WorldWideScience

Sample records for big volume modelirovanie

  1. HARNESSING BIG DATA VOLUMES

    Directory of Open Access Journals (Sweden)

    Bogdan DINU

    2014-04-01

    Full Text Available Big Data can revolutionize humanity. Hidden within the huge amounts and variety of the data we are creating we may find information, facts, social insights and benchmarks that were once virtually impossible to find or were simply inexistent. Large volumes of data allow organizations to tap in real time the full potential of all the internal or external information they possess. Big data calls for quick decisions and innovative ways to assist customers and the society as a whole. Big data platforms and product portfolio will help customers harness to the full the value of big data volumes. This paper deals with technical and technological issues related to handling big data volumes in the Big Data environment.

  2. Volume and Value of Big Healthcare Data.

    Science.gov (United States)

    Dinov, Ivo D

    Modern scientific inquiries require significant data-driven evidence and trans-disciplinary expertise to extract valuable information and gain actionable knowledge about natural processes. Effective evidence-based decisions require collection, processing and interpretation of vast amounts of complex data. The Moore's and Kryder's laws of exponential increase of computational power and information storage, respectively, dictate the need rapid trans-disciplinary advances, technological innovation and effective mechanisms for managing and interrogating Big Healthcare Data. In this article, we review important aspects of Big Data analytics and discuss important questions like: What are the challenges and opportunities associated with this biomedical, social, and healthcare data avalanche? Are there innovative statistical computing strategies to represent, model, analyze and interpret Big heterogeneous data? We present the foundation of a new compressive big data analytics (CBDA) framework for representation, modeling and inference of large, complex and heterogeneous datasets. Finally, we consider specific directions likely to impact the process of extracting information from Big healthcare data, translating that information to knowledge, and deriving appropriate actions.

  3. On 'light' fermions and proton stability in 'big divisor' D3/D7 large volume compactifications

    International Nuclear Information System (INIS)

    Misra, Aalok; Shukla, Pramod

    2011-01-01

    Building on our earlier work (Misra and Shukla, Nucl. Phys. B 827:112, 2010; Phys. Lett. B 685:347-352, 2010), we show the possibility of generating ''light'' fermion mass scales of MeV-GeV range (possibly related to the first two generations of quarks/leptons) as well as eV (possibly related to first two generations of neutrinos) in type IIB string theory compactified on Swiss-Cheese orientifolds in the presence of a mobile space-time filling D3-brane restricted to (in principle) stacks of fluxed D7-branes wrapping the ''big'' divisor Σ B . This part of the paper is an expanded version of the latter half of Sect. 3 of a published short invited review (Misra, Mod. Phys. Lett. A 26:1, 2011) written by one of the authors [AM ]. Further, we also show that there are no SUSY GUT-type dimension-five operators corresponding to proton decay, and we estimate the proton lifetime from a SUSY GUT-type four-fermion dimension-six operator to be 10 61 years. Based on GLSM calculations in (Misra and Shukla, Nucl. Phys. B 827:112, 2010) for obtaining the geometric Kaehler potential for the ''big divisor,'' using further the Donaldson's algorithm, we also briefly discuss in the first of the two appendices the metric for the Swiss-Cheese Calabi-Yau used, which we obtain and which becomes Ricci flat in the large-volume limit. (orig.)

  4. On `light' fermions and proton stability in `big divisor' D3/ D7 large volume compactifications

    Science.gov (United States)

    Misra, Aalok; Shukla, Pramod

    2011-06-01

    Building on our earlier work (Misra and Shukla, Nucl. Phys. B 827:112, 2010; Phys. Lett. B 685:347-352, 2010), we show the possibility of generating "light" fermion mass scales of MeV-GeV range (possibly related to the first two generations of quarks/leptons) as well as eV (possibly related to first two generations of neutrinos) in type IIB string theory compactified on Swiss-Cheese orientifolds in the presence of a mobile space-time filling D3-brane restricted to (in principle) stacks of fluxed D7-branes wrapping the "big" divisor Σ B . This part of the paper is an expanded version of the latter half of Sect. 3 of a published short invited review (Misra, Mod. Phys. Lett. A 26:1, 2011) written by one of the authors [AM]. Further, we also show that there are no SUSY GUT-type dimension-five operators corresponding to proton decay, and we estimate the proton lifetime from a SUSY GUT-type four-fermion dimension-six operator to be 1061 years. Based on GLSM calculations in (Misra and Shukla, Nucl. Phys. B 827:112, 2010) for obtaining the geometric Kähler potential for the "big divisor," using further the Donaldson's algorithm, we also briefly discuss in the first of the two appendices the metric for the Swiss-Cheese Calabi-Yau used, which we obtain and which becomes Ricci flat in the large-volume limit.

  5. Frameworks for management, storage and preparation of large data volumes Big Data

    Directory of Open Access Journals (Sweden)

    Marco Antonio Almeida Pamiño

    2017-05-01

    Full Text Available Weather systems like the World Meteorological Organization ́s Global Information System need to store different kinds of images, data and files. Big Data and its 3V paradigm can provide a suitable solution to solve this problem. This tutorial presents some concepts around the Hadoop framework, de facto standard implementation of Big Data, and how to store semi-estructured data generated by automatic weather stations using this framework. Finally, a formal method to generate weather reports using Hadoop ́s ecosystem frameworks is presented.

  6. The Big Mac and Teaching about Japan. Footnotes. Volume 8, Number 5

    Science.gov (United States)

    Ellington, Lucien

    2003-01-01

    The Big Mac can be effective tool in helping students achieve a better understanding of Japan. It can defeat Orientalist stereotypes about the Japanese--and also challenge young people who might have oversimplified notions of what exactly occurs when U.S. fast food chains take root in another culture. Many deride McDonald's as a villain…

  7. Do container volume, site preparation, and field fertilization affect restoration potential of Wyoming big sagebrush?

    Science.gov (United States)

    Kayla R. Herriman; Anthony S. Davis; Kent G. Apostol; Olga. A. Kildisheva; Amy L. Ross-Davis; Kas Dumroese

    2016-01-01

    Land management practices, invasive species expansion, and changes in the fire regime greatly impact the distribution of native plants in natural areas. Wyoming big sagebrush (Artemisia tridentata ssp. wyomingensis), a keystone species in the Great Basin, has seen a 50% reduction in its distribution. For many dryland species, reestablishment efforts have...

  8. Sonar atlas of caverns comprising the U.S. Strategic Petroleum Reserve. Volume 2, Big Hill Site, Texas.

    Energy Technology Data Exchange (ETDEWEB)

    Rautman, Christopher Arthur; Lord, Anna Snider

    2007-08-01

    Downhole sonar surveys from the four active U.S. Strategic Petroleum Reserve sites have been modeled and used to generate a four-volume sonar atlas, showing the three-dimensional geometry of each cavern. This volume 2 focuses on the Big Hill SPR site, located in southeastern Texas. Volumes 1, 3, and 4, respectively, present images for the Bayou Choctaw SPR site, Louisiana, the Bryan Mound SPR site, Texas, and the West Hackberry SPR site, Louisiana. The atlas uses a consistent presentation format throughout. The basic geometric measurements provided by the down-cavern surveys have also been used to generate a number of geometric attributes, the values of which have been mapped onto the geometric form of each cavern using a color-shading scheme. The intent of the various geometrical attributes is to highlight deviations of the cavern shape from the idealized cylindrical form of a carefully leached underground storage cavern in salt. The atlas format does not allow interpretation of such geometric deviations and anomalies. However, significant geometric anomalies, not directly related to the leaching history of the cavern, may provide insight into the internal structure of the relevant salt dome.

  9. Characterizing Big Data Management

    OpenAIRE

    Rogério Rossi; Kechi Hirama

    2015-01-01

    Big data management is a reality for an increasing number of organizations in many areas and represents a set of challenges involving big data modeling, storage and retrieval, analysis and visualization. However, technological resources, people and processes are crucial to facilitate the management of big data in any kind of organization, allowing information and knowledge from a large volume of data to support decision-making. Big data management can be supported by these three dimensions: t...

  10. Big Cat Coalitions: A Comparative Analysis of Regional Brain Volumes in Felidae.

    Science.gov (United States)

    Sakai, Sharleen T; Arsznov, Bradley M; Hristova, Ani E; Yoon, Elise J; Lundrigan, Barbara L

    2016-01-01

    Broad-based species comparisons across mammalian orders suggest a number of factors that might influence the evolution of large brains. However, the relationship between these factors and total and regional brain size remains unclear. This study investigated the relationship between relative brain size and regional brain volumes and sociality in 13 felid species in hopes of revealing relationships that are not detected in more inclusive comparative studies. In addition, a more detailed analysis was conducted of four focal species: lions ( Panthera leo ), leopards ( Panthera pardus ), cougars ( Puma concolor ), and cheetahs ( Acinonyx jubatus ). These species differ markedly in sociality and behavioral flexibility, factors hypothesized to contribute to increased relative brain size and/or frontal cortex size. Lions are the only truly social species, living in prides. Although cheetahs are largely solitary, males often form small groups. Both leopards and cougars are solitary. Of the four species, leopards exhibit the most behavioral flexibility, readily adapting to changing circumstances. Regional brain volumes were analyzed using computed tomography. Skulls ( n = 75) were scanned to create three-dimensional virtual endocasts, and regional brain volumes were measured using either sulcal or bony landmarks obtained from the endocasts or skulls. Phylogenetic least squares regression analyses found that sociality does not correspond with larger relative brain size in these species. However, the sociality/solitary variable significantly predicted anterior cerebrum (AC) volume, a region that includes frontal cortex. This latter finding is despite the fact that the two social species in our sample, lions and cheetahs, possess the largest and smallest relative AC volumes, respectively. Additionally, an ANOVA comparing regional brain volumes in four focal species revealed that lions and leopards, while not significantly different from one another, have relatively larger AC

  11. Big Cat Coalitions: A comparative analysis of regional brain volumes in Felidae

    Directory of Open Access Journals (Sweden)

    Sharleen T Sakai

    2016-10-01

    Full Text Available Broad-based species comparisons across mammalian orders suggest a number of factors that might influence the evolution of large brains. However, the relationship between these factors and total and regional brain size remains unclear. This study investigated the relationship between relative brain size and regional brain volumes and sociality in 13 felid species in hopes of revealing relationships that are not detected in more inclusive comparative studies. In addition, a more detailed analysis was conducted of 4 focal species: lions (Panthera leo, leopards (Panthera pardus, cougars (Puma concolor, and cheetahs (Acinonyx jubatus. These species differ markedly in sociality and behavioral flexibility, factors hypothesized to contribute to increased relative brain size and/or frontal cortex size. Lions are the only truly social species, living in prides. Although cheetahs are largely solitary, males often form small groups. Both leopards and cougars are solitary. Of the four species, leopards exhibit the most behavioral flexibility, readily adapting to changing circumstances. Regional brain volumes were analyzed using computed tomography (CT. Skulls (n=75 were scanned to create three-dimensional virtual endocasts, and regional brain volumes were measured using either sulcal or bony landmarks obtained from the endocasts or skulls. Phylogenetic least squares (PGLS regression analyses found that sociality does not correspond with larger relative brain size in these species. However, the sociality/solitary variable significantly predicted anterior cerebrum (AC volume, a region that includes frontal cortex. This latter finding is despite the fact that the two social species in our sample, lions and cheetahs, possess the largest and smallest relative AC volumes, respectively. Additionally, an ANOVA comparing regional brain volumes in 4 focal species revealed that lions and leopards, while not significantly different from one another, have relatively

  12. An Industrial Perspective of CAM/ROB Fuzzy Integrated Postprocessing Implementation for Redundant Robotic Workcells Applicability for Big Volume Prototyping

    Science.gov (United States)

    Andrés, J.; Gracia, L.; Tornero, J.; García, J. A.; González, F.

    2009-11-01

    The implementation of a postprocessor for the NX™ platform (Siemens Corp.) is described in this paper. It is focused on a milling redundant robotic milling workcell consisting of one KUKA KR 15/2 manipulator (6 rotary joints, KRC2 controller) mounted on a linear axis and synchronized with a rotary table (i.e., two additional joints). For carrying out a milling task, a choice among a set of possible configurations is required, taking into account the ability to avoid singular configurations by using both additional joints. Usually, experience and knowledge of the workman allow an efficient control in these cases, but being it a tedious job. Similarly to this expert knowledge, a stand-alone fuzzy controller has been programmed with Matlab's Fuzzy Logic Toolbox (The MathWorks, Inc.). Two C++ programs complement the translation of the toolpath tracking (expressed in the Cartesian space) from the NX™-CAM module into KRL (KUKA Robot Language). In order to avoid singularities or joint limits, the location of the robot and the workpiece during the execution of the task is fit after an inverse kinematics position analysis and a fuzzy inference (i.e., fuzzy criterion in the Joint Space). Additionally, the applicability of robot arms for the manufacture of big volume prototypes with this technique is proven by means of one case studied. It consists of a big orographic model to simulate floodways, return flows and retention storage of a reservoir in the Mijares river (Puebla de Arenoso, Spain). This article deals with the problem for a constant tool orientation milling process and sets the technological basis for future research at five axis milling operations.

  13. Observer variation in target volume delineation of lung cancer related to radiation oncologist-computer interaction: A 'Big Brother' evaluation

    International Nuclear Information System (INIS)

    Steenbakkers, Roel J.H.M.; Duppen, Joop C.; Fitton, Isabelle; Deurloo, Kirsten E.I.; Zijp, Lambert; Uitterhoeve, Apollonia L.J.; Rodrigus, Patrick T.R.; Kramer, Gijsbert W.P.; Bussink, Johan; Jaeger, Katrien De; Belderbos, Jose S.A.; Hart, Augustinus A.M.; Nowak, Peter J.C.M.; Herk, Marcel van; Rasch, Coen R.N.

    2005-01-01

    Background and purpose: To evaluate the process of target volume delineation in lung cancer for optimization of imaging, delineation protocol and delineation software. Patients and methods: Eleven radiation oncologists (observers) from five different institutions delineated the Gross Tumor Volume (GTV) including positive lymph nodes of 22 lung cancer patients (stages I-IIIB) on CT only. All radiation oncologist-computer interactions were recorded with a tool called 'Big Brother'. For each radiation oncologist and patient the following issues were analyzed: delineation time, number of delineated points and corrections, zoom levels, level and window (L/W) settings, CT slice changes, use of side windows (coronal and sagittal) and software button use. Results: The mean delineation time per GTV was 16 min (SD 10 min). The mean delineation time for lymph node positive patients was on average 3 min larger (P=0.02) than for lymph node negative patients. Many corrections (55%) were due to L/W change (e.g. delineating in mediastinum L/W and then correcting in lung L/W). For the lymph node region, a relatively large number of corrections was found (3.7 corr/cm 2 ), indicating that it was difficult to delineate lymph nodes. For the tumor-atelectasis region, a relative small number of corrections was found (1.0 corr/cm 2 ), indicating that including or excluding atelectasis into the GTV was a clinical decision. Inappropriate use of L/W settings was frequently found (e.g. 46% of all delineated points in the tumor-lung region were delineated in mediastinum L/W settings). Despite a large observer variation in cranial and caudal direction of 0.72 cm (1 SD), the coronal and sagittal side windows were not used in 45 and 60% of the cases, respectively. For the more difficult cases, observer variation was smaller when the coronal and sagittal side windows were used. Conclusions: With the 'Big Brother' tool a method was developed to trace the delineation process. The differences between

  14. Characterizing Big Data Management

    Directory of Open Access Journals (Sweden)

    Rogério Rossi

    2015-06-01

    Full Text Available Big data management is a reality for an increasing number of organizations in many areas and represents a set of challenges involving big data modeling, storage and retrieval, analysis and visualization. However, technological resources, people and processes are crucial to facilitate the management of big data in any kind of organization, allowing information and knowledge from a large volume of data to support decision-making. Big data management can be supported by these three dimensions: technology, people and processes. Hence, this article discusses these dimensions: the technological dimension that is related to storage, analytics and visualization of big data; the human aspects of big data; and, in addition, the process management dimension that involves in a technological and business approach the aspects of big data management.

  15. Big Data and Chemical Education

    Science.gov (United States)

    Pence, Harry E.; Williams, Antony J.

    2016-01-01

    The amount of computerized information that organizations collect and process is growing so large that the term Big Data is commonly being used to describe the situation. Accordingly, Big Data is defined by a combination of the Volume, Variety, Velocity, and Veracity of the data being processed. Big Data tools are already having an impact in…

  16. Big universe, big data

    DEFF Research Database (Denmark)

    Kremer, Jan; Stensbo-Smidt, Kristoffer; Gieseke, Fabian Cristian

    2017-01-01

    , modern astronomy requires big data know-how, in particular it demands highly efficient machine learning and image analysis algorithms. But scalability is not the only challenge: Astronomy applications touch several current machine learning research questions, such as learning from biased data and dealing......, and highlight some recent methodological advancements in machine learning and image analysis triggered by astronomical applications....

  17. Big data, big responsibilities

    Directory of Open Access Journals (Sweden)

    Primavera De Filippi

    2014-01-01

    Full Text Available Big data refers to the collection and aggregation of large quantities of data produced by and about people, things or the interactions between them. With the advent of cloud computing, specialised data centres with powerful computational hardware and software resources can be used for processing and analysing a humongous amount of aggregated data coming from a variety of different sources. The analysis of such data is all the more valuable to the extent that it allows for specific patterns to be found and new correlations to be made between different datasets, so as to eventually deduce or infer new information, as well as to potentially predict behaviours or assess the likelihood for a certain event to occur. This article will focus specifically on the legal and moral obligations of online operators collecting and processing large amounts of data, to investigate the potential implications of big data analysis on the privacy of individual users and on society as a whole.

