1966 and subsequently became flooded. The dewatering of the Mary Lee seam workings in 1985 caused the submerged pillars to be exposed to the atmosphere. Due to multiple seam mining and the fact that workings had been inundated then dewatered, a subsurface investigation ensued to determine the sequence and ultimate cause of surface subsidence. Core sample tests with fracture analysis in conjunction with down-the-hole TV camera inspections provided necessary information to determine that the subsidence started in the lower seam and progressed through the upper coal seam to the surface. Evidence from the investigation program established that dewatering of the lower seam workings caused the marginally stable support pillars and the roof to collapse. This failure triggered additional subsidence in the upper seam which broadened the area of influence at the surface

Coherent diffraction microscopy at SPring-8: instrumentation, data acquisition and data analysis

International Nuclear Information System (INIS)

Xu, Rui; Salha, Sara; Raines, Kevin S.; Jiang, Huaidong; Chen, Chien-Chun; Takahashi, Yukio; Kohmura, Yoshiki; Nishino, Yoshinori; Song, Changyong; Ishikawa, Tetsuya; Miao, Jianwei

2011-01-01

An instrumentation and data analysis review of coherent diffraction microscopy at SPring-8 is given. This work will be of interest to those who want to apply coherent diffraction imaging to studies of materials science and biological samples. Since the first demonstration of coherent diffraction microscopy in 1999, this lensless imaging technique has been experimentally refined by continued developments. Here, instrumentation and experimental procedures for measuring oversampled diffraction patterns from non-crystalline specimens using an undulator beamline (BL29XUL) at SPring-8 are presented. In addition, detailed post-experimental data analysis is provided that yields high-quality image reconstructions. As the acquisition of high-quality diffraction patterns is at least as important as the phase-retrieval procedure to guarantee successful image reconstructions, this work will be of interest for those who want to apply this imaging technique to materials science and biological samples
Assessing Subjectivity in Sensor Data Post Processing via a Controlled Experiment

Science.gov (United States)

Jones, A. S.; Horsburgh, J. S.; Eiriksson, D.

2017-12-01

Environmental data collected by in situ sensors must be reviewed to verify validity, and conducting quality control often requires making edits in post processing to generate approved datasets. This process involves decisions by technicians, data managers, or data users on how to handle problematic data. Options include: removing data from a series, retaining data with annotations, and altering data based on algorithms related to adjacent data points or the patterns of data at other locations or of other variables. Ideally, given the same dataset and the same quality control guidelines, multiple data quality control technicians would make the same decisions in data post processing. However, despite the development and implementation of guidelines aimed to ensure consistent quality control procedures, we have faced ambiguity when performing post processing, and we have noticed inconsistencies in the practices of individuals performing quality control post processing. Technicians with the same level of training and using the same input datasets may produce different results, affecting the overall quality and comparability of finished data products. Different results may also be produced by technicians that do not have the same level of training. In order to assess the effect of subjective decision making by the individual technician on the end data product, we designed an experiment where multiple users performed quality control post processing on the same datasets using a consistent set of guidelines, field notes, and tools. We also assessed the effect of technician experience and training by conducting the same procedures with a group of novices unfamiliar with the data and the quality control process and compared their results to those generated by a group of more experienced technicians. In this presentation, we report our observations of the degree of subjectivity in sensor data post processing, assessing and quantifying the impacts of individual technician as
Data acquisition for X ray microprobe. User's manual

International Nuclear Information System (INIS)

2002-01-01

A modified data acquisition software for X ray microprobe was developed by the Physics Group, Instrumentation Unit, IAEA Laboratories at Seibersdorf, with assistance from M. Bogovac, Croatia. The software consists of data acquisition (scanning and calibration), automatic positioning and micro-movement of sample, data reduction and evaluation. The acquisition software was designed in order to support different measurement set-ups which are applied in low-energy nuclear physics. The modification was done in 1999-2000 under the projects Nuclear Spectrometry and Utilization of Particle Accelerators. The manual supersedes the first version entitled Microanalysis Data Acquisition and Control Program published under Computer Manual Series, No. 9 in 1996. The software described in this manual is freely available from the IAEA upon request
Data Warehouse, Data Mining Dan Konsep Cross-Selling Pada Analisis Penjualan Produk

Directory of Open Access Journals (Sweden)

Eka Miranda

2010-12-01

Full Text Available This paper is about designing and implementing data warehousing and data mining, along with their roles in supporting decision-making related to sales product analysis in cross-selling concept of PT XYZ. The database the company used is not supporting data analysis and decision-making. Therefore, it made a data warehousing design that could be used to keep data in a huge amount and could give report and answer from user’s questions in ad hoc. The method is used to design and implement data warehousing and data mining which consists of literature study, company problem analysis, and data warehousing design, and testing result. The writing results are a data warehousing design and data mining and also the implementation of cross-selling concept to analysis the sales, purchases, and customers’ cancellation data. The data could be showed and analyzed from some point of views that could help managers to analyse and acknowledge more information.
The FINUDA data acquisition system

International Nuclear Information System (INIS)

Cerello, P.; Marcello, S.; Filippini, V.; Fiore, L.; Gianotti, P.; Raimondo, A.

1996-07-01

A parallel scalable Data Acquisition System, based on VME, has been developed to be used in the FINUDA experiment, scheduled to run at the DAPHNE machine at Frascati starting from 1997. The acquisition software runs on embedded RTPC 8067 processors using the LynxOS operating system. The readout of event fragments is coordinated by a suitable trigger Supervisor. data read by different controllers are transported via dedicated bus to a Global Event Builder running on a UNIX machine. Commands from and to VME processors are sent via socket based network protocols. The network hardware is presently ethernet, but it can easily changed to optical fiber
Design and application of pulse information acquisition and analysis ...

African Journals Online (AJOL)

... two-dimensional information acquisition, multiplex signals combination and deep data mining. Conclusions: The newly developed system could translate the pulse signals into digital, visual and measurable motion information of vessel. Keywords: Visualized pulse information; Radial artery; B mode ultrasound; Traditional ...
Analysis of Occupational Accidents in Underground and Surface Mining in Spain Using Data-Mining Techniques.

Science.gov (United States)

Sanmiquel, Lluís; Bascompta, Marc; Rossell, Josep M; Anticoi, Hernán Francisco; Guash, Eduard

2018-03-07

An analysis of occupational accidents in the mining sector was conducted using the data from the Spanish Ministry of Employment and Social Safety between 2005 and 2015, and data-mining techniques were applied. Data was processed with the software Weka. Two scenarios were chosen from the accidents database: surface and underground mining. The most important variables involved in occupational accidents and their association rules were determined. These rules are composed of several predictor variables that cause accidents, defining its characteristics and context. This study exposes the 20 most important association rules in the sector-either surface or underground mining-based on the statistical confidence levels of each rule as obtained by Weka. The outcomes display the most typical immediate causes, along with the percentage of accidents with a basis in each association rule. The most important immediate cause is body movement with physical effort or overexertion, and the type of accident is physical effort or overexertion. On the other hand, the second most important immediate cause and type of accident are different between the two scenarios. Data-mining techniques were chosen as a useful tool to find out the root cause of the accidents.
Mining Outlier Data in Mobile Internet-Based Large Real-Time Databases

Directory of Open Access Journals (Sweden)

Xin Liu

2018-01-01

Full Text Available Mining outlier data guarantees access security and data scheduling of parallel databases and maintains high-performance operation of real-time databases. Traditional mining methods generate abundant interference data with reduced accuracy, efficiency, and stability, causing severe deficiencies. This paper proposes a new mining outlier data method, which is used to analyze real-time data features, obtain magnitude spectra models of outlier data, establish a decisional-tree information chain transmission model for outlier data in mobile Internet, obtain the information flow of internal outlier data in the information chain of a large real-time database, and cluster data. Upon local characteristic time scale parameters of information flow, the phase position features of the outlier data before filtering are obtained; the decision-tree outlier-classification feature-filtering algorithm is adopted to acquire signals for analysis and instant amplitude and to achieve the phase-frequency characteristics of outlier data. Wavelet transform threshold denoising is combined with signal denoising to analyze data offset, to correct formed detection filter model, and to realize outlier data mining. The simulation suggests that the method detects the characteristic outlier data feature response distribution, reduces response time, iteration frequency, and mining error rate, improves mining adaptation and coverage, and shows good mining outcomes.
Report from Dagstuhl Seminar 12331 Mobility Data Mining and Privacy

OpenAIRE

Clifton, Christopher W.; Kuijpers, Bart; Morik, Katharina; Saygin, Yucel

2012-01-01

This report documents the program and the outcomes of Dagstuhl Seminar 12331 “Mobility Data Mining and Privacy”. Mobility data mining aims to extract knowledge from movement behaviour of people, but this data also poses novel privacy risks. This seminar gathered a multidisciplinary team for a conversation on how to balance the value in mining mobility data with privacy issues. The seminar focused on four key issues: Privacy in vehicular data, in cellular data, context- dependent privacy, and ...
Mining algorithm for association rules in big data based on Hadoop

Science.gov (United States)

Fu, Chunhua; Wang, Xiaojing; Zhang, Lijun; Qiao, Liying

2018-04-01

In order to solve the problem that the traditional association rules mining algorithm has been unable to meet the mining needs of large amount of data in the aspect of efficiency and scalability, take FP-Growth as an example, the algorithm is realized in the parallelization based on Hadoop framework and Map Reduce model. On the basis, it is improved using the transaction reduce method for further enhancement of the algorithm's mining efficiency. The experiment, which consists of verification of parallel mining results, comparison on efficiency between serials and parallel, variable relationship between mining time and node number and between mining time and data amount, is carried out in the mining results and efficiency by Hadoop clustering. Experiments show that the paralleled FP-Growth algorithm implemented is able to accurately mine frequent item sets, with a better performance and scalability. It can be better to meet the requirements of big data mining and efficiently mine frequent item sets and association rules from large dataset.
Data mining in time series databases

CERN Document Server

Kandel, Abraham; Bunke, Horst

2004-01-01

Adding the time dimension to real-world databases produces Time SeriesDatabases (TSDB) and introduces new aspects and difficulties to datamining and knowledge discovery. This book covers the state-of-the-artmethodology for mining time series databases. The novel data miningmethods presented in the book include techniques for efficientsegmentation, indexing, and classification of noisy and dynamic timeseries. A graph-based method for anomaly detection in time series isdescribed and the book also studies the implications of a novel andpotentially useful representation of time series as strings. Theproblem of detecting changes in data mining models that are inducedfrom temporal databases is additionally discussed.
Data Mine and Forget It?: A Cautionary Tale

Science.gov (United States)

Tada, Yuri; Kraft, Norbert Otto; Orasanu, Judith M.

2011-01-01

With the development of new technologies, data mining has become increasingly popular. However, caution should be exercised in choosing the variables to include in data mining. A series of regression trees was created to demonstrate the change in the selection by the program of significant predictors based on the nature of variables.
Randomized algorithms in automatic control and data mining

CERN Document Server

Granichin, Oleg; Toledano-Kitai, Dvora

2015-01-01

In the fields of data mining and control, the huge amount of unstructured data and the presence of uncertainty in system descriptions have always been critical issues. The book Randomized Algorithms in Automatic Control and Data Mining introduces the readers to the fundamentals of randomized algorithm applications in data mining (especially clustering) and in automatic control synthesis. The methods proposed in this book guarantee that the computational complexity of classical algorithms and the conservativeness of standard robust control techniques will be reduced. It is shown that when a problem requires "brute force" in selecting among options, algorithms based on random selection of alternatives offer good results with certain probability for a restricted time and significantly reduce the volume of operations.
Event metadata records as a testbed for scalable data mining

International Nuclear Information System (INIS)

Gemmeren, P van; Malon, D

2010-01-01

At a data rate of 200 hertz, event metadata records ('TAGs,' in ATLAS parlance) provide fertile grounds for development and evaluation of tools for scalable data mining. It is easy, of course, to apply HEP-specific selection or classification rules to event records and to label such an exercise 'data mining,' but our interest is different. Advanced statistical methods and tools such as classification, association rule mining, and cluster analysis are common outside the high energy physics community. These tools can prove useful, not for discovery physics, but for learning about our data, our detector, and our software. A fixed and relatively simple schema makes TAG export to other storage technologies such as HDF5 straightforward. This simplifies the task of exploiting very-large-scale parallel platforms such as Argonne National Laboratory's BlueGene/P, currently the largest supercomputer in the world for open science, in the development of scalable tools for data mining. Using a domain-neutral scientific data format may also enable us to take advantage of existing data mining components from other communities. There is, further, a substantial literature on the topic of one-pass algorithms and stream mining techniques, and such tools may be inserted naturally at various points in the event data processing and distribution chain. This paper describes early experience with event metadata records from ATLAS simulation and commissioning as a testbed for scalable data mining tool development and evaluation.
Introduction to the special section on educational data mining

NARCIS (Netherlands)

Calders, T.G.K.; Pechenizkiy, M.