  18. Herpetofaunal Inventories of the National Parks of South Florida and the Caribbean: Volume III. Big Cypress National Preserve

    Science.gov (United States)

    Rice, Kenneth G.; Waddle, J. Hardin; Crockett, Marquette E.; Jeffrey, Brian M.; Rice, Amanda N.; Percival, H. Franklin

    2005-01-01

    Amphibian declines and extinctions have been documented around the world, often in protected natural areas. Concern for this trend has prompted the U.S. Geological Survey and the National Park Service to document all species of amphibians that occur within U.S. National Parks and to search for any signs that amphibians may be declining. This study, an inventory of amphibian species in Big Cypress National Preserve, was conducted from 2002 to 2003. The goals of the project were to create a georeferenced inventory of amphibian species, use new analytical techniques to estimate proportion of sites occupied by each species, look for any signs of amphibian decline (missing species, disease, die-offs, and so forth.), and to establish a protocol that could be used for future monitoring efforts. Several sampling methods were used to accomplish these goals. Visual encounter surveys and anuran vocalization surveys were conducted in all habitats throughout the park to estimate the proportion of sites or proportion of area occupied (PAO) by each amphibian species in each habitat. Opportunistic collections, as well as limited drift fence data, were used to augment the visual encounter methods for highly aquatic or cryptic species. A total of 545 visits to 104 sites were conducted for standard sampling alone, and 2,358 individual amphibians and 374 reptiles were encountered. Data analysis was conducted in program PRESENCE to provide PAO estimates for each of the anuran species. All of the amphibian species historically found in Big Cypress National Preserve were detected during this project. At least one individual of each of the four salamander species was captured during sampling. Each of the anuran species in the preserve was adequately sampled using standard herpetological sampling methods, and PAO estimates were produced for each species of anuran by habitat. This information serves as an indicator of habitat associations of the species and relative abundance of sites

  19. Big science

    CERN Multimedia

    Nadis, S

    2003-01-01

    " "Big science" is moving into astronomy, bringing large experimental teams, multi-year research projects, and big budgets. If this is the wave of the future, why are some astronomers bucking the trend?" (2 pages).

  20. Big Data Analytics

    Indian Academy of Sciences (India)

    The volume and variety of data being generated using computersis doubling every two years. It is estimated that in 2015,8 Zettabytes (Zetta=1021) were generated which consistedmostly of unstructured data such as emails, blogs, Twitter,Facebook posts, images, and videos. This is called big data. Itis possible to analyse ...

  1. Towards large volume big divisor D3/D7 " μ-split supersymmetry" and Ricci-flat Swiss-cheese metrics, and dimension-six neutrino mass operators

    Science.gov (United States)

    Dhuria, Mansi; Misra, Aalok

    2012-02-01

    We show that it is possible to realize a " μ-split SUSY" scenario (Cheng and Cheng, 2005) [1] in the context of large volume limit of type IIB compactifications on Swiss-cheese Calabi-Yau orientifolds in the presence of a mobile space-time filling D3-brane and a (stack of) D7-brane(s) wrapping the "big" divisor. For this, we investigate the possibility of getting one Higgs to be light while other to be heavy in addition to a heavy higgsino mass parameter. Further, we examine the existence of long lived gluino that manifests one of the major consequences of μ-split SUSY scenario, by computing its decay width as well as lifetime corresponding to the three-body decays of the gluino into either a quark, a squark and a neutralino or a quark, squark and goldstino, as well as two-body decays of the gluino into either a neutralino and a gluon or a goldstino and a gluon. Guided by the geometric Kähler potential for Σ obtained in Misra and Shukla (2010) [2] based on GLSM techniques, and the Donaldson's algorithm (Barun et al., 2008) [3] for obtaining numerically a Ricci-flat metric, we give details of our calculation in Misra and Shukla (2011) [4] pertaining to our proposed metric for the full Swiss-cheese Calabi-Yau (the geometric Kähler potential being needed to be included in the full moduli space Kähler potential in the presence of the mobile space-time filling D3-brane), but for simplicity of calculation, close to the big divisor, which is Ricci-flat in the large volume limit. Also, as an application of the one-loop RG flow solution for the higgsino mass parameter, we show that the contribution to the neutrino masses at the EW scale from dimension-six operators arising from the Kähler potential, is suppressed relative to the Weinberg-type dimension-five operators.

  2. Big Data Analytics in Medicine and Healthcare.

    Science.gov (United States)

    Ristevski, Blagoj; Chen, Ming

    2018-05-10

    This paper surveys big data with highlighting the big data analytics in medicine and healthcare. Big data characteristics: value, volume, velocity, variety, veracity and variability are described. Big data analytics in medicine and healthcare covers integration and analysis of large amount of complex heterogeneous data such as various - omics data (genomics, epigenomics, transcriptomics, proteomics, metabolomics, interactomics, pharmacogenomics, diseasomics), biomedical data and electronic health records data. We underline the challenging issues about big data privacy and security. Regarding big data characteristics, some directions of using suitable and promising open-source distributed data processing software platform are given.

  3. Big data uncertainties.

    Science.gov (United States)

    Maugis, Pierre-André G

    2018-07-01

    Big data-the idea that an always-larger volume of information is being constantly recorded-suggests that new problems can now be subjected to scientific scrutiny. However, can classical statistical methods be used directly on big data? We analyze the problem by looking at two known pitfalls of big datasets. First, that they are biased, in the sense that they do not offer a complete view of the populations under consideration. Second, that they present a weak but pervasive level of dependence between all their components. In both cases we observe that the uncertainty of the conclusion obtained by statistical methods is increased when used on big data, either because of a systematic error (bias), or because of a larger degree of randomness (increased variance). We argue that the key challenge raised by big data is not only how to use big data to tackle new problems, but to develop tools and methods able to rigorously articulate the new risks therein. Copyright © 2016. Published by Elsevier Ltd.

  4. Towards Large Volume Big Divisor D3-D7 "mu-Split Supersymmetry" and Ricci-Flat Swiss-Cheese Metrics, and Dimension-Six Neutrino Mass Operators

    CERN Document Server

    Dhuria, Mansi

    2012-01-01

    We show that it is possible to realize a "mu-split SUSY" scenario [1] in the context of large volume limit of type IIB compactifications on Swiss-Cheese Calabi-Yau's in the presence of a mobile space-time filling D3-brane and a (stack of) D7-brane(s) wrapping the "big" divisor Sigma_B. For this, we investigate the possibility of getting one Higgs to be light while other to be heavy in addition to a heavy Higgsino mass parameter. Further, we examine the existence of long lived gluino that manifests one of the major consequences of mu-split SUSY scenario, by computing its decay width as well as lifetime corresponding to the 3-body decays of the gluino into a quark, a squark and a neutralino or Goldstino, as well as 2-body decays of the gluino into either a neutralino or a Goldstino and a gluon. Guided by the geometric Kaehler potential for Sigma_B obtained in [2] based on GLSM techniques, and the Donaldson's algorithm [3] for obtaining numerically a Ricci-flat metric, we give details of our calculation in [4] p...

  5. Urbanising Big

    DEFF Research Database (Denmark)

    Ljungwall, Christer

    2013-01-01

    Development in China raises the question of how big a city can become, and at the same time be sustainable, writes Christer Ljungwall of the Swedish Agency for Growth Policy Analysis.......Development in China raises the question of how big a city can become, and at the same time be sustainable, writes Christer Ljungwall of the Swedish Agency for Growth Policy Analysis....

  6. Big Argumentation?

    Directory of Open Access Journals (Sweden)

    Daniel Faltesek

    2013-08-01

    Full Text Available Big Data is nothing new. Public concern regarding the mass diffusion of data has appeared repeatedly with computing innovations, in the formation before Big Data it was most recently referred to as the information explosion. In this essay, I argue that the appeal of Big Data is not a function of computational power, but of a synergistic relationship between aesthetic order and a politics evacuated of a meaningful public deliberation. Understanding, and challenging, Big Data requires an attention to the aesthetics of data visualization and the ways in which those aesthetics would seem to depoliticize information. The conclusion proposes an alternative argumentative aesthetic as the appropriate response to the depoliticization posed by the popular imaginary of Big Data.

  7. Big data

    DEFF Research Database (Denmark)

    Madsen, Anders Koed; Flyverbom, Mikkel; Hilbert, Martin

    2016-01-01

    is to outline a research agenda that can be used to raise a broader set of sociological and practice-oriented questions about the increasing datafication of international relations and politics. First, it proposes a way of conceptualizing big data that is broad enough to open fruitful investigations......The claim that big data can revolutionize strategy and governance in the context of international relations is increasingly hard to ignore. Scholars of international political sociology have mainly discussed this development through the themes of security and surveillance. The aim of this paper...... into the emerging use of big data in these contexts. This conceptualization includes the identification of three moments contained in any big data practice. Second, it suggests a research agenda built around a set of subthemes that each deserve dedicated scrutiny when studying the interplay between big data...

  8. How Big Is Too Big?

    Science.gov (United States)

    Cibes, Margaret; Greenwood, James

    2016-01-01

    Media Clips appears in every issue of Mathematics Teacher, offering readers contemporary, authentic applications of quantitative reasoning based on print or electronic media. This issue features "How Big is Too Big?" (Margaret Cibes and James Greenwood) in which students are asked to analyze the data and tables provided and answer a…

  9. Big data challenges

    DEFF Research Database (Denmark)

    Bachlechner, Daniel; Leimbach, Timo

    2016-01-01

    Although reports on big data success stories have been accumulating in the media, most organizations dealing with high-volume, high-velocity and high-variety information assets still face challenges. Only a thorough understanding of these challenges puts organizations into a position in which...... they can make an informed decision for or against big data, and, if the decision is positive, overcome the challenges smoothly. The combination of a series of interviews with leading experts from enterprises, associations and research institutions, and focused literature reviews allowed not only...... framework are also relevant. For large enterprises and startups specialized in big data, it is typically easier to overcome the challenges than it is for other enterprises and public administration bodies....

  10. Exploring complex and big data

    Directory of Open Access Journals (Sweden)

    Stefanowski Jerzy

    2017-12-01

    Full Text Available This paper shows how big data analysis opens a range of research and technological problems and calls for new approaches. We start with defining the essential properties of big data and discussing the main types of data involved. We then survey the dedicated solutions for storing and processing big data, including a data lake, virtual integration, and a polystore architecture. Difficulties in managing data quality and provenance are also highlighted. The characteristics of big data imply also specific requirements and challenges for data mining algorithms, which we address as well. The links with related areas, including data streams and deep learning, are discussed. The common theme that naturally emerges from this characterization is complexity. All in all, we consider it to be the truly defining feature of big data (posing particular research and technological challenges, which ultimately seems to be of greater importance than the sheer data volume.

  11. Big Data; A Management Revolution : The emerging role of big data in businesses

    OpenAIRE

    Blasiak, Kevin

    2014-01-01

    Big data is a term that was coined in 2012 and has since then emerged to one of the top trends in business and technology. Big data is an agglomeration of different technologies resulting in data processing capabilities that have been unreached before. Big data is generally characterized by 4 factors. Volume, velocity and variety. These three factors distinct it from the traditional data use. The possibilities to utilize this technology are vast. Big data technology has touch points in differ...

  12. Big Surveys, Big Data Centres

    Science.gov (United States)

    Schade, D.

    2016-06-01

    Well-designed astronomical surveys are powerful and have consistently been keystones of scientific progress. The Byurakan Surveys using a Schmidt telescope with an objective prism produced a list of about 3000 UV-excess Markarian galaxies but these objects have stimulated an enormous amount of further study and appear in over 16,000 publications. The CFHT Legacy Surveys used a wide-field imager to cover thousands of square degrees and those surveys are mentioned in over 1100 publications since 2002. Both ground and space-based astronomy have been increasing their investments in survey work. Survey instrumentation strives toward fair samples and large sky coverage and therefore strives to produce massive datasets. Thus we are faced with the "big data" problem in astronomy. Survey datasets require specialized approaches to data management. Big data places additional challenging requirements for data management. If the term "big data" is defined as data collections that are too large to move then there are profound implications for the infrastructure that supports big data science. The current model of data centres is obsolete. In the era of big data the central problem is how to create architectures that effectively manage the relationship between data collections, networks, processing capabilities, and software, given the science requirements of the projects that need to be executed. A stand alone data silo cannot support big data science. I'll describe the current efforts of the Canadian community to deal with this situation and our successes and failures. I'll talk about how we are planning in the next decade to try to create a workable and adaptable solution to support big data science.

  13. Nursing Needs Big Data and Big Data Needs Nursing.

    Science.gov (United States)

    Brennan, Patricia Flatley; Bakken, Suzanne

    2015-09-01

    Contemporary big data initiatives in health care will benefit from greater integration with nursing science and nursing practice; in turn, nursing science and nursing practice has much to gain from the data science initiatives. Big data arises secondary to scholarly inquiry (e.g., -omics) and everyday observations like cardiac flow sensors or Twitter feeds. Data science methods that are emerging ensure that these data be leveraged to improve patient care. Big data encompasses data that exceed human comprehension, that exist at a volume unmanageable by standard computer systems, that arrive at a velocity not under the control of the investigator and possess a level of imprecision not found in traditional inquiry. Data science methods are emerging to manage and gain insights from big data. The primary methods included investigation of emerging federal big data initiatives, and exploration of exemplars from nursing informatics research to benchmark where nursing is already poised to participate in the big data revolution. We provide observations and reflections on experiences in the emerging big data initiatives. Existing approaches to large data set analysis provide a necessary but not sufficient foundation for nursing to participate in the big data revolution. Nursing's Social Policy Statement guides a principled, ethical perspective on big data and data science. There are implications for basic and advanced practice clinical nurses in practice, for the nurse scientist who collaborates with data scientists, and for the nurse data scientist. Big data and data science has the potential to provide greater richness in understanding patient phenomena and in tailoring interventional strategies that are personalized to the patient. © 2015 Sigma Theta Tau International.

  14. Turning big bang into big bounce: II. Quantum dynamics

    Energy Technology Data Exchange (ETDEWEB)

    Malkiewicz, Przemyslaw; Piechocki, Wlodzimierz, E-mail: pmalk@fuw.edu.p, E-mail: piech@fuw.edu.p [Theoretical Physics Department, Institute for Nuclear Studies, Hoza 69, 00-681 Warsaw (Poland)

    2010-11-21

    We analyze the big bounce transition of the quantum Friedmann-Robertson-Walker model in the setting of the nonstandard loop quantum cosmology (LQC). Elementary observables are used to quantize composite observables. The spectrum of the energy density operator is bounded and continuous. The spectrum of the volume operator is bounded from below and discrete. It has equally distant levels defining a quantum of the volume. The discreteness may imply a foamy structure of spacetime at a semiclassical level which may be detected in astro-cosmo observations. The nonstandard LQC method has a free parameter that should be fixed in some way to specify the big bounce transition.

  15. Big Opportunities and Big Concerns of Big Data in Education

    Science.gov (United States)

    Wang, Yinying

    2016-01-01

    Against the backdrop of the ever-increasing influx of big data, this article examines the opportunities and concerns over big data in education. Specifically, this article first introduces big data, followed by delineating the potential opportunities of using big data in education in two areas: learning analytics and educational policy. Then, the…

  16. Big Dreams

    Science.gov (United States)

    Benson, Michael T.

    2015-01-01

    The Keen Johnson Building is symbolic of Eastern Kentucky University's historic role as a School of Opportunity. It is a place that has inspired generations of students, many from disadvantaged backgrounds, to dream big dreams. The construction of the Keen Johnson Building was inspired by a desire to create a student union facility that would not…

  17. Big Science

    Energy Technology Data Exchange (ETDEWEB)

    Anon.

    1986-05-15

    Astronomy, like particle physics, has become Big Science where the demands of front line research can outstrip the science budgets of whole nations. Thus came into being the European Southern Observatory (ESO), founded in 1962 to provide European scientists with a major modern observatory to study the southern sky under optimal conditions.

  18. Big Egos in Big Science

    DEFF Research Database (Denmark)

    Andersen, Kristina Vaarst; Jeppesen, Jacob

    In this paper we investigate the micro-mechanisms governing structural evolution and performance of scientific collaboration. Scientific discovery tends not to be lead by so called lone ?stars?, or big egos, but instead by collaboration among groups of researchers, from a multitude of institutions...

  19. Big Data and Big Science

    OpenAIRE

    Di Meglio, Alberto

    2014-01-01

    Brief introduction to the challenges of big data in scientific research based on the work done by the HEP community at CERN and how the CERN openlab promotes collaboration among research institutes and industrial IT companies. Presented at the FutureGov 2014 conference in Singapore.

  20. Big inquiry

    Energy Technology Data Exchange (ETDEWEB)

    Wynne, B [Lancaster Univ. (UK)

    1979-06-28

    The recently published report entitled 'The Big Public Inquiry' from the Council for Science and Society and the Outer Circle Policy Unit is considered, with especial reference to any future enquiry which may take place into the first commercial fast breeder reactor. Proposals embodied in the report include stronger rights for objectors and an attempt is made to tackle the problem that participation in a public inquiry is far too late to be objective. It is felt by the author that the CSS/OCPU report is a constructive contribution to the debate about big technology inquiries but that it fails to understand the deeper currents in the economic and political structure of technology which so influence the consequences of whatever formal procedures are evolved.

  1. Big Data

    OpenAIRE

    Bútora, Matúš

    2017-01-01

    Cieľom bakalárskej práca je popísať problematiku Big Data a agregačné operácie OLAP pre podporu rozhodovania, ktoré sú na ne aplikované pomocou technológie Apache Hadoop. Prevažná časť práce je venovaná popisu práve tejto technológie. Posledná kapitola sa zaoberá spôsobom aplikovania agregačných operácií a problematikou ich realizácie. Nasleduje celkové zhodnotenie práce a možnosti využitia výsledného systému do budúcna. The aim of the bachelor thesis is to describe the Big Data issue and ...

  2. BIG DATA

    OpenAIRE

    Abhishek Dubey

    2018-01-01

    The term 'Big Data' portrays inventive methods and advances to catch, store, disseminate, oversee and break down petabyte-or bigger estimated sets of data with high-speed & diverted structures. Enormous information can be organized, non-structured or half-organized, bringing about inadequacy of routine information administration techniques. Information is produced from different distinctive sources and can touch base in the framework at different rates. With a specific end goal to handle this...

  3. Big Data Analytics in Healthcare.

    Science.gov (United States)

    Belle, Ashwin; Thiagarajan, Raghuram; Soroushmehr, S M Reza; Navidi, Fatemeh; Beard, Daniel A; Najarian, Kayvan

    2015-01-01

    The rapidly expanding field of big data analytics has started to play a pivotal role in the evolution of healthcare practices and research. It has provided tools to accumulate, manage, analyze, and assimilate large volumes of disparate, structured, and unstructured data produced by current healthcare systems. Big data analytics has been recently applied towards aiding the process of care delivery and disease exploration. However, the adoption rate and research development in this space is still hindered by some fundamental problems inherent within the big data paradigm. In this paper, we discuss some of these major challenges with a focus on three upcoming and promising areas of medical research: image, signal, and genomics based analytics. Recent research which targets utilization of large volumes of medical data while combining multimodal data from disparate sources is discussed. Potential areas of research within this field which have the ability to provide meaningful impact on healthcare delivery are also examined.

  4. Big Data and HPC: A Happy Marriage

    KAUST Repository

    Mehmood, Rashid

    2016-01-01

    International Data Corporation (IDC) defines Big Data technologies as “a new generation of technologies and architectures, designed to economically extract value from very large volumes of a wide variety of data produced every day, by enabling high

  5. Big Data for personalized healthcare

    NARCIS (Netherlands)

    Siemons, Liseth; Sieverink, Floor; Vollenbroek, Wouter; van de Wijngaert, Lidwien; Braakman-Jansen, Annemarie; van Gemert-Pijnen, Lisette

    2016-01-01

    Big Data, often defined according to the 5V model (volume, velocity, variety, veracity and value), is seen as the key towards personalized healthcare. However, it also confronts us with new technological and ethical challenges that require more sophisticated data management tools and data analysis

  6. Keynote: Big Data, Big Opportunities

    OpenAIRE

    Borgman, Christine L.

    2014-01-01

    The enthusiasm for big data is obscuring the complexity and diversity of data in scholarship and the challenges for stewardship. Inside the black box of data are a plethora of research, technology, and policy issues. Data are not shiny objects that are easily exchanged. Rather, data are representations of observations, objects, or other entities used as evidence of phenomena for the purposes of research or scholarship. Data practices are local, varying from field to field, individual to indiv...

  7. Addressing big data issues in Scientific Data Infrastructure

    NARCIS (Netherlands)

    Demchenko, Y.; Membrey, P.; Grosso, P.; de Laat, C.; Smari, W.W.; Fox, G.C.

    2013-01-01

    Big Data are becoming a new technology focus both in science and in industry. This paper discusses the challenges that are imposed by Big Data on the modern and future Scientific Data Infrastructure (SDI). The paper discusses a nature and definition of Big Data that include such features as Volume,

  8. Visualizing big energy data

    DEFF Research Database (Denmark)

    Hyndman, Rob J.; Liu, Xueqin Amy; Pinson, Pierre

    2018-01-01

    Visualization is a crucial component of data analysis. It is always a good idea to plot the data before fitting models, making predictions, or drawing conclusions. As sensors of the electric grid are collecting large volumes of data from various sources, power industry professionals are facing th...... the challenge of visualizing such data in a timely fashion. In this article, we demonstrate several data-visualization solutions for big energy data through three case studies involving smart-meter data, phasor measurement unit (PMU) data, and probabilistic forecasts, respectively....

  9. Big Data Challenges

    Directory of Open Access Journals (Sweden)

    Alexandru Adrian TOLE

    2013-10-01

    Full Text Available The amount of data that is traveling across the internet today, not only that is large, but is complex as well. Companies, institutions, healthcare system etc., all of them use piles of data which are further used for creating reports in order to ensure continuity regarding the services that they have to offer. The process behind the results that these entities requests represents a challenge for software developers and companies that provide IT infrastructure. The challenge is how to manipulate an impressive volume of data that has to be securely delivered through the internet and reach its destination intact. This paper treats the challenges that Big Data creates.

  10. Networking for big data

    CERN Document Server

    Yu, Shui; Misic, Jelena; Shen, Xuemin (Sherman)

    2015-01-01

    Networking for Big Data supplies an unprecedented look at cutting-edge research on the networking and communication aspects of Big Data. Starting with a comprehensive introduction to Big Data and its networking issues, it offers deep technical coverage of both theory and applications.The book is divided into four sections: introduction to Big Data, networking theory and design for Big Data, networking security for Big Data, and platforms and systems for Big Data applications. Focusing on key networking issues in Big Data, the book explains network design and implementation for Big Data. It exa

  11. Big Data's Role in Precision Public Health.

    Science.gov (United States)

    Dolley, Shawn

    2018-01-01

    Precision public health is an emerging practice to more granularly predict and understand public health risks and customize treatments for more specific and homogeneous subpopulations, often using new data, technologies, and methods. Big data is one element that has consistently helped to achieve these goals, through its ability to deliver to practitioners a volume and variety of structured or unstructured data not previously possible. Big data has enabled more widespread and specific research and trials of stratifying and segmenting populations at risk for a variety of health problems. Examples of success using big data are surveyed in surveillance and signal detection, predicting future risk, targeted interventions, and understanding disease. Using novel big data or big data approaches has risks that remain to be resolved. The continued growth in volume and variety of available data, decreased costs of data capture, and emerging computational methods mean big data success will likely be a required pillar of precision public health into the future. This review article aims to identify the precision public health use cases where big data has added value, identify classes of value that big data may bring, and outline the risks inherent in using big data in precision public health efforts.

  12. Big Data

    DEFF Research Database (Denmark)

    Aaen, Jon; Nielsen, Jeppe Agger

    2016-01-01

    Big Data byder sig til som en af tidens mest hypede teknologiske innovationer, udråbt til at rumme kimen til nye, værdifulde operationelle indsigter for private virksomheder og offentlige organisationer. Mens de optimistiske udmeldinger er mange, er forskningen i Big Data i den offentlige sektor...... indtil videre begrænset. Denne artikel belyser, hvordan den offentlige sundhedssektor kan genanvende og udnytte en stadig større mængde data under hensyntagen til offentlige værdier. Artiklen bygger på et casestudie af anvendelsen af store mængder sundhedsdata i Dansk AlmenMedicinsk Database (DAMD......). Analysen viser, at (gen)brug af data i nye sammenhænge er en flerspektret afvejning mellem ikke alene økonomiske rationaler og kvalitetshensyn, men også kontrol over personfølsomme data og etiske implikationer for borgeren. I DAMD-casen benyttes data på den ene side ”i den gode sags tjeneste” til...

  13. Big data analytics turning big data into big money

    CERN Document Server

    Ohlhorst, Frank J

    2012-01-01

    Unique insights to implement big data analytics and reap big returns to your bottom line Focusing on the business and financial value of big data analytics, respected technology journalist Frank J. Ohlhorst shares his insights on the newly emerging field of big data analytics in Big Data Analytics. This breakthrough book demonstrates the importance of analytics, defines the processes, highlights the tangible and intangible values and discusses how you can turn a business liability into actionable material that can be used to redefine markets, improve profits and identify new business opportuni

  14. Medical big data: promise and challenges

    Directory of Open Access Journals (Sweden)

    Choong Ho Lee

    2017-03-01

    Full Text Available The concept of big data, commonly characterized by volume, variety, velocity, and veracity, goes far beyond the data type and includes the aspects of data analysis, such as hypothesis-generating, rather than hypothesis-testing. Big data focuses on temporal stability of the association, rather than on causal relationship and underlying probability distribution assumptions are frequently not required. Medical big data as material to be analyzed has various features that are not only distinct from big data of other disciplines, but also distinct from traditional clinical epidemiology. Big data technology has many areas of application in healthcare, such as predictive modeling and clinical decision support, disease or safety surveillance, public health, and research. Big data analytics frequently exploits analytic methods developed in data mining, including classification, clustering, and regression. Medical big data analyses are complicated by many technical issues, such as missing values, curse of dimensionality, and bias control, and share the inherent limitations of observation study, namely the inability to test causality resulting from residual confounding and reverse causation. Recently, propensity score analysis and instrumental variable analysis have been introduced to overcome these limitations, and they have accomplished a great deal. Many challenges, such as the absence of evidence of practical benefits of big data, methodological issues including legal and ethical issues, and clinical integration and utility issues, must be overcome to realize the promise of medical big data as the fuel of a continuous learning healthcare system that will improve patient outcome and reduce waste in areas including nephrology.

  15. Medical big data: promise and challenges.

    Science.gov (United States)

    Lee, Choong Ho; Yoon, Hyung-Jin

    2017-03-01

    The concept of big data, commonly characterized by volume, variety, velocity, and veracity, goes far beyond the data type and includes the aspects of data analysis, such as hypothesis-generating, rather than hypothesis-testing. Big data focuses on temporal stability of the association, rather than on causal relationship and underlying probability distribution assumptions are frequently not required. Medical big data as material to be analyzed has various features that are not only distinct from big data of other disciplines, but also distinct from traditional clinical epidemiology. Big data technology has many areas of application in healthcare, such as predictive modeling and clinical decision support, disease or safety surveillance, public health, and research. Big data analytics frequently exploits analytic methods developed in data mining, including classification, clustering, and regression. Medical big data analyses are complicated by many technical issues, such as missing values, curse of dimensionality, and bias control, and share the inherent limitations of observation study, namely the inability to test causality resulting from residual confounding and reverse causation. Recently, propensity score analysis and instrumental variable analysis have been introduced to overcome these limitations, and they have accomplished a great deal. Many challenges, such as the absence of evidence of practical benefits of big data, methodological issues including legal and ethical issues, and clinical integration and utility issues, must be overcome to realize the promise of medical big data as the fuel of a continuous learning healthcare system that will improve patient outcome and reduce waste in areas including nephrology.

  16. GEOSS: Addressing Big Data Challenges

    Science.gov (United States)

    Nativi, S.; Craglia, M.; Ochiai, O.

    2014-12-01

    In the sector of Earth Observation, the explosion of data is due to many factors including: new satellite constellations, the increased capabilities of sensor technologies, social media, crowdsourcing, and the need for multidisciplinary and collaborative research to face Global Changes. In this area, there are many expectations and concerns about Big Data. Vendors have attempted to use this term for their commercial purposes. It is necessary to understand whether Big Data is a radical shift or an incremental change for the existing digital infrastructures. This presentation tries to explore and discuss the impact of Big Data challenges and new capabilities on the Global Earth Observation System of Systems (GEOSS) and particularly on its common digital infrastructure called GCI. GEOSS is a global and flexible network of content providers allowing decision makers to access an extraordinary range of data and information at their desk. The impact of the Big Data dimensionalities (commonly known as 'V' axes: volume, variety, velocity, veracity, visualization) on GEOSS is discussed. The main solutions and experimentation developed by GEOSS along these axes are introduced and analyzed. GEOSS is a pioneering framework for global and multidisciplinary data sharing in the Earth Observation realm; its experience on Big Data is valuable for the many lessons learned.

  17. Big data analytics methods and applications

    CERN Document Server

    Rao, BLS; Rao, SB

    2016-01-01

    This book has a collection of articles written by Big Data experts to describe some of the cutting-edge methods and applications from their respective areas of interest, and provides the reader with a detailed overview of the field of Big Data Analytics as it is practiced today. The chapters cover technical aspects of key areas that generate and use Big Data such as management and finance; medicine and healthcare; genome, cytome and microbiome; graphs and networks; Internet of Things; Big Data standards; bench-marking of systems; and others. In addition to different applications, key algorithmic approaches such as graph partitioning, clustering and finite mixture modelling of high-dimensional data are also covered. The varied collection of themes in this volume introduces the reader to the richness of the emerging field of Big Data Analytics.

  18. Epidemiology in the Era of Big Data

    Science.gov (United States)

    Mooney, Stephen J; Westreich, Daniel J; El-Sayed, Abdulrahman M

    2015-01-01

    Big Data has increasingly been promoted as a revolutionary development in the future of science, including epidemiology. However, the definition and implications of Big Data for epidemiology remain unclear. We here provide a working definition of Big Data predicated on the so-called ‘3 Vs’: variety, volume, and velocity. From this definition, we argue that Big Data has evolutionary and revolutionary implications for identifying and intervening on the determinants of population health. We suggest that as more sources of diverse data become publicly available, the ability to combine and refine these data to yield valid answers to epidemiologic questions will be invaluable. We conclude that, while epidemiology as practiced today will continue to be practiced in the Big Data future, a component of our field’s future value lies in integrating subject matter knowledge with increased technical savvy. Our training programs and our visions for future public health interventions should reflect this future. PMID:25756221

  19. [Big data in imaging].

    Science.gov (United States)

    Sewerin, Philipp; Ostendorf, Benedikt; Hueber, Axel J; Kleyer, Arnd

    2018-04-01

    Until now, most major medical advancements have been achieved through hypothesis-driven research within the scope of clinical trials. However, due to a multitude of variables, only a certain number of research questions could be addressed during a single study, thus rendering these studies expensive and time consuming. Big data acquisition enables a new data-based approach in which large volumes of data can be used to investigate all variables, thus opening new horizons. Due to universal digitalization of the data as well as ever-improving hard- and software solutions, imaging would appear to be predestined for such analyses. Several small studies have already demonstrated that automated analysis algorithms and artificial intelligence can identify pathologies with high precision. Such automated systems would also seem well suited for rheumatology imaging, since a method for individualized risk stratification has long been sought for these patients. However, despite all the promising options, the heterogeneity of the data and highly complex regulations covering data protection in Germany would still render a big data solution for imaging difficult today. Overcoming these boundaries is challenging, but the enormous potential advances in clinical management and science render pursuit of this goal worthwhile.

  20. NURE aerial gamma-ray and magnetic reconnaissance survey: Big Bend area, Marfa MH 13-5, Fort Stockton MH 13-6, Presidio MH 13-8, Emory Peak MH 13-9 Quadrangles. Volume I. Narrative report

    International Nuclear Information System (INIS)

    1979-02-01

    A rotary-wing, reconnaissance, high sensitivity, radiometric and magnetic survey was performed in the Big Bend area of Texas. Four 1:250,000 scale NTMS quadrangles (Marfa, Ft. Stockton, Presidio, and Emory Peak) were surveyed. A total of 7,529 line miles (12,115 kilometers) of data were collected utilizing a Sikorsky S58T helicopter. Traverse lines were flown in an east-west direction at 3.0 mile (5 kilometer) spacing, with tie lines flown in a north-south direction at 12.5 mile (20 kilometer) spacing. The data were digitally recorded at 1.0 second intervals. The NaI terrestrial detectors used in this survey had a total volume of 2,154 cubic inches. The magnetometer employed was a modified ASQ-10 fluxgate system. The radiometric data was normalized to 400 feet terrain clearance and is presented in the form of computer listings on microfiche and as stacked profile plots. Profile plots are contained in Volume II of this report. A geologic interpretation of the radiometric and magnetic data is included as part of this report

  1. Automated Big Traffic Analytics for Cyber Security

    OpenAIRE

    Miao, Yuantian; Ruan, Zichan; Pan, Lei; Wang, Yu; Zhang, Jun; Xiang, Yang

    2018-01-01

    Network traffic analytics technology is a cornerstone for cyber security systems. We demonstrate its use through three popular and contemporary cyber security applications in intrusion detection, malware analysis and botnet detection. However, automated traffic analytics faces the challenges raised by big traffic data. In terms of big data's three characteristics --- volume, variety and velocity, we review three state of the art techniques to mitigate the key challenges including real-time tr...

  2. Big Data in the Aerospace Industry

    Directory of Open Access Journals (Sweden)

    Victor Emmanuell BADEA

    2018-01-01

    Full Text Available This paper presents the approaches related to the need for large volume data analysis, Big Data, and also the information that the beneficiaries of this analysis can interpret. Aerospace companies understand better the challenges of Big Data than the rest of the industries. Also, in this paper we describe a novel analytical system that enables query processing and predictive analytics over streams of large aviation data.

  3. Big data governance an emerging imperative

    CERN Document Server

    Soares, Sunil

    2012-01-01

    Written by a leading expert in the field, this guide focuses on the convergence of two major trends in information management-big data and information governance-by taking a strategic approach oriented around business cases and industry imperatives. With the advent of new technologies, enterprises are expanding and handling very large volumes of data; this book, nontechnical in nature and geared toward business audiences, encourages the practice of establishing appropriate governance over big data initiatives and addresses how to manage and govern big data, highlighting the relevant processes,

  4. Big Data - Smart Health Strategies

    Science.gov (United States)

    2014-01-01

    Summary Objectives To select best papers published in 2013 in the field of big data and smart health strategies, and summarize outstanding research efforts. Methods A systematic search was performed using two major bibliographic databases for relevant journal papers. The references obtained were reviewed in a two-stage process, starting with a blinded review performed by the two section editors, and followed by a peer review process operated by external reviewers recognized as experts in the field. Results The complete review process selected four best papers, illustrating various aspects of the special theme, among them: (a) using large volumes of unstructured data and, specifically, clinical notes from Electronic Health Records (EHRs) for pharmacovigilance; (b) knowledge discovery via querying large volumes of complex (both structured and unstructured) biological data using big data technologies and relevant tools; (c) methodologies for applying cloud computing and big data technologies in the field of genomics, and (d) system architectures enabling high-performance access to and processing of large datasets extracted from EHRs. Conclusions The potential of big data in biomedicine has been pinpointed in various viewpoint papers and editorials. The review of current scientific literature illustrated a variety of interesting methods and applications in the field, but still the promises exceed the current outcomes. As we are getting closer towards a solid foundation with respect to common understanding of relevant concepts and technical aspects, and the use of standardized technologies and tools, we can anticipate to reach the potential that big data offer for personalized medicine and smart health strategies in the near future. PMID:25123721

  5. Big bacteria

    DEFF Research Database (Denmark)

    Schulz, HN; Jørgensen, BB

    2001-01-01

    A small number of prokaryotic species have a unique physiology or ecology related to their development of unusually large size. The biomass of bacteria varies over more than 10 orders of magnitude, from the 0.2 mum wide nanobacteria to the largest cells of the colorless sulfur bacteria...... and by actively creating an advective flow through the entire population. Diffusion limitation generally restricts the maximal size of prokaryotic cells and provides a selective advantage for mum-sized cells at the normally low substrate concentrations in the environment. The largest heterotrophic bacteria......, the 80 x 600 mum large Epulopiscium sp. from the gut of tropical fish, are presumably living in a very nutrient-rich medium. Many large bacteria contain numerous inclusions in the cells that reduce the volume of active cytoplasm. The most striking examples of competitive advantage from large cell size...