2012-01-01

Educational Data Mining (EDM) is an emerging multidisciplinary research area, in which methods and techniques for exploring data originating from various educational information systems have been developed. EDM is both a learning science, as well as a rich application area for data mining, due to
HSM: Heterogeneous Subspace Mining in High Dimensional Data

DEFF Research Database (Denmark)

Müller, Emmanuel; Assent, Ira; Seidl, Thomas

2009-01-01

Heterogeneous data, i.e. data with both categorical and continuous values, is common in many databases. However, most data mining algorithms assume either continuous or categorical attributes, but not both. In high dimensional data, phenomena due to the "curse of dimensionality" pose additional...... challenges. Usually, due to locally varying relevance of attributes, patterns do not show across the full set of attributes. In this paper we propose HSM, which defines a new pattern model for heterogeneous high dimensional data. It allows data mining in arbitrary subsets of the attributes that are relevant...... for the respective patterns. Based on this model we propose an efficient algorithm, which is aware of the heterogeneity of the attributes. We extend an indexing structure for continuous attributes such that HSM indexing adapts to different attribute types. In our experiments we show that HSM efficiently mines...
ThaleMine: A Warehouse for Arabidopsis Data Integration and Discovery.

Science.gov (United States)

Krishnakumar, Vivek; Contrino, Sergio; Cheng, Chia-Yi; Belyaeva, Irina; Ferlanti, Erik S; Miller, Jason R; Vaughn, Matthew W; Micklem, Gos; Town, Christopher D; Chan, Agnes P

2017-01-01

ThaleMine (https://apps.araport.org/thalemine/) is a comprehensive data warehouse that integrates a wide array of genomic information of the model plant Arabidopsis thaliana. The data collection currently includes the latest structural and functional annotation from the Araport11 update, the Col-0 genome sequence, RNA-seq and array expression, co-expression, protein interactions, homologs, pathways, publications, alleles, germplasm and phenotypes. The data are collected from a wide variety of public resources. Users can browse gene-specific data through Gene Report pages, identify and create gene lists based on experiments or indexed keywords, and run GO enrichment analysis to investigate the biological significance of selected gene sets. Developed by the Arabidopsis Information Portal project (Araport, https://www.araport.org/), ThaleMine uses the InterMine software framework, which builds well-structured data, and provides powerful data query and analysis functionality. The warehoused data can be accessed by users via graphical interfaces, as well as programmatically via web-services. Here we describe recent developments in ThaleMine including new features and extensions, and discuss future improvements. InterMine has been broadly adopted by the model organism research community including nematode, rat, mouse, zebrafish, budding yeast, the modENCODE project, as well as being used for human data. ThaleMine is the first InterMine developed for a plant model. As additional new plant InterMines are developed by the legume and other plant research communities, the potential of cross-organism integrative data analysis will be further enabled. © The Author 2016. Published by Oxford University Press on behalf of Japanese Society of Plant Physiologists. All rights reserved. For permissions, please email: journals.permissions@oup.com.
A Survey on Accessing Data over Cloud Environment using Data mining Algorithms

OpenAIRE

B.Prasanalakshmi; A.Selvaraj

2015-01-01

In today's world to access the large set of data is more complex, because the data may be structured and unstructured like in the form of text, images, videos, etc., it cannot be controlled from the internet users this is known as Big data. Useful data can be accessed through extracting from big data with the help of data mining algorithms. Data mining is a technique for determine the patterns; classify the data, clustering from the large set of data. In this paper we will discuss how large s...
Overview of data acquisition system for SST-1 diagnostics

International Nuclear Information System (INIS)

Sharma, Manika; Mansuri, Imran; Raval, Tushar; Sharma, A.L; Pradhan, S.

2016-01-01

Highlights: • An account of architecture and data acquisition activities of SST-1 data acquisition system (DAS) for SST-1 diagnostics and subsystems. • PXI based Data acquisition system and CAMAC based Data acquisition system for slow and fast plasma diagnostics. • SST-1 DAS interface and its communication with SST-1 central control system. Integration of SST-1 DAS with timing system. • SST-1 DAS data archival and data analysis. - Abstract: The recent first phase operations of SST-1 in short pulse mode have provided an excellent opportunity for the essential initial tests and benchmark of the SST-1 Data Acquisition System. This paper describes the SST-1 Data Acquisition systems (DAS), which with its heterogeneous composition and distributed architecture, aims to cover a wide range of slow to fast channels interfaced with a large set of diagnostics. The DAS also provides the essential user interface for data acquisition to cater both on and off-line data usage. The central archiving and retrieval service is based on a dual step architecture involving a combination of Network Attached Server (NAS) and a Storage Area Network (SAN). SST-1 Data Acquisition Systems have been reliably operated in the SST-1 experimental campaigns. At present different distributed DAS caters the need of around 130 channels from different SST-1 diagnostics and its subsystems. PXI based DAS and CAMAC based DAS have been chosen to cater the need, with sampling rates varying from 10Ksamples/sec to 1Msamples/sec. For these large sets of channels acquiring from individual diagnostics and subsystems has been a combined setup, subjected to a gradual phase of optimization and tests resulting into a series of improvisations over the recent operations. In order to facilitate a reliable data acquisition, the model further integrates the objects of the systems with the Central Control System of SST-1 using the TCP/IP communication. The associated DAS software essentially addresses the
Overview of data acquisition system for SST-1 diagnostics

Energy Technology Data Exchange (ETDEWEB)

Sharma, Manika, E-mail: bithi@ipr.res.in; Mansuri, Imran; Raval, Tushar; Sharma, A.L; Pradhan, S.

2016-11-15

Highlights: • An account of architecture and data acquisition activities of SST-1 data acquisition system (DAS) for SST-1 diagnostics and subsystems. • PXI based Data acquisition system and CAMAC based Data acquisition system for slow and fast plasma diagnostics. • SST-1 DAS interface and its communication with SST-1 central control system. Integration of SST-1 DAS with timing system. • SST-1 DAS data archival and data analysis. - Abstract: The recent first phase operations of SST-1 in short pulse mode have provided an excellent opportunity for the essential initial tests and benchmark of the SST-1 Data Acquisition System. This paper describes the SST-1 Data Acquisition systems (DAS), which with its heterogeneous composition and distributed architecture, aims to cover a wide range of slow to fast channels interfaced with a large set of diagnostics. The DAS also provides the essential user interface for data acquisition to cater both on and off-line data usage. The central archiving and retrieval service is based on a dual step architecture involving a combination of Network Attached Server (NAS) and a Storage Area Network (SAN). SST-1 Data Acquisition Systems have been reliably operated in the SST-1 experimental campaigns. At present different distributed DAS caters the need of around 130 channels from different SST-1 diagnostics and its subsystems. PXI based DAS and CAMAC based DAS have been chosen to cater the need, with sampling rates varying from 10Ksamples/sec to 1Msamples/sec. For these large sets of channels acquiring from individual diagnostics and subsystems has been a combined setup, subjected to a gradual phase of optimization and tests resulting into a series of improvisations over the recent operations. In order to facilitate a reliable data acquisition, the model further integrates the objects of the systems with the Central Control System of SST-1 using the TCP/IP communication. The associated DAS software essentially addresses the

Optimization of Hierarchical System for Data Acquisition

Directory of Open Access Journals (Sweden)

V. Novotny

2011-04-01

Full Text Available Television broadcasting over IP networks (IPTV is one of a number of network applications that are except of media distribution also interested in data acquisition from group of information resources of variable size. IP-TV uses Real-time Transport Protocol (RTP protocol for media streaming and RTP Control Protocol (RTCP protocol for session quality feedback. Other applications, for example sensor networks, have data acquisition as the main task. Current solutions have mostly problem with scalability - how to collect and process information from large amount of end nodes quickly and effectively? The article deals with optimization of hierarchical system of data acquisition. Problem is mathematically described, delay minima are searched and results are proved by simulations.
A Big Data Platform for Storing, Accessing, Mining and Learning Geospatial Data

Science.gov (United States)

Yang, C. P.; Bambacus, M.; Duffy, D.; Little, M. M.

2017-12-01

Big Data is becoming a norm in geoscience domains. A platform that is capable to effiently manage, access, analyze, mine, and learn the big data for new information and knowledge is desired. This paper introduces our latest effort on developing such a platform based on our past years' experiences on cloud and high performance computing, analyzing big data, comparing big data containers, and mining big geospatial data for new information. The platform includes four layers: a) the bottom layer includes a computing infrastructure with proper network, computer, and storage systems; b) the 2nd layer is a cloud computing layer based on virtualization to provide on demand computing services for upper layers; c) the 3rd layer is big data containers that are customized for dealing with different types of data and functionalities; d) the 4th layer is a big data presentation layer that supports the effient management, access, analyses, mining and learning of big geospatial data.
Implementasi Data Warehouse dan Data Mining: Studi Kasus Analisis Peminatan Studi Siswa

Directory of Open Access Journals (Sweden)

Eka Miranda

2011-06-01

Full Text Available This paper discusses the implementation of data mining and their role in helping decision-making related to students’ specialization program selection. Currently, the university uses a database to store records of transactions which can not directly be used to assist analysis and decision making. Based on these issues then made the data warehouse design used to store large amounts of data and also has the potential to gain new data distribution perspectives and allows to answer the ad hoc question as well as to perform data analysis. The method used consists of: record analysis related to students’ academic achievement, designing data warehouse and data mining. The paper’s results are in a form of data warehouse and data mining design and its implementation with the classification techniques and association rules. From these results can be seen the students’ tendency and pattern background in choosing the specialization, to help them make decisions.
The viability of business data mining in the sports environment ...

African Journals Online (AJOL)

Data mining can be viewed as the process of extracting previously unknown information from large databases and utilising this information to make crucial business decisions (Simoudis, 1996: 26). This paper considers the viability of using data mining tools and techniques in sports, particularly with regard to mining the ...
A tomograph VMEbus parallel processing data acquisition system

International Nuclear Information System (INIS)

Atkins, M.S.; Wilkinson, N.A.; Rogers, J.G.

1988-11-01

This paper describes a VME based data acquisition system suitable for the development of Positron Volume Imaging tomographs which use 3-D data for improved image resolution over slice-oriented tomographs. The data acquisition must be flexible enough to accommodate several 3-D reconstruction algorithms; hence, a software-based system is most suitable. Furthermore, because of the increased dimensions and resolution of volume imaging tomographs, the raw data event rate is greater than that of slice-oriented machines. These dual requirements are met by our data acquisition systems. Flexibility is achieved through an array of processors connected over a VMEbus, operating asynchronously and in parallel. High raw data throughput is achieved using a dedicated high speed data transfer device available for the VMEbus. The device can attain a raw data rate of 2.5 million coincidence events per second for raw events per second for raw events which are 64 bits wide. Real-time data acquisition and pre-processing requirements can be met by about forty 20 MHz Motorola 68020/68881 processors
A data acquisition system based on a personal computer

International Nuclear Information System (INIS)

Omata, K.; Fujita, Y.; Yoshikawa, N.; Sekiguchi, M.; Shida, Y.

1991-07-01

A versatile and flexible data acquisition system KODAQ (Kakuken Online Data AcQuisition system) has been developed. The system runs with CAMAC and a most popular Japanese personal computer, PC9801 (NEC), similar to the IBM PC/AT. The system is designed to set up easily a data acquisition system for various kinds of nuclear-physics experiments. (author)
Real-time UNIX in HEP data acquisition

International Nuclear Information System (INIS)

Buono, S.; Gaponenko, I.; Jones, R.; Mapelli, L.; Mornacchi, G.; Prigent, D.; Sanchez-Corral, E.; Skiadelli, M.; Toppers, A.; Duval, P.Y.; Ferrato, D.; Le Van Suu, A.; Qian, Z.; Rondot, C.; Ambrosini, G.; Fumagalli, G.; Aguer, M.; Huet, M.

1994-01-01

Today's experimentation in high energy physics is characterized by an increasing need for sensitivity to rare phenomena and complex physics signatures, which require the use of huge and sophisticated detectors and consequently a high performance readout and data acquisition. Multi-level triggering, hierarchical data collection and an always increasing amount of processing power, distributed throughout the data acquisition layers, will impose a number of features on the software environment, especially the need for a high level of standardization. Real-time UNIX seems, today, the best solution for the platform independence, operating system interface standards and real-time features necessary for data acquisition in HEP experiments. We present the results of the evaluation, in a realistic application environment, of a Real-Time UNIX operating system: the EP/LX real-time UNIX system. ((orig.))
Isothermal thermogravimetric data acquisition analysis system

Science.gov (United States)

Cooper, Kenneth, Jr.