  6. Big Data, Big Problems: A Healthcare Perspective.

    Science.gov (United States)

    Househ, Mowafa S; Aldosari, Bakheet; Alanazi, Abdullah; Kushniruk, Andre W; Borycki, Elizabeth M

    2017-01-01

    Much has been written on the benefits of big data for healthcare such as improving patient outcomes, public health surveillance, and healthcare policy decisions. Over the past five years, Big Data, and the data sciences field in general, has been hyped as the "Holy Grail" for the healthcare industry promising a more efficient healthcare system with the promise of improved healthcare outcomes. However, more recently, healthcare researchers are exposing the potential and harmful effects Big Data can have on patient care associating it with increased medical costs, patient mortality, and misguided decision making by clinicians and healthcare policy makers. In this paper, we review the current Big Data trends with a specific focus on the inadvertent negative impacts that Big Data could have on healthcare, in general, and specifically, as it relates to patient and clinical care. Our study results show that although Big Data is built up to be as a the "Holy Grail" for healthcare, small data techniques using traditional statistical methods are, in many cases, more accurate and can lead to more improved healthcare outcomes than Big Data methods. In sum, Big Data for healthcare may cause more problems for the healthcare industry than solutions, and in short, when it comes to the use of data in healthcare, "size isn't everything."

  7. Big Game Reporting Stations

    Data.gov (United States)

    Vermont Center for Geographic Information — Point locations of big game reporting stations. Big game reporting stations are places where hunters can legally report harvested deer, bear, or turkey. These are...

  8. Stalin's Big Fleet Program

    National Research Council Canada - National Science Library

    Mauner, Milan

    2002-01-01

    Although Dr. Milan Hauner's study 'Stalin's Big Fleet program' has focused primarily on the formation of Big Fleets during the Tsarist and Soviet periods of Russia's naval history, there are important lessons...

  9. Big Data Semantics

    NARCIS (Netherlands)

    Ceravolo, Paolo; Azzini, Antonia; Angelini, Marco; Catarci, Tiziana; Cudré-Mauroux, Philippe; Damiani, Ernesto; Mazak, Alexandra; van Keulen, Maurice; Jarrar, Mustafa; Santucci, Giuseppe; Sattler, Kai-Uwe; Scannapieco, Monica; Wimmer, Manuel; Wrembel, Robert; Zaraket, Fadi

    2018-01-01

    Big Data technology has discarded traditional data modeling approaches as no longer applicable to distributed data processing. It is, however, largely recognized that Big Data impose novel challenges in data and infrastructure management. Indeed, multiple components and procedures must be

  10. 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.

  11. Big Data Provenance: Challenges, State of the Art and Opportunities

    OpenAIRE

    Wang, Jianwu; Crawl, Daniel; Purawat, Shweta; Nguyen, Mai; Altintas, Ilkay

    2015-01-01

    Ability to track provenance is a key feature of scientific workflows to support data lineage and reproducibility. The challenges that are introduced by the volume, variety and velocity of Big Data, also pose related challenges for provenance and quality of Big Data, defined as veracity. The increasing size and variety of distributed Big Data provenance information bring new technical challenges and opportunities throughout the provenance lifecycle including recording, querying, sharing and ut...

  12. Modeling and Management of Big Data: Challenges and opportunities

    OpenAIRE

    Gil, David; Song, Il-Yeol

    2016-01-01

    The term Big Data denotes huge-volume, complex, rapid growing datasets with numerous, autonomous and independent sources. In these new circumstances Big Data bring many attractive opportunities; however, good opportunities are always followed by challenges, such as modelling, new paradigms, novel architectures that require original approaches to address data complexities. The purpose of this special issue on Modeling and Management of Big Data is to discuss research and experience in modellin...

  13. Big Spectrum Data: The New Resource for Cognitive Wireless Networking

    OpenAIRE

    Ding, Guoru; Wu, Qihui; Wang, Jinlong; Yao, Yu-Dong

    2014-01-01

    The era of Big Data is here now, which has brought both unprecedented opportunities and critical challenges. In this article, from a perspective of cognitive wireless networking, we start with a definition of Big Spectrum Data by analyzing its characteristics in terms of six Vs, i.e., volume, variety, velocity, veracity, viability, and value. We then present a high-level tutorial on research frontiers in Big Spectrum Data analytics to guide the development of practical algorithms. We also hig...

  14. Big Data: Survey, Technologies, Opportunities, and Challenges

    Directory of Open Access Journals (Sweden)

    Nawsher Khan

    2014-01-01

    Full Text Available Big Data has gained much attention from the academia and the IT industry. In the digital and computing world, information is generated and collected at a rate that rapidly exceeds the boundary range. Currently, over 2 billion people worldwide are connected to the Internet, and over 5 billion individuals own mobile phones. By 2020, 50 billion devices are expected to be connected to the Internet. At this point, predicted data production will be 44 times greater than that in 2009. As information is transferred and shared at light speed on optic fiber and wireless networks, the volume of data and the speed of market growth increase. However, the fast growth rate of such large data generates numerous challenges, such as the rapid growth of data, transfer speed, diverse data, and security. Nonetheless, Big Data is still in its infancy stage, and the domain has not been reviewed in general. Hence, this study comprehensively surveys and classifies the various attributes of Big Data, including its nature, definitions, rapid growth rate, volume, management, analysis, and security. This study also proposes a data life cycle that uses the technologies and terminologies of Big Data. Future research directions in this field are determined based on opportunities and several open issues in Big Data domination. These research directions facilitate the exploration of the domain and the development of optimal techniques to address Big Data.

  15. Big data: survey, technologies, opportunities, and challenges.

    Science.gov (United States)

    Khan, Nawsher; Yaqoob, Ibrar; Hashem, Ibrahim Abaker Targio; Inayat, Zakira; Ali, Waleed Kamaleldin Mahmoud; Alam, Muhammad; Shiraz, Muhammad; Gani, Abdullah

    2014-01-01

    Big Data has gained much attention from the academia and the IT industry. In the digital and computing world, information is generated and collected at a rate that rapidly exceeds the boundary range. Currently, over 2 billion people worldwide are connected to the Internet, and over 5 billion individuals own mobile phones. By 2020, 50 billion devices are expected to be connected to the Internet. At this point, predicted data production will be 44 times greater than that in 2009. As information is transferred and shared at light speed on optic fiber and wireless networks, the volume of data and the speed of market growth increase. However, the fast growth rate of such large data generates numerous challenges, such as the rapid growth of data, transfer speed, diverse data, and security. Nonetheless, Big Data is still in its infancy stage, and the domain has not been reviewed in general. Hence, this study comprehensively surveys and classifies the various attributes of Big Data, including its nature, definitions, rapid growth rate, volume, management, analysis, and security. This study also proposes a data life cycle that uses the technologies and terminologies of Big Data. Future research directions in this field are determined based on opportunities and several open issues in Big Data domination. These research directions facilitate the exploration of the domain and the development of optimal techniques to address Big Data.

  16. Main Issues in Big Data Security

    Directory of Open Access Journals (Sweden)

    Julio Moreno

    2016-09-01

    Full Text Available Data is currently one of the most important assets for companies in every field. The continuous growth in the importance and volume of data has created a new problem: it cannot be handled by traditional analysis techniques. This problem was, therefore, solved through the creation of a new paradigm: Big Data. However, Big Data originated new issues related not only to the volume or the variety of the data, but also to data security and privacy. In order to obtain a full perspective of the problem, we decided to carry out an investigation with the objective of highlighting the main issues regarding Big Data security, and also the solutions proposed by the scientific community to solve them. In this paper, we explain the results obtained after applying a systematic mapping study to security in the Big Data ecosystem. It is almost impossible to carry out detailed research into the entire topic of security, and the outcome of this research is, therefore, a big picture of the main problems related to security in a Big Data system, along with the principal solutions to them proposed by the research community.

  17. Big Data: Survey, Technologies, Opportunities, and Challenges

    Science.gov (United States)

    Khan, Nawsher; Yaqoob, Ibrar; Hashem, Ibrahim Abaker Targio; Inayat, Zakira; Mahmoud Ali, Waleed Kamaleldin; Alam, Muhammad; Shiraz, Muhammad; Gani, Abdullah

    2014-01-01

    Big Data has gained much attention from the academia and the IT industry. In the digital and computing world, information is generated and collected at a rate that rapidly exceeds the boundary range. Currently, over 2 billion people worldwide are connected to the Internet, and over 5 billion individuals own mobile phones. By 2020, 50 billion devices are expected to be connected to the Internet. At this point, predicted data production will be 44 times greater than that in 2009. As information is transferred and shared at light speed on optic fiber and wireless networks, the volume of data and the speed of market growth increase. However, the fast growth rate of such large data generates numerous challenges, such as the rapid growth of data, transfer speed, diverse data, and security. Nonetheless, Big Data is still in its infancy stage, and the domain has not been reviewed in general. Hence, this study comprehensively surveys and classifies the various attributes of Big Data, including its nature, definitions, rapid growth rate, volume, management, analysis, and security. This study also proposes a data life cycle that uses the technologies and terminologies of Big Data. Future research directions in this field are determined based on opportunities and several open issues in Big Data domination. These research directions facilitate the exploration of the domain and the development of optimal techniques to address Big Data. PMID:25136682

  18. Big data computing

    CERN Document Server

    Akerkar, Rajendra

    2013-01-01

    Due to market forces and technological evolution, Big Data computing is developing at an increasing rate. A wide variety of novel approaches and tools have emerged to tackle the challenges of Big Data, creating both more opportunities and more challenges for students and professionals in the field of data computation and analysis. Presenting a mix of industry cases and theory, Big Data Computing discusses the technical and practical issues related to Big Data in intelligent information management. Emphasizing the adoption and diffusion of Big Data tools and technologies in industry, the book i

  19. Microsoft big data solutions

    CERN Document Server

    Jorgensen, Adam; Welch, John; Clark, Dan; Price, Christopher; Mitchell, Brian

    2014-01-01

    Tap the power of Big Data with Microsoft technologies Big Data is here, and Microsoft's new Big Data platform is a valuable tool to help your company get the very most out of it. This timely book shows you how to use HDInsight along with HortonWorks Data Platform for Windows to store, manage, analyze, and share Big Data throughout the enterprise. Focusing primarily on Microsoft and HortonWorks technologies but also covering open source tools, Microsoft Big Data Solutions explains best practices, covers on-premises and cloud-based solutions, and features valuable case studies. Best of all,

  20. Big Data in Drug Discovery.

    Science.gov (United States)

    Brown, Nathan; Cambruzzi, Jean; Cox, Peter J; Davies, Mark; Dunbar, James; Plumbley, Dean; Sellwood, Matthew A; Sim, Aaron; Williams-Jones, Bryn I; Zwierzyna, Magdalena; Sheppard, David W

    2018-01-01

    Interpretation of Big Data in the drug discovery community should enhance project timelines and reduce clinical attrition through improved early decision making. The issues we encounter start with the sheer volume of data and how we first ingest it before building an infrastructure to house it to make use of the data in an efficient and productive way. There are many problems associated with the data itself including general reproducibility, but often, it is the context surrounding an experiment that is critical to success. Help, in the form of artificial intelligence (AI), is required to understand and translate the context. On the back of natural language processing pipelines, AI is also used to prospectively generate new hypotheses by linking data together. We explain Big Data from the context of biology, chemistry and clinical trials, showcasing some of the impressive public domain sources and initiatives now available for interrogation. © 2018 Elsevier B.V. All rights reserved.

  1. Soft computing in big data processing

    CERN Document Server

    Park, Seung-Jong; Lee, Jee-Hyong

    2014-01-01

    Big data is an essential key to build a smart world as a meaning of the streaming, continuous integration of large volume and high velocity data covering from all sources to final destinations. The big data range from data mining, data analysis and decision making, by drawing statistical rules and mathematical patterns through systematical or automatically reasoning. The big data helps serve our life better, clarify our future and deliver greater value. We can discover how to capture and analyze data. Readers will be guided to processing system integrity and implementing intelligent systems. With intelligent systems, we deal with the fundamental data management and visualization challenges in effective management of dynamic and large-scale data, and efficient processing of real-time and spatio-temporal data. Advanced intelligent systems have led to managing the data monitoring, data processing and decision-making in realistic and effective way. Considering a big size of data, variety of data and frequent chan...

  2. Research on Implementing Big Data: Technology, People, & Processes

    Science.gov (United States)

    Rankin, Jenny Grant; Johnson, Margie; Dennis, Randall

    2015-01-01

    When many people hear the term "big data", they primarily think of a technology tool for the collection and reporting of data of high variety, volume, and velocity. However, the complexity of big data is not only the technology, but the supporting processes, policies, and people supporting it. This paper was written by three experts to…

  3. Big data in forensic science and medicine.

    Science.gov (United States)

    Lefèvre, Thomas

    2018-07-01

    In less than a decade, big data in medicine has become quite a phenomenon and many biomedical disciplines got their own tribune on the topic. Perspectives and debates are flourishing while there is a lack for a consensual definition for big data. The 3Vs paradigm is frequently evoked to define the big data principles and stands for Volume, Variety and Velocity. Even according to this paradigm, genuine big data studies are still scarce in medicine and may not meet all expectations. On one hand, techniques usually presented as specific to the big data such as machine learning techniques are supposed to support the ambition of personalized, predictive and preventive medicines. These techniques are mostly far from been new and are more than 50 years old for the most ancient. On the other hand, several issues closely related to the properties of big data and inherited from other scientific fields such as artificial intelligence are often underestimated if not ignored. Besides, a few papers temper the almost unanimous big data enthusiasm and are worth attention since they delineate what is at stakes. In this context, forensic science is still awaiting for its position papers as well as for a comprehensive outline of what kind of contribution big data could bring to the field. The present situation calls for definitions and actions to rationally guide research and practice in big data. It is an opportunity for grounding a true interdisciplinary approach in forensic science and medicine that is mainly based on evidence. Copyright © 2017 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

  4. Big data analytics in support of virtual network topology adaptability

    OpenAIRE

    Gifre Renom, Lluís; Contreras, Luis Miguel; Lopez Alvarez, Victor; Velasco Esteban, Luis Domingo

    2016-01-01

    ABNO's OAM Handler is extended with big data analytics capabilities to anticipate traffic changes in volume and direction. Predicted traffic is used as input for VNT re-optimization. Experimental assessment is realized on UPC's SYNERGY testbed. Peer Reviewed

  5. Principles of big data preparing, sharing, and analyzing complex information

    CERN Document Server

    Berman, Jules J

    2013-01-01

    Principles of Big Data helps readers avoid the common mistakes that endanger all Big Data projects. By stressing simple, fundamental concepts, this book teaches readers how to organize large volumes of complex data, and how to achieve data permanence when the content of the data is constantly changing. General methods for data verification and validation, as specifically applied to Big Data resources, are stressed throughout the book. The book demonstrates how adept analysts can find relationships among data objects held in disparate Big Data resources, when the data objects are endo

  6. Teoria del Big Bang e buchi neri

    CERN Document Server

    Wald, Robert M

    1980-01-01

    Un giovane fisico americano delinea con chiarezza in questo volume le attuali concezioni dello spazio, del tempo e della gravitazione, cosi come si sono andate delineando dopo e innovazioni teoriche aperte da Einstein. Esse investono problemi affascinanti, come la teoria del big bang, da cui avrebbe avuto origine l'universo, e l'enigma dei buchi neri.

  7. The big bang

    International Nuclear Information System (INIS)

    Chown, Marcus.

    1987-01-01

    The paper concerns the 'Big Bang' theory of the creation of the Universe 15 thousand million years ago, and traces events which physicists predict occurred soon after the creation. Unified theory of the moment of creation, evidence of an expanding Universe, the X-boson -the particle produced very soon after the big bang and which vanished from the Universe one-hundredth of a second after the big bang, and the fate of the Universe, are all discussed. (U.K.)

  8. Big Data’s Role in Precision Public Health

    Science.gov (United States)

    Dolley, Shawn

    2018-01-01

    Precision public health is an emerging practice to more granularly predict and understand public health risks and customize treatments for more specific and homogeneous subpopulations, often using new data, technologies, and methods. Big data is one element that has consistently helped to achieve these goals, through its ability to deliver to practitioners a volume and variety of structured or unstructured data not previously possible. Big data has enabled more widespread and specific research and trials of stratifying and segmenting populations at risk for a variety of health problems. Examples of success using big data are surveyed in surveillance and signal detection, predicting future risk, targeted interventions, and understanding disease. Using novel big data or big data approaches has risks that remain to be resolved. The continued growth in volume and variety of available data, decreased costs of data capture, and emerging computational methods mean big data success will likely be a required pillar of precision public health into the future. This review article aims to identify the precision public health use cases where big data has added value, identify classes of value that big data may bring, and outline the risks inherent in using big data in precision public health efforts. PMID:29594091

  9. Big data: the management revolution.

    Science.gov (United States)

    McAfee, Andrew; Brynjolfsson, Erik

    2012-10-01

    Big data, the authors write, is far more powerful than the analytics of the past. Executives can measure and therefore manage more precisely than ever before. They can make better predictions and smarter decisions. They can target more-effective interventions in areas that so far have been dominated by gut and intuition rather than by data and rigor. The differences between big data and analytics are a matter of volume, velocity, and variety: More data now cross the internet every second than were stored in the entire internet 20 years ago. Nearly real-time information makes it possible for a company to be much more agile than its competitors. And that information can come from social networks, images, sensors, the web, or other unstructured sources. The managerial challenges, however, are very real. Senior decision makers have to learn to ask the right questions and embrace evidence-based decision making. Organizations must hire scientists who can find patterns in very large data sets and translate them into useful business information. IT departments have to work hard to integrate all the relevant internal and external sources of data. The authors offer two success stories to illustrate how companies are using big data: PASSUR Aerospace enables airlines to match their actual and estimated arrival times. Sears Holdings directly analyzes its incoming store data to make promotions much more precise and faster.