1991-01-01

The description of an Isothermal Thermogravimetric Analysis (TGA) Data Acquisition System is presented. The system consists of software and hardware to perform a wide variety of TGA experiments. The software is written in ANSI C using Borland's Turbo C++. The hardware consists of a 486/25 MHz machine with a Capital Equipment Corp. IEEE488 interface card. The interface is to a Hewlett Packard 3497A data acquisition system using two analog input cards and a digital actuator card. The system provides for 16 TGA rigs with weight and temperature measurements from each rig. Data collection is conducted in three phases. Acquisition is done at a rapid rate during initial startup, at a slower rate during extended data collection periods, and finally at a fast rate during shutdown. Parameters controlling the rate and duration of each phase are user programmable. Furnace control (raising and lowering) is also programmable. Provision is made for automatic restart in the event of power failure or other abnormal terminations. Initial trial runs were conducted to show system stability.
Challenges in computational statistics and data mining

CERN Document Server

Mielniczuk, Jan

2016-01-01

This volume contains nineteen research papers belonging to the areas of computational statistics, data mining, and their applications. Those papers, all written specifically for this volume, are their authors’ contributions to honour and celebrate Professor Jacek Koronacki on the occcasion of his 70th birthday. The book’s related and often interconnected topics, represent Jacek Koronacki’s research interests and their evolution. They also clearly indicate how close the areas of computational statistics and data mining are.
The ALICE data acquisition system

CERN Document Server

Carena, F; Chapeland, S; Chibante Barroso, V; Costa, F; Dénes, E; Divià, R; Fuchs, U; Grigore, A; Kiss, T; Simonetti, G; Soós, C; Telesca, A; Vande Vyvre, P; Von Haller, B

2014-01-01

In this paper we describe the design, the construction, the commissioning and the operation of the Data Acquisition (DAQ) and Experiment Control Systems (ECS) of the ALICE experiment at the CERN Large Hadron Collider (LHC). The DAQ and the ECS are the systems used respectively for the acquisition of all physics data and for the overall control of the experiment. They are two computing systems made of hundreds of PCs and data storage units interconnected via two networks. The collection of experimental data from the detectors is performed by several hundreds of high-speed optical links. We describe in detail the design considerations for these systems handling the extreme data throughput resulting from central lead ions collisions at LHC energy. The implementation of the resulting requirements into hardware (custom optical links and commercial computing equipment), infrastructure (racks, cooling, power distribution, control room), and software led to many innovative solutions which are described together with ...
Data Mining and Complex Problems: Case Study in Composite Materials

Science.gov (United States)

Rabelo, Luis; Marin, Mario

2009-01-01

Data mining is defined as the discovery of useful, possibly unexpected, patterns and relationships in data using statistical and non-statistical techniques in order to develop schemes for decision and policy making. Data mining can be used to discover the sources and causes of problems in complex systems. In addition, data mining can support simulation strategies by finding the different constants and parameters to be used in the development of simulation models. This paper introduces a framework for data mining and its application to complex problems. To further explain some of the concepts outlined in this paper, the potential application to the NASA Shuttle Reinforced Carbon-Carbon structures and genetic programming is used as an illustration.
A Lossless Network for Data Acquisition

CERN Document Server

AUTHOR|(SzGeCERN)698154; The ATLAS collaboration; Lehmann Miotto, Giovanna

2016-01-01

The planned upgrades of the experiments at the Large Hadron Collider at CERN will require higher bandwidth networks for their data acquisition systems. The network congestion problem arising from the bursty many-to-one communication pattern, typical for these systems, will become more demanding. It is questionable whether commodity TCP/IP and Ethernet technologies in their current form will be still able to effectively adapt to the bursty traffic without losing packets due to the scarcity of buffers in the networking hardware. We continue our study of the idea of lossless switching in software running on commercial-off-the-shelf servers for data acquisition systems, using the ATLAS experiment as a case study. The flexibility of design in software, performance of modern computer platforms, and buffering capabilities constrained solely by the amount of DRAM memory are a strong basis for building a network dedicated to data acquisition with commodity hardware, which can provide reliable transport in congested co...
Data Acquisition and Flux Calculations

DEFF Research Database (Denmark)

Rebmann, C.; Kolle, O; Heinesch, B

2012-01-01

In this chapter, the basic theory and the procedures used to obtain turbulent fluxes of energy, mass, and momentum with the eddy covariance technique will be detailed. This includes a description of data acquisition, pretreatment of high-frequency data and flux calculation....
Data mining concepts, methods and applications in management and engineering design

CERN Document Server

Yin, Yong; Tang, Jiafu; Zhu, JianMing

2011-01-01

Data Mining introduces in clear and simple ways how to use existing data mining methods to obtain effective solutions for a variety of management and engineering design problems. Data Mining is organised into two parts: the first provides a focused introduction to data mining and the second goes into greater depth on subjects such as customer analysis. It covers almost all managerial activities of a company, including: * supply chain design, * product development, * manufacturing system design, * product quality control, and * preservation of privacy. Incorporating recent developments of data
Improve Data Mining and Knowledge Discovery through the use of MatLab

Science.gov (United States)

Shaykahian, Gholan Ali; Martin, Dawn Elliott; Beil, Robert

2011-01-01

Data mining is widely used to mine business, engineering, and scientific data. Data mining uses pattern based queries, searches, or other analyses of one or more electronic databases/datasets in order to discover or locate a predictive pattern or anomaly indicative of system failure, criminal or terrorist activity, etc. There are various algorithms, techniques and methods used to mine data; including neural networks, genetic algorithms, decision trees, nearest neighbor method, rule induction association analysis, slice and dice, segmentation, and clustering. These algorithms, techniques and methods used to detect patterns in a dataset, have been used in the development of numerous open source and commercially available products and technology for data mining. Data mining is best realized when latent information in a large quantity of data stored is discovered. No one technique solves all data mining problems; challenges are to select algorithms or methods appropriate to strengthen data/text mining and trending within given datasets. In recent years, throughout industry, academia and government agencies, thousands of data systems have been designed and tailored to serve specific engineering and business needs. Many of these systems use databases with relational algebra and structured query language to categorize and retrieve data. In these systems, data analyses are limited and require prior explicit knowledge of metadata and database relations; lacking exploratory data mining and discoveries of latent information. This presentation introduces MatLab(TradeMark)(MATrix LABoratory), an engineering and scientific data analyses tool to perform data mining. MatLab was originally intended to perform purely numerical calculations (a glorified calculator). Now, in addition to having hundreds of mathematical functions, it is a programming language with hundreds built in standard functions and numerous available toolboxes. MatLab's ease of data processing, visualization and
Data acquisition system for the Large Scintillating Neutrino Detector at Los Alamos

International Nuclear Information System (INIS)

Anderson, G.; Cohen, I.; Homann, B.; Smith, D.; Strossman, W.; VanDalen, G.J.; Weaver, L.S.; Evans, D.; Vernon, W.; Band, A.; Burman, R.; Chang, T.; Federspiel, F.; Foreman, W.; Gomulka, S.; Hart, G.; Kozlowski, T.; Louis, W.C.; Margulies, J.; Nuanes, A.; Sandberg, V.; Thompson, T.N.; White, D.H.; Whitehouse, D.