  10. Summary big data

    CERN Document Server

    2014-01-01

    This work offers a summary of Cukier the book: "Big Data: A Revolution That Will Transform How we Live, Work, and Think" by Viktor Mayer-Schonberg and Kenneth. Summary of the ideas in Viktor Mayer-Schonberg's and Kenneth Cukier's book: " Big Data " explains that big data is where we use huge quantities of data to make better predictions based on the fact we identify patters in the data rather than trying to understand the underlying causes in more detail. This summary highlights that big data will be a source of new economic value and innovation in the future. Moreover, it shows that it will

  11. Data: Big and Small.

    Science.gov (United States)

    Jones-Schenk, Jan

    2017-02-01

    Big data is a big topic in all leadership circles. Leaders in professional development must develop an understanding of what data are available across the organization that can inform effective planning for forecasting. Collaborating with others to integrate data sets can increase the power of prediction. Big data alone is insufficient to make big decisions. Leaders must find ways to access small data and triangulate multiple types of data to ensure the best decision making. J Contin Educ Nurs. 2017;48(2):60-61. Copyright 2017, SLACK Incorporated.

  12. A Big Video Manifesto

    DEFF Research Database (Denmark)

    Mcilvenny, Paul Bruce; Davidsen, Jacob

    2017-01-01

    and beautiful visualisations. However, we also need to ask what the tools of big data can do both for the Humanities and for more interpretative approaches and methods. Thus, we prefer to explore how the power of computation, new sensor technologies and massive storage can also help with video-based qualitative......For the last few years, we have witnessed a hype about the potential results and insights that quantitative big data can bring to the social sciences. The wonder of big data has moved into education, traffic planning, and disease control with a promise of making things better with big numbers...

  13. Will Organization Design Be Affected By Big Data?

    Directory of Open Access Journals (Sweden)

    Giles Slinger

    2014-12-01

    Full Text Available Computing power and analytical methods allow us to create, collate, and analyze more data than ever before. When datasets are unusually large in volume, velocity, and variety, they are referred to as “big data.” Some observers have suggested that in order to cope with big data (a organizational structures will need to change and (b the processes used to design organizations will be different. In this article, we differentiate big data from relatively slow-moving, linked people data. We argue that big data will change organizational structures as organizations pursue the opportunities presented by big data. The processes by which organizations are designed, however, will be relatively unaffected by big data. Instead, organization design processes will be more affected by the complex links found in people data.

  14. Big Data Provenance: Challenges, State of the Art and Opportunities.

    Science.gov (United States)

    Wang, Jianwu; Crawl, Daniel; Purawat, Shweta; Nguyen, Mai; Altintas, Ilkay

    2015-01-01

    Ability to track provenance is a key feature of scientific workflows to support data lineage and reproducibility. The challenges that are introduced by the volume, variety and velocity of Big Data, also pose related challenges for provenance and quality of Big Data, defined as veracity. The increasing size and variety of distributed Big Data provenance information bring new technical challenges and opportunities throughout the provenance lifecycle including recording, querying, sharing and utilization. This paper discusses the challenges and opportunities of Big Data provenance related to the veracity of the datasets themselves and the provenance of the analytical processes that analyze these datasets. It also explains our current efforts towards tracking and utilizing Big Data provenance using workflows as a programming model to analyze Big Data.

  15. Towards large volume big divisor D3/D7 “μ-split supersymmetry” and Ricci-flat Swiss-cheese metrics, and dimension-six neutrino mass operators

    International Nuclear Information System (INIS)

    Dhuria, Mansi; Misra, Aalok

    2012-01-01

    We show that it is possible to realize a “μ-split SUSY” scenario (Cheng and Cheng, 2005) in the context of large volume limit of type IIB compactifications on Swiss-cheese Calabi-Yau orientifolds in the presence of a mobile space-time filling D3-brane and a (stack of) D7-brane(s) wrapping the “big” divisor. For this, we investigate the possibility of getting one Higgs to be light while other to be heavy in addition to a heavy higgsino mass parameter. Further, we examine the existence of long lived gluino that manifests one of the major consequences of μ-split SUSY scenario, by computing its decay width as well as lifetime corresponding to the three-body decays of the gluino into either a quark, a squark and a neutralino or a quark, squark and goldstino, as well as two-body decays of the gluino into either a neutralino and a gluon or a goldstino and a gluon. Guided by the geometric Kähler potential for Σ B obtained in Misra and Shukla (2010) based on GLSM techniques, and the Donaldson's algorithm (Barun et al., 2008) for obtaining numerically a Ricci-flat metric, we give details of our calculation in Misra and Shukla (2011) pertaining to our proposed metric for the full Swiss-cheese Calabi-Yau (the geometric Kähler potential being needed to be included in the full moduli space Kähler potential in the presence of the mobile space-time filling D3-brane), but for simplicity of calculation, close to the big divisor, which is Ricci-flat in the large volume limit. Also, as an application of the one-loop RG flow solution for the higgsino mass parameter, we show that the contribution to the neutrino masses at the EW scale from dimension-six operators arising from the Kähler potential, is suppressed relative to the Weinberg-type dimension-five operators.

  16. Bliver big data til big business?

    DEFF Research Database (Denmark)

    Ritter, Thomas

    2015-01-01

    Danmark har en digital infrastruktur, en registreringskultur og it-kompetente medarbejdere og kunder, som muliggør en førerposition, men kun hvis virksomhederne gør sig klar til næste big data-bølge.......Danmark har en digital infrastruktur, en registreringskultur og it-kompetente medarbejdere og kunder, som muliggør en førerposition, men kun hvis virksomhederne gør sig klar til næste big data-bølge....

  17. Dual of big bang and big crunch

    International Nuclear Information System (INIS)

    Bak, Dongsu

    2007-01-01

    Starting from the Janus solution and its gauge theory dual, we obtain the dual gauge theory description of the cosmological solution by the procedure of double analytic continuation. The coupling is driven either to zero or to infinity at the big-bang and big-crunch singularities, which are shown to be related by the S-duality symmetry. In the dual Yang-Mills theory description, these are nonsingular as the coupling goes to zero in the N=4 super Yang-Mills theory. The cosmological singularities simply signal the failure of the supergravity description of the full type IIB superstring theory

  18. Big Data Analytics in Chemical Engineering.

    Science.gov (United States)

    Chiang, Leo; Lu, Bo; Castillo, Ivan

    2017-06-07

    Big data analytics is the journey to turn data into insights for more informed business and operational decisions. As the chemical engineering community is collecting more data (volume) from different sources (variety), this journey becomes more challenging in terms of using the right data and the right tools (analytics) to make the right decisions in real time (velocity). This article highlights recent big data advancements in five industries, including chemicals, energy, semiconductors, pharmaceuticals, and food, and then discusses technical, platform, and culture challenges. To reach the next milestone in multiplying successes to the enterprise level, government, academia, and industry need to collaboratively focus on workforce development and innovation.

  19. Research in Big Data Warehousing using Hadoop

    Directory of Open Access Journals (Sweden)

    Abderrazak Sebaa

    2017-05-01

    Full Text Available Traditional data warehouses have played a key role in decision support system until the recent past. However, the rapid growing of the data generation by the current applications requires new data warehousing systems: volume and format of collected datasets, data source variety, integration of unstructured data and powerful analytical processing. In the age of the Big Data, it is important to follow this pace and adapt the existing warehouse systems to overcome the new issues and challenges. In this paper, we focus on the data warehousing over big data. We discuss the limitations of the traditional ones. We present its alternative technologies and related future work for data warehousing.

  20. Energy scale of the Big Bounce

    International Nuclear Information System (INIS)

    Malkiewicz, Przemyslaw; Piechocki, Wlodzimierz

    2009-01-01

    We examine the nature of the cosmological Big Bounce transition within the loop geometry underlying loop quantum cosmology at classical and quantum levels. Our canonical quantization method is an alternative to the standard loop quantum cosmology. An evolution parameter we use has a clear interpretation. Our method opens the door for analyses of spectra of physical observables like the energy density and the volume operator. We find that one cannot determine the energy scale specific to the Big Bounce by making use of the loop geometry without an extra input from observational cosmology.

  1. From big data to smart data

    CERN Document Server

    Iafrate, Fernando

    2015-01-01

    A pragmatic approach to Big Data by taking the reader on a journey between Big Data (what it is) and the Smart Data (what it is for). Today's decision making can be reached via information (related to the data), knowledge (related to people and processes), and timing (the capacity to decide, act and react at the right time). The huge increase in volume of data traffic, and its format (unstructured data such as blogs, logs, and video) generated by the "digitalization" of our world modifies radically our relationship to the space (in motion) and time, dimension and by capillarity, the enterpr

  2. Market research & the ethics of big data

    OpenAIRE

    Nunan, Daniel; Di Domenico, M.

    2013-01-01

    The term ‘big data’ has recently emerged to describe a range of technological and\\ud commercial trends enabling the storage and analysis of huge amounts of customer data,\\ud such as that generated by social networks and mobile devices. Much of the commercial\\ud promise of big data is in the ability to generate valuable insights from collecting new\\ud types and volumes of data in ways that were not previously economically viable. At the\\ud same time a number of questions have been raised about...

  3. Big Data and Neuroimaging.

    Science.gov (United States)

    Webb-Vargas, Yenny; Chen, Shaojie; Fisher, Aaron; Mejia, Amanda; Xu, Yuting; Crainiceanu, Ciprian; Caffo, Brian; Lindquist, Martin A

    2017-12-01

    Big Data are of increasing importance in a variety of areas, especially in the biosciences. There is an emerging critical need for Big Data tools and methods, because of the potential impact of advancements in these areas. Importantly, statisticians and statistical thinking have a major role to play in creating meaningful progress in this arena. We would like to emphasize this point in this special issue, as it highlights both the dramatic need for statistical input for Big Data analysis and for a greater number of statisticians working on Big Data problems. We use the field of statistical neuroimaging to demonstrate these points. As such, this paper covers several applications and novel methodological developments of Big Data tools applied to neuroimaging data.

  4. Big data for health.

    Science.gov (United States)

    Andreu-Perez, Javier; Poon, Carmen C Y; Merrifield, Robert D; Wong, Stephen T C; Yang, Guang-Zhong

    2015-07-01

    This paper provides an overview of recent developments in big data in the context of biomedical and health informatics. It outlines the key characteristics of big data and how medical and health informatics, translational bioinformatics, sensor informatics, and imaging informatics will benefit from an integrated approach of piecing together different aspects of personalized information from a diverse range of data sources, both structured and unstructured, covering genomics, proteomics, metabolomics, as well as imaging, clinical diagnosis, and long-term continuous physiological sensing of an individual. It is expected that recent advances in big data will expand our knowledge for testing new hypotheses about disease management from diagnosis to prevention to personalized treatment. The rise of big data, however, also raises challenges in terms of privacy, security, data ownership, data stewardship, and governance. This paper discusses some of the existing activities and future opportunities related to big data for health, outlining some of the key underlying issues that need to be tackled.

  5. From Big Data to Big Business

    DEFF Research Database (Denmark)

    Lund Pedersen, Carsten

    2017-01-01

    Idea in Brief: Problem: There is an enormous profit potential for manufacturing firms in big data, but one of the key barriers to obtaining data-driven growth is the lack of knowledge about which capabilities are needed to extract value and profit from data. Solution: We (BDBB research group at C...

  6. Big data, big knowledge: big data for personalized healthcare.

    Science.gov (United States)

    Viceconti, Marco; Hunter, Peter; Hose, Rod

    2015-07-01

    The idea that the purely phenomenological knowledge that we can extract by analyzing large amounts of data can be useful in healthcare seems to contradict the desire of VPH researchers to build detailed mechanistic models for individual patients. But in practice no model is ever entirely phenomenological or entirely mechanistic. We propose in this position paper that big data analytics can be successfully combined with VPH technologies to produce robust and effective in silico medicine solutions. In order to do this, big data technologies must be further developed to cope with some specific requirements that emerge from this application. Such requirements are: working with sensitive data; analytics of complex and heterogeneous data spaces, including nontextual information; distributed data management under security and performance constraints; specialized analytics to integrate bioinformatics and systems biology information with clinical observations at tissue, organ and organisms scales; and specialized analytics to define the "physiological envelope" during the daily life of each patient. These domain-specific requirements suggest a need for targeted funding, in which big data technologies for in silico medicine becomes the research priority.

  7. Big Data in industry

    Science.gov (United States)

    Latinović, T. S.; Preradović, D. M.; Barz, C. R.; Latinović, M. T.; Petrica, P. P.; Pop-Vadean, A.

    2016-08-01

    The amount of data at the global level has grown exponentially. Along with this phenomena, we have a need for a new unit of measure like exabyte, zettabyte, and yottabyte as the last unit measures the amount of data. The growth of data gives a situation where the classic systems for the collection, storage, processing, and visualization of data losing the battle with a large amount, speed, and variety of data that is generated continuously. Many of data that is created by the Internet of Things, IoT (cameras, satellites, cars, GPS navigation, etc.). It is our challenge to come up with new technologies and tools for the management and exploitation of these large amounts of data. Big Data is a hot topic in recent years in IT circles. However, Big Data is recognized in the business world, and increasingly in the public administration. This paper proposes an ontology of big data analytics and examines how to enhance business intelligence through big data analytics as a service by presenting a big data analytics services-oriented architecture. This paper also discusses the interrelationship between business intelligence and big data analytics. The proposed approach in this paper might facilitate the research and development of business analytics, big data analytics, and business intelligence as well as intelligent agents.

  8. Big data a primer

    CERN Document Server

    Bhuyan, Prachet; Chenthati, Deepak

    2015-01-01

    This book is a collection of chapters written by experts on various aspects of big data. The book aims to explain what big data is and how it is stored and used. The book starts from  the fundamentals and builds up from there. It is intended to serve as a review of the state-of-the-practice in the field of big data handling. The traditional framework of relational databases can no longer provide appropriate solutions for handling big data and making it available and useful to users scattered around the globe. The study of big data covers a wide range of issues including management of heterogeneous data, big data frameworks, change management, finding patterns in data usage and evolution, data as a service, service-generated data, service management, privacy and security. All of these aspects are touched upon in this book. It also discusses big data applications in different domains. The book will prove useful to students, researchers, and practicing database and networking engineers.

  9. Recht voor big data, big data voor recht

    NARCIS (Netherlands)

    Lafarre, Anne

    Big data is een niet meer weg te denken fenomeen in onze maatschappij. Het is de hype cycle voorbij en de eerste implementaties van big data-technieken worden uitgevoerd. Maar wat is nu precies big data? Wat houden de vijf V's in die vaak genoemd worden in relatie tot big data? Ter inleiding van

  10. Assessing Big Data

    DEFF Research Database (Denmark)

    Leimbach, Timo; Bachlechner, Daniel

    2015-01-01

    In recent years, big data has been one of the most controversially discussed technologies in terms of its possible positive and negative impact. Therefore, the need for technology assessments is obvious. This paper first provides, based on the results of a technology assessment study, an overview...... of the potential and challenges associated with big data and then describes the problems experienced during the study as well as methods found helpful to address them. The paper concludes with reflections on how the insights from the technology assessment study may have an impact on the future governance of big...... data....

  11. Big bang nucleosynthesis

    International Nuclear Information System (INIS)

    Boyd, Richard N.

    2001-01-01

    The precision of measurements in modern cosmology has made huge strides in recent years, with measurements of the cosmic microwave background and the determination of the Hubble constant now rivaling the level of precision of the predictions of big bang nucleosynthesis. However, these results are not necessarily consistent with the predictions of the Standard Model of big bang nucleosynthesis. Reconciling these discrepancies may require extensions of the basic tenets of the model, and possibly of the reaction rates that determine the big bang abundances

  12. Big data for dummies

    CERN Document Server

    Hurwitz, Judith; Halper, Fern; Kaufman, Marcia

    2013-01-01

    Find the right big data solution for your business or organization Big data management is one of the major challenges facing business, industry, and not-for-profit organizations. Data sets such as customer transactions for a mega-retailer, weather patterns monitored by meteorologists, or social network activity can quickly outpace the capacity of traditional data management tools. If you need to develop or manage big data solutions, you'll appreciate how these four experts define, explain, and guide you through this new and often confusing concept. You'll learn what it is, why it m

  13. Big Data, indispensable today

    Directory of Open Access Journals (Sweden)

    Radu-Ioan ENACHE

    2015-10-01

    Full Text Available Big data is and will be used more in the future as a tool for everything that happens both online and offline. Of course , online is a real hobbit, Big Data is found in this medium , offering many advantages , being a real help for all consumers. In this paper we talked about Big Data as being a plus in developing new applications, by gathering useful information about the users and their behaviour.We've also presented the key aspects of real-time monitoring and the architecture principles of this technology. The most important benefit brought to this paper is presented in the cloud section.

  14. Big Data: Concept, Potentialities and Vulnerabilities

    Directory of Open Access Journals (Sweden)

    Fernando Almeida

    2018-03-01

    Full Text Available The evolution of information systems and the growth in the use of the Internet and social networks has caused an explosion in the amount of available data relevant to the activities of the companies. Therefore, the treatment of these available data is vital to support operational, tactical and strategic decisions. This paper aims to present the concept of big data and the main technologies that support the analysis of large data volumes. The potential of big data is explored considering nine sectors of activity, such as financial, retail, healthcare, transports, agriculture, energy, manufacturing, public, and media and entertainment. In addition, the main current opportunities, vulnerabilities and privacy challenges of big data are discussed. It was possible to conclude that despite the potential for using the big data to grow in the previously identified areas, there are still some challenges that need to be considered and mitigated, namely the privacy of information, the existence of qualified human resources to work with Big Data and the promotion of a data-driven organizational culture.

  15. Big Data in der Cloud

    DEFF Research Database (Denmark)

    Leimbach, Timo; Bachlechner, Daniel

    2014-01-01

    Technology assessment of big data, in particular cloud based big data services, for the Office for Technology Assessment at the German federal parliament (Bundestag)......Technology assessment of big data, in particular cloud based big data services, for the Office for Technology Assessment at the German federal parliament (Bundestag)...

  16. Small Big Data Congress 2017

    NARCIS (Netherlands)

    Doorn, J.

    2017-01-01

    TNO, in collaboration with the Big Data Value Center, presents the fourth Small Big Data Congress! Our congress aims at providing an overview of practical and innovative applications based on big data. Do you want to know what is happening in applied research with big data? And what can already be

  17. Cryptography for Big Data Security

    Science.gov (United States)

    2015-07-13

    Cryptography for Big Data Security Book Chapter for Big Data: Storage, Sharing, and Security (3S) Distribution A: Public Release Ariel Hamlin1 Nabil...Email: arkady@ll.mit.edu ii Contents 1 Cryptography for Big Data Security 1 1.1 Introduction...48 Chapter 1 Cryptography for Big Data Security 1.1 Introduction With the amount

  18. Big data opportunities and challenges

    CERN Document Server

    2014-01-01

    This ebook aims to give practical guidance for all those who want to understand big data better and learn how to make the most of it. Topics range from big data analysis, mobile big data and managing unstructured data to technologies, governance and intellectual property and security issues surrounding big data.

  19. Big Data as Governmentality

    DEFF Research Database (Denmark)

    Flyverbom, Mikkel; Madsen, Anders Koed; Rasche, Andreas

    This paper conceptualizes how large-scale data and algorithms condition and reshape knowledge production when addressing international development challenges. The concept of governmentality and four dimensions of an analytics of government are proposed as a theoretical framework to examine how big...... data is constituted as an aspiration to improve the data and knowledge underpinning development efforts. Based on this framework, we argue that big data’s impact on how relevant problems are governed is enabled by (1) new techniques of visualizing development issues, (2) linking aspects...... shows that big data problematizes selected aspects of traditional ways to collect and analyze data for development (e.g. via household surveys). We also demonstrate that using big data analyses to address development challenges raises a number of questions that can deteriorate its impact....

  20. Big Data Revisited

    DEFF Research Database (Denmark)

    Kallinikos, Jannis; Constantiou, Ioanna

    2015-01-01

    We elaborate on key issues of our paper New games, new rules: big data and the changing context of strategy as a means of addressing some of the concerns raised by the paper’s commentators. We initially deal with the issue of social data and the role it plays in the current data revolution...... and the technological recording of facts. We further discuss the significance of the very mechanisms by which big data is produced as distinct from the very attributes of big data, often discussed in the literature. In the final section of the paper, we qualify the alleged importance of algorithms and claim...... that the structures of data capture and the architectures in which data generation is embedded are fundamental to the phenomenon of big data....

  1. The Big Bang Singularity

    Science.gov (United States)

    Ling, Eric

    The big bang theory is a model of the universe which makes the striking prediction that the universe began a finite amount of time in the past at the so called "Big Bang singularity." We explore the physical and mathematical justification of this surprising result. After laying down the framework of the universe as a spacetime manifold, we combine physical observations with global symmetrical assumptions to deduce the FRW cosmological models which predict a big bang singularity. Next we prove a couple theorems due to Stephen Hawking which show that the big bang singularity exists even if one removes the global symmetrical assumptions. Lastly, we investigate the conditions one needs to impose on a spacetime if one wishes to avoid a singularity. The ideas and concepts used here to study spacetimes are similar to those used to study Riemannian manifolds, therefore we compare and contrast the two geometries throughout.

  2. BigDansing

    KAUST Repository

    Khayyat, Zuhair; Ilyas, Ihab F.; Jindal, Alekh; Madden, Samuel; Ouzzani, Mourad; Papotti, Paolo; Quiané -Ruiz, Jorge-Arnulfo; Tang, Nan; Yin, Si

    2015-01-01

    of the underlying distributed platform. BigDansing takes these rules into a series of transformations that enable distributed computations and several optimizations, such as shared scans and specialized joins operators. Experimental results on both synthetic

  3. Boarding to Big data

    Directory of Open Access Journals (Sweden)

    Oana Claudia BRATOSIN

    2016-05-01

    Full Text Available Today Big data is an emerging topic, as the quantity of the information grows exponentially, laying the foundation for its main challenge, the value of the information. The information value is not only defined by the value extraction from huge data sets, as fast and optimal as possible, but also by the value extraction from uncertain and inaccurate data, in an innovative manner using Big data analytics. At this point, the main challenge of the businesses that use Big data tools is to clearly define the scope and the necessary output of the business so that the real value can be gained. This article aims to explain the Big data concept, its various classifications criteria, architecture, as well as the impact in the world wide processes.

  4. Big Creek Pit Tags

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The BCPITTAGS database is used to store data from an Oncorhynchus mykiss (steelhead/rainbow trout) population dynamics study in Big Creek, a coastal stream along the...

  5. Scaling Big Data Cleansing

    KAUST Repository

    Khayyat, Zuhair

    2017-01-01

    on top of general-purpose distributed platforms. Its programming inter- face allows users to express data quality rules independently from the requirements of parallel and distributed environments. Without sacrificing their quality, BigDans- ing also

  6. Reframing Open Big Data

    DEFF Research Database (Denmark)

    Marton, Attila; Avital, Michel; Jensen, Tina Blegind

    2013-01-01

    Recent developments in the techniques and technologies of collecting, sharing and analysing data are challenging the field of information systems (IS) research let alone the boundaries of organizations and the established practices of decision-making. Coined ‘open data’ and ‘big data......’, these developments introduce an unprecedented level of societal and organizational engagement with the potential of computational data to generate new insights and information. Based on the commonalities shared by open data and big data, we develop a research framework that we refer to as open big data (OBD......) by employing the dimensions of ‘order’ and ‘relationality’. We argue that these dimensions offer a viable approach for IS research on open and big data because they address one of the core value propositions of IS; i.e. how to support organizing with computational data. We contrast these dimensions with two...

  7. Conociendo Big Data

    Directory of Open Access Journals (Sweden)

    Juan José Camargo-Vega

    2014-12-01

    Full Text Available Teniendo en cuenta la importancia que ha adquirido el término Big Data, la presente investigación buscó estudiar y analizar de manera exhaustiva el estado del arte del Big Data; además, y como segundo objetivo, analizó las características, las herramientas, las tecnologías, los modelos y los estándares relacionados con Big Data, y por último buscó identificar las características más relevantes en la gestión de Big Data, para que con ello se pueda conocer todo lo concerniente al tema central de la investigación.La metodología utilizada incluyó revisar el estado del arte de Big Data y enseñar su situación actual; conocer las tecnologías de Big Data; presentar algunas de las bases de datos NoSQL, que son las que permiten procesar datos con formatos no estructurados, y mostrar los modelos de datos y las tecnologías de análisis de ellos, para terminar con algunos beneficios de Big Data.El diseño metodológico usado para la investigación fue no experimental, pues no se manipulan variables, y de tipo exploratorio, debido a que con esta investigación se empieza a conocer el ambiente del Big Data.

  8. Big Bang baryosynthesis

    International Nuclear Information System (INIS)

    Turner, M.S.; Chicago Univ., IL

    1983-01-01

    In these lectures I briefly review Big Bang baryosynthesis. In the first lecture I discuss the evidence which exists for the BAU, the failure of non-GUT symmetrical cosmologies, the qualitative picture of baryosynthesis, and numerical results of detailed baryosynthesis calculations. In the second lecture I discuss the requisite CP violation in some detail, further the statistical mechanics of baryosynthesis, possible complications to the simplest scenario, and one cosmological implication of Big Bang baryosynthesis. (orig./HSI)

  9. Minsky on "Big Government"

    Directory of Open Access Journals (Sweden)

    Daniel de Santana Vasconcelos

    2014-03-01

    Full Text Available This paper objective is to assess, in light of the main works of Minsky, his view and analysis of what he called the "Big Government" as that huge institution which, in parallels with the "Big Bank" was capable of ensuring stability in the capitalist system and regulate its inherently unstable financial system in mid-20th century. In this work, we analyze how Minsky proposes an active role for the government in a complex economic system flawed by financial instability.

  10. Big data need big theory too.

    Science.gov (United States)

    Coveney, Peter V; Dougherty, Edward R; Highfield, Roger R

    2016-11-13

    The current interest in big data, machine learning and data analytics has generated the widespread impression that such methods are capable of solving most problems without the need for conventional scientific methods of inquiry. Interest in these methods is intensifying, accelerated by the ease with which digitized data can be acquired in virtually all fields of endeavour, from science, healthcare and cybersecurity to economics, social sciences and the humanities. In multiscale modelling, machine learning appears to provide a shortcut to reveal correlations of arbitrary complexity between processes at the atomic, molecular, meso- and macroscales. Here, we point out the weaknesses of pure big data approaches with particular focus on biology and medicine, which fail to provide conceptual accounts for the processes to which they are applied. No matter their 'depth' and the sophistication of data-driven methods, such as artificial neural nets, in the end they merely fit curves to existing data. Not only do these methods invariably require far larger quantities of data than anticipated by big data aficionados in order to produce statistically reliable results, but they can also fail in circumstances beyond the range of the data used to train them because they are not designed to model the structural characteristics of the underlying system. We argue that it is vital to use theory as a guide to experimental design for maximal efficiency of data collection and to produce reliable predictive models and conceptual knowledge. Rather than continuing to fund, pursue and promote 'blind' big data projects with massive budgets, we call for more funding to be allocated to the elucidation of the multiscale and stochastic processes controlling the behaviour of complex systems, including those of life, medicine and healthcare.This article is part of the themed issue 'Multiscale modelling at the physics-chemistry-biology interface'. © 2015 The Authors.

  11. Big Data and medicine: a big deal?

    Science.gov (United States)

    Mayer-Schönberger, V; Ingelsson, E

    2018-05-01

    Big Data promises huge benefits for medical research. Looking beyond superficial increases in the amount of data collected, we identify three key areas where Big Data differs from conventional analyses of data samples: (i) data are captured more comprehensively relative to the phenomenon under study; this reduces some bias but surfaces important trade-offs, such as between data quantity and data quality; (ii) data are often analysed using machine learning tools, such as neural networks rather than conventional statistical methods resulting in systems that over time capture insights implicit in data, but remain black boxes, rarely revealing causal connections; and (iii) the purpose of the analyses of data is no longer simply answering existing questions, but hinting at novel ones and generating promising new hypotheses. As a consequence, when performed right, Big Data analyses can accelerate research. Because Big Data approaches differ so fundamentally from small data ones, research structures, processes and mindsets need to adjust. The latent value of data is being reaped through repeated reuse of data, which runs counter to existing practices not only regarding data privacy, but data management more generally. Consequently, we suggest a number of adjustments such as boards reviewing responsible data use, and incentives to facilitate comprehensive data sharing. As data's role changes to a resource of insight, we also need to acknowledge the importance of collecting and making data available as a crucial part of our research endeavours, and reassess our formal processes from career advancement to treatment approval. © 2017 The Association for the Publication of the Journal of Internal Medicine.

  12. Big data analysis new algorithms for a new society

    CERN Document Server

    Stefanowski, Jerzy

    2016-01-01

    This edited volume is devoted to Big Data Analysis from a Machine Learning standpoint as presented by some of the most eminent researchers in this area. It demonstrates that Big Data Analysis opens up new research problems which were either never considered before, or were only considered within a limited range. In addition to providing methodological discussions on the principles of mining Big Data and the difference between traditional statistical data analysis and newer computing frameworks, this book presents recently developed algorithms affecting such areas as business, financial forecasting, human mobility, the Internet of Things, information networks, bioinformatics, medical systems and life science. It explores, through a number of specific examples, how the study of Big Data Analysis has evolved and how it has started and will most likely continue to affect society. While the benefits brought upon by Big Data Analysis are underlined, the book also discusses some of the warnings that have been issued...

  13. [Utilization of Big Data in Medicine and Future Outlook].

    Science.gov (United States)

    Kinosada, Yasutomi; Uematsu, Machiko; Fujiwara, Takuya

    2016-03-01

    "Big data" is a new buzzword. The point is not to be dazzled by the volume of data, but rather to analyze it, and convert it into insights, innovations, and business value. There are also real differences between conventional analytics and big data. In this article, we show some results of big data analysis using open DPC (Diagnosis Procedure Combination) data in areas of the central part of JAPAN: Toyama, Ishikawa, Fukui, Nagano, Gifu, Aichi, Shizuoka, and Mie Prefectures. These 8 prefectures contain 51 medical administration areas called the second medical area. By applying big data analysis techniques such as k-means, hierarchical clustering, and self-organizing maps to DPC data, we can visualize the disease structure and detect similarities or variations among the 51 second medical areas. The combination of a big data analysis technique and open DPC data is a very powerful method to depict real figures on patient distribution in Japan.

  14. Commentary: Epidemiology in the era of big data.

    Science.gov (United States)

    Mooney, Stephen J; Westreich, Daniel J; El-Sayed, Abdulrahman M

    2015-05-01

    Big Data has increasingly been promoted as a revolutionary development in the future of science, including epidemiology. However, the definition and implications of Big Data for epidemiology remain unclear. We here provide a working definition of Big Data predicated on the so-called "three V's": variety, volume, and velocity. From this definition, we argue that Big Data has evolutionary and revolutionary implications for identifying and intervening on the determinants of population health. We suggest that as more sources of diverse data become publicly available, the ability to combine and refine these data to yield valid answers to epidemiologic questions will be invaluable. We conclude that while epidemiology as practiced today will continue to be practiced in the Big Data future, a component of our field's future value lies in integrating subject matter knowledge with increased technical savvy. Our training programs and our visions for future public health interventions should reflect this future.

  15. Information granularity, big data, and computational intelligence

    CERN Document Server

    Chen, Shyi-Ming

    2015-01-01

    The recent pursuits emerging in the realm of big data processing, interpretation, collection and organization have emerged in numerous sectors including business, industry, and government organizations. Data sets such as customer transactions for a mega-retailer, weather monitoring, intelligence gathering, quickly outpace the capacities of traditional techniques and tools of data analysis. The 3V (volume, variability and velocity) challenges led to the emergence of new techniques and tools in data visualization, acquisition, and serialization. Soft Computing being regarded as a plethora of technologies of fuzzy sets (or Granular Computing), neurocomputing and evolutionary optimization brings forward a number of unique features that might be instrumental to the development of concepts and algorithms to deal with big data. This carefully edited volume provides the reader with an updated, in-depth material on the emerging principles, conceptual underpinnings, algorithms and practice of Computational Intelligenc...

  16. Techniques and environments for big data analysis parallel, cloud, and grid computing

    CERN Document Server

    Dehuri, Satchidananda; Kim, Euiwhan; Wang, Gi-Name

    2016-01-01

    This volume is aiming at a wide range of readers and researchers in the area of Big Data by presenting the recent advances in the fields of Big Data Analysis, as well as the techniques and tools used to analyze it. The book includes 10 distinct chapters providing a concise introduction to Big Data Analysis and recent Techniques and Environments for Big Data Analysis. It gives insight into how the expensive fitness evaluation of evolutionary learning can play a vital role in big data analysis by adopting Parallel, Grid, and Cloud computing environments.

  17. Research in Big Data Warehousing using Hadoop

    OpenAIRE

    Abderrazak Sebaa; Fatima Chikh; Amina Nouicer; Abdelkamel Tari

    2017-01-01

    Traditional data warehouses have played a key role in decision support system until the recent past. However, the rapid growing of the data generation by the current applications requires new data warehousing systems: volume and format of collected datasets, data source variety, integration of unstructured data and powerful analytical processing. In the age of the Big Data, it is important to follow this pace and adapt the existing warehouse systems to overcome the new issues and challenges. ...

  18. Big data challenges: Impact, potential responses and research needs

    OpenAIRE

    Bachlechner, Daniel; Leimbach, Timo

    2016-01-01

    Although reports on big data success stories have been accumulating in the media, most organizations dealing with high-volume, high-velocity and high-variety information assets still face challenges. Only a thorough understanding of these challenges puts organizations into a position in which they can make an informed decision for or against big data, and, if the decision is positive, overcome the challenges smoothly. The combination of a series of interviews with leading experts from enterpr...

  19. Big Data for Infectious Disease Surveillance and Modeling

    OpenAIRE

    Bansal, Shweta; Chowell, Gerardo; Simonsen, Lone; Vespignani, Alessandro; Viboud, Cécile

    2016-01-01

    We devote a special issue of the Journal of Infectious Diseases to review the recent advances of big data in strengthening disease surveillance, monitoring medical adverse events, informing transmission models, and tracking patient sentiments and mobility. We consider a broad definition of big data for public health, one encompassing patient information gathered from high-volume electronic health records and participatory surveillance systems, as well as mining of digital traces such as socia...

  20. Simulation Experiments: Better data, not just big data

    OpenAIRE

    Sanchez, Susan M.

    2014-01-01

    Proceeding WSC '14 Proceedings of the 2014 Winter Simulation Conference Pages 805-816 Data mining tools have been around for several decades, but the term “big data” has only recently captured widespread attention. Numerous success stories have been promulgated as organizations have sifted through massive volumes of data to find interesting patterns that are, in turn, transformed into actionable information. Yet a key drawback to the big data paradigm is that it relies on obser...

  1. On the Performance Evaluation of Big Data Systems

    OpenAIRE

    Pirzadeh, Pouria

    2015-01-01

    Big Data is turning to be a key basis for the competition and growth among various businesses. The emerging need to store and process huge volumes of data has resulted in the appearance of different Big Data serving systems with fundamental differences. Big Data benchmarking is a means to assist users to pick the correct system to fulfill their applications' needs. It can also help the developers of these systems to make the correct decisions in building and extending them. While there have b...