1992-01-01

The data acquisition system for the Large Scintillating Neutrino Detector (LSND) is described. The system collects time and charge information in real time from 1600 photomultiplier tubes and passes the data in intelligent-trigger selected time windows to analysis computers, where events are reconstructed and analyzed as candidates for a variety of neutrino-related physics processes. The system is composed of fourteen VME crates linked to a Silicon Graphics, Inc. ''4D/480'' multiprocessor computer through multiple, parallel Ethernets, and a collection of contemporary high-performance workstations
Review of Recent Development of Dynamic Wind Farm Equivalent Models Based on Big Data Mining

Science.gov (United States)

Wang, Chenggen; Zhou, Qian; Han, Mingzhe; Lv, Zhan’ao; Hou, Xiao; Zhao, Haoran; Bu, Jing

2018-04-01

Recently, the big data mining method has been applied in dynamic wind farm equivalent modeling. In this paper, its recent development with present research both domestic and overseas is reviewed. Firstly, the studies of wind speed prediction, equivalence and its distribution in the wind farm are concluded. Secondly, two typical approaches used in the big data mining method is introduced, respectively. For single wind turbine equivalent modeling, it focuses on how to choose and identify equivalent parameters. For multiple wind turbine equivalent modeling, the following three aspects are concentrated, i.e. aggregation of different wind turbine clusters, the parameters in the same cluster, and equivalence of collector system. Thirdly, an outlook on the development of dynamic wind farm equivalent models in the future is discussed.
Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining

OpenAIRE

Chen, D

2012-01-01

Many small online retailers and new entrants to the online retail sector are keen to practice data mining and consumer-centric marketing in their businesses yet technically lack the necessary knowledge and expertise to do so. In this article a case study of using data mining techniques in customer-centric business intelligence for an online retailer is presented. The main purpose of this analysis is to help the business better understand its customers and therefore conduct customer-centric ma...
Data mining in soft computing framework: a survey.

Science.gov (United States)

Mitra, S; Pal, S K; Mitra, P

2002-01-01

The present article provides a survey of the available literature on data mining using soft computing. A categorization has been provided based on the different soft computing tools and their hybridizations used, the data mining function implemented, and the preference criterion selected by the model. The utility of the different soft computing methodologies is highlighted. Generally fuzzy sets are suitable for handling the issues related to understandability of patterns, incomplete/noisy data, mixed media information and human interaction, and can provide approximate solutions faster. Neural networks are nonparametric, robust, and exhibit good learning and generalization capabilities in data-rich environments. Genetic algorithms provide efficient search algorithms to select a model, from mixed media data, based on some preference criterion/objective function. Rough sets are suitable for handling different types of uncertainty in data. Some challenges to data mining and the application of soft computing methodologies are indicated. An extensive bibliography is also included.
Uncertainty modeling for data mining a label semantics approach

CERN Document Server

Qin, Zengchang

2014-01-01

Outlining a new research direction in fuzzy set theory applied to data mining, this volume proposes a number of new data mining algorithms and includes dozens of figures and illustrations that help the reader grasp the complexities of the concepts.

Minus 3: a general purpose data acquisition system at LBL's 88''-cyclotron and superhilac

International Nuclear Information System (INIS)

Maples, C.; Sivak, J.

1979-05-01

MINUS 3 is a general, multi-tasked data acquisition package operating on the ModComp IV/25 computers at both the 88''-Cyclotron and SuperHILAC. It currently can acquire data via three different channels: interrupt; serial DMA link; and remote slave units for histogram type data. Two additional acquisition paths, CAMAC (with programmable differential branch drivers) and MODACS (for multiple CPU linkages and control) are scheduled to be added in the near future. The package operates in a prioritized, time-available mode which permits it to dynamically adapt to microscopic data rate structures due to beam characteristics at different accelerators. Special hardware has been added to the graphics system to provide enhanced high-speed interactive capability. The program framework is also designed as a parasitic environment in which users may, in parallel, attach their own specialized and independent code
The handbook of data mining

CERN Document Server

Ye, Nong

2003-01-01

This bk is the 1st comprehensive one to feature systematic coverage of the concepts, techniques, examples, issues, software tools and future advancements of data mining. The demand for DM apps are increasing in indus, gov, & academia.
Neutron flux density data acquisition system based on LabVIEW

International Nuclear Information System (INIS)

Zhao Yanhui; Zhao Xiuliang; Li Zonglun; Liang Fengyan; Liu Liyan

2011-01-01

In the LabVIEW software, combined with PCI-6251 data acquisition card, VI of neutron flux density data acquisition is realized by DAQmx data acquisition functions. VI is composed of front panel and block diagram. The data collected can be displayed in the forms of the data curve and the data control, and saved in the form of files. Test results show that the frequency of output signal in NI ELVIS can be accurately measured by the system, realizing neutron flux density data acquisition based on LabVIEW. (authors)
Highly Robust Methods in Data Mining

Czech Academy of Sciences Publication Activity Database

Kalina, Jan

2013-01-01

Roč. 8, č. 1 (2013), s. 9-24 ISSN 1452-4864 Institutional support: RVO:67985807 Keywords : data mining * robust statistics * high-dimensional data * cluster analysis * logistic regression * neural networks Subject RIV: BB - Applied Statistics, Operational Research
Data acquisition system in TPE-1RM15

International Nuclear Information System (INIS)

Yagi, Yasuyuki; Yahagi, Eiichi; Hirano, Yoichi; Shimada, Toshio; Hirota, Isao; Maejima, Yoshiki

1991-01-01

The data acquisition system for TPE-1RM15 reversed field pinch machine had been developed and has recently been completed. Thd data to be acquired consist of many channels of time series data which come from plasma diagnostics. The newly developed data acquisition system uses CAMAC (Computer Automated Measurement And Control) system as a front end data acquisition system and micro-VAX II for control, file management and analyses. Special computer programs, DAQR/D, have been developed for data acquisition routine. Experimental setting and process controlling items are managed by a parameter database in a shared common region and every task can easily refer to it. The acquired data are stored into a mass storage system (total of 1.3GBytes plus a magnetic tape system) including an optical disk system, which can save storage space and allow quick reference. At present, the CAMAC system has 88 (1MHz sampling) and 64(5kHz sampling) channels corresponding to 1.6 MBytes per shot. The data acquisition system can finish one routine within 5 minutes with 1.6MBytes data depending on the amount of graphic outputs. Hardwares and softwares of the system are specified so that the system can be easily expanded. The computer is connected to the AIST Ethernet and the system can be remotely accessed and the acquired data can be transferred to the mainframes on the network. Details about specifications and performance of the system are given in this report. (author)
Is Europe Falling Behind in Data Mining? Copyright’s Impact on Data Mining in Academic Research

NARCIS (Netherlands)

Handke, C.; Guibault, L.; Vallbé, J.J.; Schmidt, B.; Dobreva, M.

2015-01-01

With the diffusion of digital information technology, data mining (DM) is widely expected to increase the productivity of all kinds of research activities. Based on bibliometric data, we demonstrate that the share of DM-related research articles in all published academic papers has increased
Supporting Solar Physics Research via Data Mining

Science.gov (United States)

Angryk, Rafal; Banda, J.; Schuh, M.; Ganesan Pillai, K.; Tosun, H.; Martens, P.

2012-05-01

In this talk we will briefly introduce three pillars of data mining (i.e. frequent patterns discovery, classification, and clustering), and discuss some possible applications of known data mining techniques which can directly benefit solar physics research. In particular, we plan to demonstrate applicability of frequent patterns discovery methods for the verification of hypotheses about co-occurrence (in space and time) of filaments and sigmoids. We will also show how classification/machine learning algorithms can be utilized to verify human-created software modules to discover individual types of solar phenomena. Finally, we will discuss applicability of clustering techniques to image data processing.
Open data mining for Taiwan's dengue epidemic.

Science.gov (United States)

Wu, ChienHsing; Kao, Shu-Chen; Shih, Chia-Hung; Kan, Meng-Hsuan

2018-07-01

By using a quantitative approach, this study examines the applicability of data mining technique to discover knowledge from open data related to Taiwan's dengue epidemic. We compare results when Google trend data are included or excluded. Data sources are government open data, climate data, and Google trend data. Research findings from analysis of 70,914 cases are obtained. Location and time (month) in open data show the highest classification power followed by climate variables (temperature and humidity), whereas gender and age show the lowest values. Both prediction accuracy and simplicity decrease when Google trends are considered (respectively 0.94 and 0.37, compared to 0.96 and 0.46). The article demonstrates the value of open data mining in the context of public health care. Copyright © 2018 Elsevier B.V. All rights reserved.
Data Mining Learning Models and Algorithms on a Scada System Data Repository

Directory of Open Access Journals (Sweden)

Mircea Rîşteiu

2010-06-01

Full Text Available This paper presents three data mining techniques applied
on a SCADA system data repository: NaÄ³ve Bayes, k-Nearest Neighbor and Decision Trees. A conclusion that k-Nearest Neighbor is a suitable method to classify the large amount of data considered is made finally according to the mining result and its reasonable explanation. The experiments are built on the training data set and evaluated using the new test set with machine learning tool WEKA.
An original approach to data acquisition: CHADAC

International Nuclear Information System (INIS)

Huppert, M.; Nayman, P.; Rivoal, M.

1981-01-01

Many labs try to boost existing data acquisition systems by inserting high performance intelligent devices in the important nodes of the system's structure. This strategy finds its limits in the system's architecture. The CHADAC project proposes a simple and efficient solution to this problem, using a multiprocessor modular architecture. CHADAC main features are: a) Parallel acquisition of data: CHADAC is fast; it dedicates one processor per branch; each processor can read and store one 16 bit word in 800 ns. b) Original structure: each processor can work in its own private memory, in its own shared memory (double access) and in the shared memory of any other processor (this feature being particulary useful to avoid wasteful data transfers). Simple and fast communications between processors are also provided by local DMA'S. c) Flexibility: each processor is autonomous and may be used as an independent acquisition system for a branch, by connecting local peripherals to it. Adjunction of fast trigger logic is possible. By its architecture and performances, CHADAC is designed to provide a good support for local intelligent devices and transfer operators developped elsewhere, providing a way to implement systems well fitted to various types of data acquisition. (orig.)
DATA MINING. CONCEPTS AND APPLICATIONS IN BANKING SECTOR

Directory of Open Access Journals (Sweden)

ADRIAN IONUT PASCU

2018-02-01

Full Text Available The concept of banking refers to the multitude of services and products that commercial banks offer to clients and include besides transactional accounts both passive and active products. Due to the increased competitiveness in banking, the relationship between the bank and the client has become an essential factor for the strategy in order to increase customer satisfaction. Currently the banking system is able to store impressive amounts of data that they collect daily, from customer data and transaction details to data on their transactional or risk profile. The process through which large amounts of data are analyzed, extracted, identified and the information obtained using mathematical and statistical models are interpreted is known as data mining. The discovery of knowledge from data involves identifying some models, some patterns with which certain events or possible risks are anticipated. This process helps banks to develop strategies in areas such as customer retention and loyalty, customer satisfaction, fraud detection and prevention, risk management, money laundering prevention. The aim of this paper is to present the concept of data mining and the concept of data discovery (KDD, but also the impact and important use of data mining techniques in the banking sector. This paper explores and reviews various data mining techniques that are applied in the banking sector but also provides insight into how these techniques are used in different areas to make decision-making easier and more efficient.
2nd International Conference on Computational Intelligence in Data Mining

CERN Document Server

Mohapatra, Durga

2016-01-01

The book is a collection of high-quality peer-reviewed research papers presented in the Second International Conference on Computational Intelligence in Data Mining (ICCIDM 2015) held at Bhubaneswar, Odisha, India during 5 – 6 December 2015. The two-volume Proceedings address the difficulties and challenges for the seamless integration of two core disciplines of computer science, i.e., computational intelligence and data mining. The book addresses different methods and techniques of integration for enhancing the overall goal of data mining. The book helps to disseminate the knowledge about some innovative, active research directions in the field of data mining, machine and computational intelligence, along with some current issues and applications of related topics.
LAMPF nuclear chemistry data acquisition system

International Nuclear Information System (INIS)

Giesler, G.C.

1983-01-01

The LAMPF Nuclear Chemistry Data Acquisition System (DAS) is designed to provide both real-time control of data acquisition and facilities for data processing for a large variety of users. It consists of a PDP-11/44 connected to a parallel CAMAC branch highway as well as to a large number of peripherals. The various types of radiation counters and spectrometers and their connections to the system will be described. Also discussed will be the various methods of connection considered and their advantages and disadvantages. The operation of the system from the standpoint of both hardware and software will be described as well as plans for the future
The Bochum on-line data acquisition system

International Nuclear Information System (INIS)

Paul, H.J.; Freiesleben, H.

1986-01-01

We describe an on-line data acquisition system based on a PDP 11 computer with CAMAC hardware. The software fully exploits the real-time features of the RSX-11M operating system. The basic characteristics of the program package, mainly written in FORTRAN 77, are: multitasking, shared common blocks, dynamical access to CAMAC hardware and data, and command orientated user interface. The system is particularly tailored for data acquisition in list mode of up to 64 parameters. (orig.)
Proceedings of the XIIIth IAGA Workshop on Geomagnetic Observatory Instruments, Data Acquisition, and Processing

Science.gov (United States)

Love, Jeffrey J.