  2. Advancements in Big Data Processing

    CERN Document Server

    Vaniachine, A; The ATLAS collaboration

    2012-01-01

    The ever-increasing volumes of scientific data present new challenges for Distributed Computing and Grid-technologies. The emerging Big Data revolution drives new discoveries in scientific fields including nanotechnology, astrophysics, high-energy physics, biology and medicine. New initiatives are transforming data-driven scientific fields by pushing Bid Data limits enabling massive data analysis in new ways. In petascale data processing scientists deal with datasets, not individual files. As a result, a task (comprised of many jobs) became a unit of petascale data processing on the Grid. Splitting of a large data processing task into jobs enabled fine-granularity checkpointing analogous to the splitting of a large file into smaller TCP/IP packets during data transfers. Transferring large data in small packets achieves reliability through automatic re-sending of the dropped TCP/IP packets. Similarly, transient job failures on the Grid can be recovered by automatic re-tries to achieve reliable Six Sigma produc...

  3. Semantic Web technologies for the big data in life sciences.

    Science.gov (United States)

    Wu, Hongyan; Yamaguchi, Atsuko

    2014-08-01

    The life sciences field is entering an era of big data with the breakthroughs of science and technology. More and more big data-related projects and activities are being performed in the world. Life sciences data generated by new technologies are continuing to grow in not only size but also variety and complexity, with great speed. To ensure that big data has a major influence in the life sciences, comprehensive data analysis across multiple data sources and even across disciplines is indispensable. The increasing volume of data and the heterogeneous, complex varieties of data are two principal issues mainly discussed in life science informatics. The ever-evolving next-generation Web, characterized as the Semantic Web, is an extension of the current Web, aiming to provide information for not only humans but also computers to semantically process large-scale data. The paper presents a survey of big data in life sciences, big data related projects and Semantic Web technologies. The paper introduces the main Semantic Web technologies and their current situation, and provides a detailed analysis of how Semantic Web technologies address the heterogeneous variety of life sciences big data. The paper helps to understand the role of Semantic Web technologies in the big data era and how they provide a promising solution for the big data in life sciences.

  4. BigDansing

    KAUST Repository

    Khayyat, Zuhair

    2015-06-02

    Data cleansing approaches have usually focused on detecting and fixing errors with little attention to scaling to big datasets. This presents a serious impediment since data cleansing often involves costly computations such as enumerating pairs of tuples, handling inequality joins, and dealing with user-defined functions. In this paper, we present BigDansing, a Big Data Cleansing system to tackle efficiency, scalability, and ease-of-use issues in data cleansing. The system can run on top of most common general purpose data processing platforms, ranging from DBMSs to MapReduce-like frameworks. A user-friendly programming interface allows users to express data quality rules both declaratively and procedurally, with no requirement of being aware of the underlying distributed platform. BigDansing takes these rules into a series of transformations that enable distributed computations and several optimizations, such as shared scans and specialized joins operators. Experimental results on both synthetic and real datasets show that BigDansing outperforms existing baseline systems up to more than two orders of magnitude without sacrificing the quality provided by the repair algorithms.

  5. Thick-Big Descriptions

    DEFF Research Database (Denmark)

    Lai, Signe Sophus

    The paper discusses the rewards and challenges of employing commercial audience measurements data – gathered by media industries for profitmaking purposes – in ethnographic research on the Internet in everyday life. It questions claims to the objectivity of big data (Anderson 2008), the assumption...... communication systems, language and behavior appear as texts, outputs, and discourses (data to be ‘found’) – big data then documents things that in earlier research required interviews and observations (data to be ‘made’) (Jensen 2014). However, web-measurement enterprises build audiences according...... to a commercial logic (boyd & Crawford 2011) and is as such directed by motives that call for specific types of sellable user data and specific segmentation strategies. In combining big data and ‘thick descriptions’ (Geertz 1973) scholars need to question how ethnographic fieldwork might map the ‘data not seen...

  6. Big data in biomedicine.

    Science.gov (United States)

    Costa, Fabricio F

    2014-04-01

    The increasing availability and growth rate of biomedical information, also known as 'big data', provides an opportunity for future personalized medicine programs that will significantly improve patient care. Recent advances in information technology (IT) applied to biomedicine are changing the landscape of privacy and personal information, with patients getting more control of their health information. Conceivably, big data analytics is already impacting health decisions and patient care; however, specific challenges need to be addressed to integrate current discoveries into medical practice. In this article, I will discuss the major breakthroughs achieved in combining omics and clinical health data in terms of their application to personalized medicine. I will also review the challenges associated with using big data in biomedicine and translational science. Copyright © 2013 Elsevier Ltd. All rights reserved.

  7. Big data bioinformatics.

    Science.gov (United States)

    Greene, Casey S; Tan, Jie; Ung, Matthew; Moore, Jason H; Cheng, Chao

    2014-12-01

    Recent technological advances allow for high throughput profiling of biological systems in a cost-efficient manner. The low cost of data generation is leading us to the "big data" era. The availability of big data provides unprecedented opportunities but also raises new challenges for data mining and analysis. In this review, we introduce key concepts in the analysis of big data, including both "machine learning" algorithms as well as "unsupervised" and "supervised" examples of each. We note packages for the R programming language that are available to perform machine learning analyses. In addition to programming based solutions, we review webservers that allow users with limited or no programming background to perform these analyses on large data compendia. © 2014 Wiley Periodicals, Inc.

  8. The big data-big model (BDBM) challenges in ecological research

    Science.gov (United States)

    Luo, Y.

    2015-12-01

    The field of ecology has become a big-data science in the past decades due to development of new sensors used in numerous studies in the ecological community. Many sensor networks have been established to collect data. For example, satellites, such as Terra and OCO-2 among others, have collected data relevant on global carbon cycle. Thousands of field manipulative experiments have been conducted to examine feedback of terrestrial carbon cycle to global changes. Networks of observations, such as FLUXNET, have measured land processes. In particular, the implementation of the National Ecological Observatory Network (NEON), which is designed to network different kinds of sensors at many locations over the nation, will generate large volumes of ecological data every day. The raw data from sensors from those networks offer an unprecedented opportunity for accelerating advances in our knowledge of ecological processes, educating teachers and students, supporting decision-making, testing ecological theory, and forecasting changes in ecosystem services. Currently, ecologists do not have the infrastructure in place to synthesize massive yet heterogeneous data into resources for decision support. It is urgent to develop an ecological forecasting system that can make the best use of multiple sources of data to assess long-term biosphere change and anticipate future states of ecosystem services at regional and continental scales. Forecasting relies on big models that describe major processes that underlie complex system dynamics. Ecological system models, despite great simplification of the real systems, are still complex in order to address real-world problems. For example, Community Land Model (CLM) incorporates thousands of processes related to energy balance, hydrology, and biogeochemistry. Integration of massive data from multiple big data sources with complex models has to tackle Big Data-Big Model (BDBM) challenges. Those challenges include interoperability of multiple

  9. Curating Big Data Made Simple: Perspectives from Scientific Communities.

    Science.gov (United States)

    Sowe, Sulayman K; Zettsu, Koji

    2014-03-01

    The digital universe is exponentially producing an unprecedented volume of data that has brought benefits as well as fundamental challenges for enterprises and scientific communities alike. This trend is inherently exciting for the development and deployment of cloud platforms to support scientific communities curating big data. The excitement stems from the fact that scientists can now access and extract value from the big data corpus, establish relationships between bits and pieces of information from many types of data, and collaborate with a diverse community of researchers from various domains. However, despite these perceived benefits, to date, little attention is focused on the people or communities who are both beneficiaries and, at the same time, producers of big data. The technical challenges posed by big data are as big as understanding the dynamics of communities working with big data, whether scientific or otherwise. Furthermore, the big data era also means that big data platforms for data-intensive research must be designed in such a way that research scientists can easily search and find data for their research, upload and download datasets for onsite/offsite use, perform computations and analysis, share their findings and research experience, and seamlessly collaborate with their colleagues. In this article, we present the architecture and design of a cloud platform that meets some of these requirements, and a big data curation model that describes how a community of earth and environmental scientists is using the platform to curate data. Motivation for developing the platform, lessons learnt in overcoming some challenges associated with supporting scientists to curate big data, and future research directions are also presented.

  10. Big Data and Biomedical Informatics: A Challenging Opportunity

    Science.gov (United States)

    2014-01-01

    Summary Big data are receiving an increasing attention in biomedicine and healthcare. It is therefore important to understand the reason why big data are assuming a crucial role for the biomedical informatics community. The capability of handling big data is becoming an enabler to carry out unprecedented research studies and to implement new models of healthcare delivery. Therefore, it is first necessary to deeply understand the four elements that constitute big data, namely Volume, Variety, Velocity, and Veracity, and their meaning in practice. Then, it is mandatory to understand where big data are present, and where they can be beneficially collected. There are research fields, such as translational bioinformatics, which need to rely on big data technologies to withstand the shock wave of data that is generated every day. Other areas, ranging from epidemiology to clinical care, can benefit from the exploitation of the large amounts of data that are nowadays available, from personal monitoring to primary care. However, building big data-enabled systems carries on relevant implications in terms of reproducibility of research studies and management of privacy and data access; proper actions should be taken to deal with these issues. An interesting consequence of the big data scenario is the availability of new software, methods, and tools, such as map-reduce, cloud computing, and concept drift machine learning algorithms, which will not only contribute to big data research, but may be beneficial in many biomedical informatics applications. The way forward with the big data opportunity will require properly applied engineering principles to design studies and applications, to avoid preconceptions or over-enthusiasms, to fully exploit the available technologies, and to improve data processing and data management regulations. PMID:24853034

  11. Big data and biomedical informatics: a challenging opportunity.

    Science.gov (United States)

    Bellazzi, R

    2014-05-22

    Big data are receiving an increasing attention in biomedicine and healthcare. It is therefore important to understand the reason why big data are assuming a crucial role for the biomedical informatics community. The capability of handling big data is becoming an enabler to carry out unprecedented research studies and to implement new models of healthcare delivery. Therefore, it is first necessary to deeply understand the four elements that constitute big data, namely Volume, Variety, Velocity, and Veracity, and their meaning in practice. Then, it is mandatory to understand where big data are present, and where they can be beneficially collected. There are research fields, such as translational bioinformatics, which need to rely on big data technologies to withstand the shock wave of data that is generated every day. Other areas, ranging from epidemiology to clinical care, can benefit from the exploitation of the large amounts of data that are nowadays available, from personal monitoring to primary care. However, building big data-enabled systems carries on relevant implications in terms of reproducibility of research studies and management of privacy and data access; proper actions should be taken to deal with these issues. An interesting consequence of the big data scenario is the availability of new software, methods, and tools, such as map-reduce, cloud computing, and concept drift machine learning algorithms, which will not only contribute to big data research, but may be beneficial in many biomedical informatics applications. The way forward with the big data opportunity will require properly applied engineering principles to design studies and applications, to avoid preconceptions or over-enthusiasms, to fully exploit the available technologies, and to improve data processing and data management regulations.

  12. Big Java late objects

    CERN Document Server

    Horstmann, Cay S

    2012-01-01

    Big Java: Late Objects is a comprehensive introduction to Java and computer programming, which focuses on the principles of programming, software engineering, and effective learning. It is designed for a two-semester first course in programming for computer science students.

  13. Big ideas: innovation policy

    OpenAIRE

    John Van Reenen

    2011-01-01

    In the last CentrePiece, John Van Reenen stressed the importance of competition and labour market flexibility for productivity growth. His latest in CEP's 'big ideas' series describes the impact of research on how policy-makers can influence innovation more directly - through tax credits for business spending on research and development.

  14. Big Data ethics

    NARCIS (Netherlands)

    Zwitter, Andrej

    2014-01-01

    The speed of development in Big Data and associated phenomena, such as social media, has surpassed the capacity of the average consumer to understand his or her actions and their knock-on effects. We are moving towards changes in how ethics has to be perceived: away from individual decisions with

  15. Big data in history

    CERN Document Server

    Manning, Patrick

    2013-01-01

    Big Data in History introduces the project to create a world-historical archive, tracing the last four centuries of historical dynamics and change. Chapters address the archive's overall plan, how to interpret the past through a global archive, the missions of gathering records, linking local data into global patterns, and exploring the results.

  16. The Big Sky inside

    Science.gov (United States)

    Adams, Earle; Ward, Tony J.; Vanek, Diana; Marra, Nancy; Hester, Carolyn; Knuth, Randy; Spangler, Todd; Jones, David; Henthorn, Melissa; Hammill, Brock; Smith, Paul; Salisbury, Rob; Reckin, Gene; Boulafentis, Johna

    2009-01-01

    The University of Montana (UM)-Missoula has implemented a problem-based program in which students perform scientific research focused on indoor air pollution. The Air Toxics Under the Big Sky program (Jones et al. 2007; Adams et al. 2008; Ward et al. 2008) provides a community-based framework for understanding the complex relationship between poor…

  17. Moving Another Big Desk.

    Science.gov (United States)

    Fawcett, Gay

    1996-01-01

    New ways of thinking about leadership require that leaders move their big desks and establish environments that encourage trust and open communication. Educational leaders must trust their colleagues to make wise choices. When teachers are treated democratically as leaders, classrooms will also become democratic learning organizations. (SM)

  18. A Big Bang Lab

    Science.gov (United States)

    Scheider, Walter

    2005-01-01

    The February 2005 issue of The Science Teacher (TST) reminded everyone that by learning how scientists study stars, students gain an understanding of how science measures things that can not be set up in lab, either because they are too big, too far away, or happened in a very distant past. The authors of "How Far are the Stars?" show how the…

  19. New 'bigs' in cosmology

    International Nuclear Information System (INIS)

    Yurov, Artyom V.; Martin-Moruno, Prado; Gonzalez-Diaz, Pedro F.

    2006-01-01

    This paper contains a detailed discussion on new cosmic solutions describing the early and late evolution of a universe that is filled with a kind of dark energy that may or may not satisfy the energy conditions. The main distinctive property of the resulting space-times is that they make to appear twice the single singular events predicted by the corresponding quintessential (phantom) models in a manner which can be made symmetric with respect to the origin of cosmic time. Thus, big bang and big rip singularity are shown to take place twice, one on the positive branch of time and the other on the negative one. We have also considered dark energy and phantom energy accretion onto black holes and wormholes in the context of these new cosmic solutions. It is seen that the space-times of these holes would then undergo swelling processes leading to big trip and big hole events taking place on distinct epochs along the evolution of the universe. In this way, the possibility is considered that the past and future be connected in a non-paradoxical manner in the universes described by means of the new symmetric solutions

  20. The Big Bang

    CERN Multimedia

    Moods, Patrick

    2006-01-01

    How did the Universe begin? The favoured theory is that everything - space, time, matter - came into existence at the same moment, around 13.7 thousand million years ago. This event was scornfully referred to as the "Big Bang" by Sir Fred Hoyle, who did not believe in it and maintained that the Universe had always existed.

  1. Identifying Dwarfs Workloads in Big Data Analytics

    OpenAIRE

    Gao, Wanling; Luo, Chunjie; Zhan, Jianfeng; Ye, Hainan; He, Xiwen; Wang, Lei; Zhu, Yuqing; Tian, Xinhui

    2015-01-01

    Big data benchmarking is particularly important and provides applicable yardsticks for evaluating booming big data systems. However, wide coverage and great complexity of big data computing impose big challenges on big data benchmarking. How can we construct a benchmark suite using a minimum set of units of computation to represent diversity of big data analytics workloads? Big data dwarfs are abstractions of extracting frequently appearing operations in big data computing. One dwarf represen...

  2. Scaling Big Data Cleansing

    KAUST Repository

    Khayyat, Zuhair

    2017-07-31

    Data cleansing approaches have usually focused on detecting and fixing errors with little attention to big data scaling. This presents a serious impediment since identify- ing and repairing dirty data often involves processing huge input datasets, handling sophisticated error discovery approaches and managing huge arbitrary errors. With large datasets, error detection becomes overly expensive and complicated especially when considering user-defined functions. Furthermore, a distinctive algorithm is de- sired to optimize inequality joins in sophisticated error discovery rather than na ̈ıvely parallelizing them. Also, when repairing large errors, their skewed distribution may obstruct effective error repairs. In this dissertation, I present solutions to overcome the above three problems in scaling data cleansing. First, I present BigDansing as a general system to tackle efficiency, scalability, and ease-of-use issues in data cleansing for Big Data. It automatically parallelizes the user’s code on top of general-purpose distributed platforms. Its programming inter- face allows users to express data quality rules independently from the requirements of parallel and distributed environments. Without sacrificing their quality, BigDans- ing also enables parallel execution of serial repair algorithms by exploiting the graph representation of discovered errors. The experimental results show that BigDansing outperforms existing baselines up to more than two orders of magnitude. Although BigDansing scales cleansing jobs, it still lacks the ability to handle sophisticated error discovery requiring inequality joins. Therefore, I developed IEJoin as an algorithm for fast inequality joins. It is based on sorted arrays and space efficient bit-arrays to reduce the problem’s search space. By comparing IEJoin against well- known optimizations, I show that it is more scalable, and several orders of magnitude faster. BigDansing depends on vertex-centric graph systems, i.e., Pregel

  3. Big Data Analytics for Prostate Radiotherapy.

    Science.gov (United States)

    Coates, James; Souhami, Luis; El Naqa, Issam

    2016-01-01

    Radiation therapy is a first-line treatment option for localized prostate cancer and radiation-induced normal tissue damage are often the main limiting factor for modern radiotherapy regimens. Conversely, under-dosing of target volumes in an attempt to spare adjacent healthy tissues limits the likelihood of achieving local, long-term control. Thus, the ability to generate personalized data-driven risk profiles for radiotherapy outcomes would provide valuable prognostic information to help guide both clinicians and patients alike. Big data applied to radiation oncology promises to deliver better understanding of outcomes by harvesting and integrating heterogeneous data types, including patient-specific clinical parameters, treatment-related dose-volume metrics, and biological risk factors. When taken together, such variables make up the basis for a multi-dimensional space (the "RadoncSpace") in which the presented modeling techniques search in order to identify significant predictors. Herein, we review outcome modeling and big data-mining techniques for both tumor control and radiotherapy-induced normal tissue effects. We apply many of the presented modeling approaches onto a cohort of hypofractionated prostate cancer patients taking into account different data types and a large heterogeneous mix of physical and biological parameters. Cross-validation techniques are also reviewed for the refinement of the proposed framework architecture and checking individual model performance. We conclude by considering advanced modeling techniques that borrow concepts from big data analytics, such as machine learning and artificial intelligence, before discussing the potential future impact of systems radiobiology approaches.