2009-01-01

The thirteenth biennial International Association of Geomagnetism and Aeronomy (IAGA) Workshop on Geomagnetic Observatory Instruments, Data Acquisition and Processing was held in the United States for the first time on June 9-18, 2008. Hosted by the U.S. Geological Survey's (USGS) Geomagnetism Program, the workshop's measurement session was held at the Boulder Observatory and the scientific session was held on the campus of the Colorado School of Mines in Golden, Colorado. More than 100 participants came from 36 countries and 6 continents. Preparation for the workshop began when the USGS Geomagnetism Program agreed, at the close of the twelfth workshop in Belsk Poland in 2006, to host the next workshop. Working under the leadership of Alan Berarducci, who served as the chairman of the local organizing committee, and Tim White, who served as co-chairman, preparations began in 2007. The Boulder Observatory was extensively renovated and additional observation piers were installed. Meeting space on the Colorado School of Mines campus was arranged, and considerable planning was devoted to managing the many large and small issues that accompany an international meeting. Without the devoted efforts of both Alan and Tim, other Geomagnetism Program staff, and our partners at the Colorado School of Mines, the workshop simply would not have occurred. We express our thanks to Jill McCarthy, the USGS Central Region Geologic Hazards Team Chief Scientist; Carol A. Finn, the Group Leader of the USGS Geomagnetism Program; the USGS International Office; and Melody Francisco of the Office of Special Programs and Continuing Education of the Colorado School of Mines. We also thank the student employees that the Geomagnetism Program has had over the years and leading up to the time of the workshop. For preparation of the proceedings, thanks go to Eddie and Tim. And, finally, we thank our sponsors, the USGS, IAGA, and the Colorado School of Mines.
COLDEX New Data Acquisition Framework

CERN Document Server

Grech, Christian

2015-01-01

COLDEX (COLD bore EXperiment) is an experiment of the TE-VSC group installed in the Super Proton Synchrotron (SPS) which mimics a LHC type cryogenic vacuum system. In the framework of the High Luminosity upgrade of the LHC (HL-LHC project), COLDEX has been recommissioned in 2014 in order to validate carbon coatings performances at cryogenic temperature with LHC type beams. To achieve this mission, a data acquisition system is needed to retrieve and store information from the different experiment’s systems (vacuum, cryogenics, controls, safety) and perform specific calculations. This work aimed to completely redesign, implement, test and operate a brand new data acquisition framework based on communication with the experiment’s PLCs for the devices potentially available over network. The communication protocol to the PLCs is based on data retrieval both from CERN middleware infrastructures (CMW, JAPC) and on a novel open source Simatic S7 data exchange package over TCP/IP (libnodave).
Optimal sampling strategy for data mining

International Nuclear Information System (INIS)

Ghaffar, A.; Shahbaz, M.; Mahmood, W.

2013-01-01

Latest technology like Internet, corporate intranets, data warehouses, ERP's, satellites, digital sensors, embedded systems, mobiles networks all are generating such a massive amount of data that it is getting very difficult to analyze and understand all these data, even using data mining tools. Huge datasets are becoming a difficult challenge for classification algorithms. With increasing amounts of data, data mining algorithms are getting slower and analysis is getting less interactive. Sampling can be a solution. Using a fraction of computing resources, Sampling can often provide same level of accuracy. The process of sampling requires much care because there are many factors involved in the determination of correct sample size. The approach proposed in this paper tries to find a solution to this problem. Based on a statistical formula, after setting some parameters, it returns a sample size called s ufficient sample size , which is then selected through probability sampling. Results indicate the usefulness of this technique in coping with the problem of huge datasets. (author)
DABASCO Experiment Data Acquisition and Control System

International Nuclear Information System (INIS)

Alberdi Primicia, J.; Artigao Arteaga, A.; Barcala Rieveira, J. M.; Oller Gonzalez, J. C.

2000-01-01

DABASCO experiment wants to study the thermohydraulic phenomena produced into the containment area for a severe accident in a nuclear power facility. This document describes the characteristics of the data acquisition and control system used in the experiment. The main elements of the system were a data acquisition board, PCI-MIO-16E-4, and an application written with LaB View. (Author) 5 refs
Using data mining to segment healthcare markets from patients' preference perspectives.

Science.gov (United States)

Liu, Sandra S; Chen, Jie

2009-01-01

This paper aims to provide an example of how to use data mining techniques to identify patient segments regarding preferences for healthcare attributes and their demographic characteristics. Data were derived from a number of individuals who received in-patient care at a health network in 2006. Data mining and conventional hierarchical clustering with average linkage and Pearson correlation procedures are employed and compared to show how each procedure best determines segmentation variables. Data mining tools identified three differentiable segments by means of cluster analysis. These three clusters have significantly different demographic profiles. The study reveals, when compared with traditional statistical methods, that data mining provides an efficient and effective tool for market segmentation. When there are numerous cluster variables involved, researchers and practitioners need to incorporate factor analysis for reducing variables to clearly and meaningfully understand clusters. Interests and applications in data mining are increasing in many businesses. However, this technology is seldom applied to healthcare customer experience management. The paper shows that efficient and effective application of data mining methods can aid the understanding of patient healthcare preferences.
Financial considerations regarding environmental liabilities: Acquisitions and divestitures

International Nuclear Information System (INIS)

Hanson, B.R.

1990-01-01

What position should your company assume in the mining industry? Should your company take a larger or smaller market position? Does it make sense to be acquiring or divesting properties? Should your company be pursuing acquisitions and divestitures simultaneously? The answers to these questions determine in large part what perspective your company will take in assessing and confronting risk of environmental liabilities. From the environmental perspective, serious problems confront the mining industry. Uninformed or misinformed regulators doing a poor job of open-quotes protectingclose quotes the environment continue to plague mining companies at the federal, state and local level. The press and public are routinely ill-informed and often hostile toward mining generally and toward specific projects. The open-quotes not-in-my-backyardclose quotes syndrome continues to haunt projects because every ore-rich plot of land lies in someone's backyard or playground. More profoundly, the industry confronts serious structural impediments caused by media-specific statutes and regulations written with an overriding preference for waste elimination or reduction. Mining and mineral production impact all media and result in an highly integrated project. Despite media-specific statutes and regulations that prefer wastes go anywhere but in their particular media, mine wastes must go somewhere. To its credit, the mining industry has persevered in the face of complex and often conflicting environmental statutes and regulations. The industry has continued to expand or contract in response to market conditions. Companies must continue to pursue acquisitions and divestitures based on their position and goals. This paper addresses financial risks in acquisitions and divestitures and, more importantly, suggests strategies to limit environmental liabilities during acquisitions and divestitures

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