  4. Big Data and Regional Science: Opportunities, Challenges, and Directions for Future Research

    OpenAIRE

    Schintler, Laurie A.; Fischer, Manfred M.

    2018-01-01

    Recent technological, social, and economic trends and transformations are contributing to the production of what is usually referred to as Big Data. Big Data, which is typically defined by four dimensions -- Volume, Velocity, Veracity, and Variety -- changes the methods and tactics for using, analyzing, and interpreting data, requiring new approaches for data provenance, data processing, data analysis and modeling, and knowledge representation. The use and analysis of Big Data involves severa...

  5. Big Data in the Earth Observing System Data and Information System

    Science.gov (United States)

    Lynnes, Chris; Baynes, Katie; McInerney, Mark

    2016-01-01

    Approaches that are being pursued for the Earth Observing System Data and Information System (EOSDIS) data system to address the challenges of Big Data were presented to the NASA Big Data Task Force. Cloud prototypes are underway to tackle the volume challenge of Big Data. However, advances in computer hardware or cloud won't help (much) with variety. Rather, interoperability standards, conventions, and community engagement are the key to addressing variety.

  6. Leveraging cloud based big data analytics in knowledge management for enhanced decision making in organizations

    OpenAIRE

    Shorfuzzaman, Mohammad

    2017-01-01

    In recent past, big data opportunities have gained much momentum to enhance knowledge management in organizations. However, big data due to its various properties like high volume, variety, and velocity can no longer be effectively stored and analyzed with traditional data management techniques to generate values for knowledge development. Hence, new technologies and architectures are required to store and analyze this big data through advanced data analytics and in turn generate vital real-t...

  7. A Brief Review on Leading Big Data Models

    Directory of Open Access Journals (Sweden)

    Sugam Sharma

    2014-11-01

    Full Text Available Today, science is passing through an era of transformation, where the inundation of data, dubbed data deluge is influencing the decision making process. The science is driven by the data and is being termed as data science. In this internet age, the volume of the data has grown up to petabytes, and this large, complex, structured or unstructured, and heterogeneous data in the form of “Big Data” has gained significant attention. The rapid pace of data growth through various disparate sources, especially social media such as Facebook, has seriously challenged the data analytic capabilities of traditional relational databases. The velocity of the expansion of the amount of data gives rise to a complete paradigm shift in how new age data is processed. Confidence in the data engineering of the existing data processing systems is gradually fading whereas the capabilities of the new techniques for capturing, storing, visualizing, and analyzing data are evolving. In this review paper, we discuss some of the modern Big Data models that are leading contributors in the NoSQL era and claim to address Big Data challenges in reliable and efficient ways. Also, we take the potential of Big Data into consideration and try to reshape the original operationaloriented definition of “Big Science” (Furner, 2003 into a new data-driven definition and rephrase it as “The science that deals with Big Data is Big Science.”

  8. Implementing the “Big Data” Concept in Official Statistics

    Directory of Open Access Journals (Sweden)

    О. V.

    2017-02-01

    Full Text Available Big data is a huge resource that needs to be used at all levels of economic planning. The article is devoted to the study of the development of the concept of “Big Data” in the world and its impact on the transformation of statistical simulation of economic processes. Statistics at the current stage should take into account the complex system of international economic relations, which functions in the conditions of globalization and brings new forms of economic development in small open economies. Statistical science should take into account such phenomena as gig-economy, common economy, institutional factors, etc. The concept of “Big Data” and open data are analyzed, problems of implementation of “Big Data” in the official statistics are shown. The ways of implementation of “Big Data” in the official statistics of Ukraine through active use of technological opportunities of mobile operators, navigation systems, surveillance cameras, social networks, etc. are presented. The possibilities of using “Big Data” in different sectors of the economy, also on the level of companies are shown. The problems of storage of large volumes of data are highlighted. The study shows that “Big Data” is a huge resource that should be used across the Ukrainian economy.

  9. How Big Are "Martin's Big Words"? Thinking Big about the Future.

    Science.gov (United States)

    Gardner, Traci

    "Martin's Big Words: The Life of Dr. Martin Luther King, Jr." tells of King's childhood determination to use "big words" through biographical information and quotations. In this lesson, students in grades 3 to 5 explore information on Dr. King to think about his "big" words, then they write about their own…

  10. Astronomy in the Big Data Era

    Directory of Open Access Journals (Sweden)

    Yanxia Zhang

    2015-05-01

    Full Text Available The fields of Astrostatistics and Astroinformatics are vital for dealing with the big data issues now faced by astronomy. Like other disciplines in the big data era, astronomy has many V characteristics. In this paper, we list the different data mining algorithms used in astronomy, along with data mining software and tools related to astronomical applications. We present SDSS, a project often referred to by other astronomical projects, as the most successful sky survey in the history of astronomy and describe the factors influencing its success. We also discuss the success of Astrostatistics and Astroinformatics organizations and the conferences and summer schools on these issues that are held annually. All the above indicates that astronomers and scientists from other areas are ready to face the challenges and opportunities provided by massive data volume.

  11. Big Data and HPC: A Happy Marriage

    KAUST Repository

    Mehmood, Rashid

    2016-01-25

    International Data Corporation (IDC) defines Big Data technologies as “a new generation of technologies and architectures, designed to economically extract value from very large volumes of a wide variety of data produced every day, by enabling high velocity capture, discovery, and/or analysis”. High Performance Computing (HPC) most generally refers to “the practice of aggregating computing power in a way that delivers much higher performance than one could get out of a typical desktop computer or workstation in order to solve large problems in science, engineering, or business”. Big data platforms are built primarily considering the economics and capacity of the system for dealing with the 4V characteristics of data. HPC traditionally has been more focussed on the speed of digesting (computing) the data. For these reasons, the two domains (HPC and Big Data) have developed their own paradigms and technologies. However, recently, these two have grown fond of each other. HPC technologies are needed by Big Data to deal with the ever increasing Vs of data in order to forecast and extract insights from existing and new domains, faster, and with greater accuracy. Increasingly more data is being produced by scientific experiments from areas such as bioscience, physics, and climate, and therefore, HPC needs to adopt data-driven paradigms. Moreover, there are synergies between them with unimaginable potential for developing new computing paradigms, solving long-standing grand challenges, and making new explorations and discoveries. Therefore, they must get married to each other. In this talk, we will trace the HPC and big data landscapes through time including their respective technologies, paradigms and major applications areas. Subsequently, we will present the factors that are driving the convergence of the two technologies, the synergies between them, as well as the benefits of their convergence to the biosciences field. The opportunities and challenges of the

  12. A Survey of Scholarly Data: From Big Data Perspective

    DEFF Research Database (Denmark)

    Khan, Samiya; Liu, Xiufeng; Shakil, Kashish A.

    2017-01-01

    of which, this scholarly reserve is popularly referred to as big scholarly data. In order to facilitate data analytics for big scholarly data, architectures and services for the same need to be developed. The evolving nature of research problems has made them essentially interdisciplinary. As a result......, there is a growing demand for scholarly applications like collaborator discovery, expert finding and research recommendation systems, in addition to several others. This research paper investigates the current trends and identifies the existing challenges in development of a big scholarly data platform......Recently, there has been a shifting focus of organizations and governments towards digitization of academic and technical documents, adding a new facet to the concept of digital libraries. The volume, variety and velocity of this generated data, satisfies the big data definition, as a result...

  13. Education Policy Research in the Big Data Era: Methodological Frontiers, Misconceptions, and Challenges

    Science.gov (United States)

    Wang, Yinying

    2017-01-01

    Despite abundant data and increasing data availability brought by technological advances, there has been very limited education policy studies that have capitalized on big data--characterized by large volume, wide variety, and high velocity. Drawing on the recent progress of using big data in public policy and computational social science…

  14. Big Data-Survey

    Directory of Open Access Journals (Sweden)

    P.S.G. Aruna Sri

    2016-03-01

    Full Text Available Big data is the term for any gathering of information sets, so expensive and complex, that it gets to be hard to process for utilizing customary information handling applications. The difficulties incorporate investigation, catch, duration, inquiry, sharing, stockpiling, Exchange, perception, and protection infringement. To reduce spot business patterns, anticipate diseases, conflict etc., we require bigger data sets when compared with the smaller data sets. Enormous information is hard to work with utilizing most social database administration frameworks and desktop measurements and perception bundles, needing rather enormously parallel programming running on tens, hundreds, or even a large number of servers. In this paper there was an observation on Hadoop architecture, different tools used for big data and its security issues.

  15. Finding the big bang

    CERN Document Server

    Page, Lyman A; Partridge, R Bruce

    2009-01-01

    Cosmology, the study of the universe as a whole, has become a precise physical science, the foundation of which is our understanding of the cosmic microwave background radiation (CMBR) left from the big bang. The story of the discovery and exploration of the CMBR in the 1960s is recalled for the first time in this collection of 44 essays by eminent scientists who pioneered the work. Two introductory chapters put the essays in context, explaining the general ideas behind the expanding universe and fossil remnants from the early stages of the expanding universe. The last chapter describes how the confusion of ideas and measurements in the 1960s grew into the present tight network of tests that demonstrate the accuracy of the big bang theory. This book is valuable to anyone interested in how science is done, and what it has taught us about the large-scale nature of the physical universe.

  16. Big Data as Governmentality

    DEFF Research Database (Denmark)

    Flyverbom, Mikkel; Klinkby Madsen, Anders; Rasche, Andreas

    data is constituted as an aspiration to improve the data and knowledge underpinning development efforts. Based on this framework, we argue that big data’s impact on how relevant problems are governed is enabled by (1) new techniques of visualizing development issues, (2) linking aspects......This paper conceptualizes how large-scale data and algorithms condition and reshape knowledge production when addressing international development challenges. The concept of governmentality and four dimensions of an analytics of government are proposed as a theoretical framework to examine how big...... of international development agendas to algorithms that synthesize large-scale data, (3) novel ways of rationalizing knowledge claims that underlie development efforts, and (4) shifts in professional and organizational identities of those concerned with producing and processing data for development. Our discussion...

  17. Big nuclear accidents

    International Nuclear Information System (INIS)

    Marshall, W.; Billingon, D.E.; Cameron, R.F.; Curl, S.J.

    1983-09-01

    Much of the debate on the safety of nuclear power focuses on the large number of fatalities that could, in theory, be caused by extremely unlikely but just imaginable reactor accidents. This, along with the nuclear industry's inappropriate use of vocabulary during public debate, has given the general public a distorted impression of the risks of nuclear power. The paper reviews the way in which the probability and consequences of big nuclear accidents have been presented in the past and makes recommendations for the future, including the presentation of the long-term consequences of such accidents in terms of 'loss of life expectancy', 'increased chance of fatal cancer' and 'equivalent pattern of compulsory cigarette smoking'. The paper presents mathematical arguments, which show the derivation and validity of the proposed methods of presenting the consequences of imaginable big nuclear accidents. (author)

  18. Big Bounce and inhomogeneities

    International Nuclear Information System (INIS)

    Brizuela, David; Mena Marugan, Guillermo A; Pawlowski, Tomasz

    2010-01-01

    The dynamics of an inhomogeneous universe is studied with the methods of loop quantum cosmology, via a so-called hybrid quantization, as an example of the quantization of vacuum cosmological spacetimes containing gravitational waves (Gowdy spacetimes). The analysis of this model with an infinite number of degrees of freedom, performed at the effective level, shows that (i) the initial Big Bang singularity is replaced (as in the case of homogeneous cosmological models) by a Big Bounce, joining deterministically two large universes, (ii) the universe size at the bounce is at least of the same order of magnitude as that of the background homogeneous universe and (iii) for each gravitational wave mode, the difference in amplitude at very early and very late times has a vanishing statistical average when the bounce dynamics is strongly dominated by the inhomogeneities, whereas this average is positive when the dynamics is in a near-vacuum regime, so that statistically the inhomogeneities are amplified. (fast track communication)

  19. Big Data and reality

    Directory of Open Access Journals (Sweden)

    Ryan Shaw

    2015-11-01

    Full Text Available DNA sequencers, Twitter, MRIs, Facebook, particle accelerators, Google Books, radio telescopes, Tumblr: what do these things have in common? According to the evangelists of “data science,” all of these are instruments for observing reality at unprecedentedly large scales and fine granularities. This perspective ignores the social reality of these very different technological systems, ignoring how they are made, how they work, and what they mean in favor of an exclusive focus on what they generate: Big Data. But no data, big or small, can be interpreted without an understanding of the process that generated them. Statistical data science is applicable to systems that have been designed as scientific instruments, but is likely to lead to confusion when applied to systems that have not. In those cases, a historical inquiry is preferable.

  20. Really big numbers

    CERN Document Server

    Schwartz, Richard Evan

    2014-01-01

    In the American Mathematical Society's first-ever book for kids (and kids at heart), mathematician and author Richard Evan Schwartz leads math lovers of all ages on an innovative and strikingly illustrated journey through the infinite number system. By means of engaging, imaginative visuals and endearing narration, Schwartz manages the monumental task of presenting the complex concept of Big Numbers in fresh and relatable ways. The book begins with small, easily observable numbers before building up to truly gigantic ones, like a nonillion, a tredecillion, a googol, and even ones too huge for names! Any person, regardless of age, can benefit from reading this book. Readers will find themselves returning to its pages for a very long time, perpetually learning from and growing with the narrative as their knowledge deepens. Really Big Numbers is a wonderful enrichment for any math education program and is enthusiastically recommended to every teacher, parent and grandparent, student, child, or other individual i...

  1. Toward a Literature-Driven Definition of Big Data in Healthcare

    Directory of Open Access Journals (Sweden)

    Emilie Baro

    2015-01-01

    Full Text Available Objective. The aim of this study was to provide a definition of big data in healthcare. Methods. A systematic search of PubMed literature published until May 9, 2014, was conducted. We noted the number of statistical individuals (n and the number of variables (p for all papers describing a dataset. These papers were classified into fields of study. Characteristics attributed to big data by authors were also considered. Based on this analysis, a definition of big data was proposed. Results. A total of 196 papers were included. Big data can be defined as datasets with Log⁡(n*p≥7. Properties of big data are its great variety and high velocity. Big data raises challenges on veracity, on all aspects of the workflow, on extracting meaningful information, and on sharing information. Big data requires new computational methods that optimize data management. Related concepts are data reuse, false knowledge discovery, and privacy issues. Conclusion. Big data is defined by volume. Big data should not be confused with data reuse: data can be big without being reused for another purpose, for example, in omics. Inversely, data can be reused without being necessarily big, for example, secondary use of Electronic Medical Records (EMR data.

  2. Toward a Literature-Driven Definition of Big Data in Healthcare

    Science.gov (United States)

    Baro, Emilie; Degoul, Samuel; Beuscart, Régis; Chazard, Emmanuel

    2015-01-01

    Objective. The aim of this study was to provide a definition of big data in healthcare. Methods. A systematic search of PubMed literature published until May 9, 2014, was conducted. We noted the number of statistical individuals (n) and the number of variables (p) for all papers describing a dataset. These papers were classified into fields of study. Characteristics attributed to big data by authors were also considered. Based on this analysis, a definition of big data was proposed. Results. A total of 196 papers were included. Big data can be defined as datasets with Log⁡(n∗p) ≥ 7. Properties of big data are its great variety and high velocity. Big data raises challenges on veracity, on all aspects of the workflow, on extracting meaningful information, and on sharing information. Big data requires new computational methods that optimize data management. Related concepts are data reuse, false knowledge discovery, and privacy issues. Conclusion. Big data is defined by volume. Big data should not be confused with data reuse: data can be big without being reused for another purpose, for example, in omics. Inversely, data can be reused without being necessarily big, for example, secondary use of Electronic Medical Records (EMR) data. PMID:26137488

  3. Toward a Literature-Driven Definition of Big Data in Healthcare.

    Science.gov (United States)

    Baro, Emilie; Degoul, Samuel; Beuscart, Régis; Chazard, Emmanuel

    2015-01-01

    The aim of this study was to provide a definition of big data in healthcare. A systematic search of PubMed literature published until May 9, 2014, was conducted. We noted the number of statistical individuals (n) and the number of variables (p) for all papers describing a dataset. These papers were classified into fields of study. Characteristics attributed to big data by authors were also considered. Based on this analysis, a definition of big data was proposed. A total of 196 papers were included. Big data can be defined as datasets with Log(n∗p) ≥ 7. Properties of big data are its great variety and high velocity. Big data raises challenges on veracity, on all aspects of the workflow, on extracting meaningful information, and on sharing information. Big data requires new computational methods that optimize data management. Related concepts are data reuse, false knowledge discovery, and privacy issues. Big data is defined by volume. Big data should not be confused with data reuse: data can be big without being reused for another purpose, for example, in omics. Inversely, data can be reused without being necessarily big, for example, secondary use of Electronic Medical Records (EMR) data.

  4. Big Bang Circus

    Science.gov (United States)

    Ambrosini, C.

    2011-06-01

    Big Bang Circus is an opera I composed in 2001 and which was premiered at the Venice Biennale Contemporary Music Festival in 2002. A chamber group, four singers and a ringmaster stage the story of the Universe confronting and interweaving two threads: how early man imagined it and how scientists described it. Surprisingly enough fancy, myths and scientific explanations often end up using the same images, metaphors and sometimes even words: a strong tension, a drumskin starting to vibrate, a shout…

  5. Big Bang 5

    CERN Document Server

    Apolin, Martin

    2007-01-01

    Physik soll verständlich sein und Spaß machen! Deshalb beginnt jedes Kapitel in Big Bang mit einem motivierenden Überblick und Fragestellungen und geht dann von den Grundlagen zu den Anwendungen, vom Einfachen zum Komplizierten. Dabei bleibt die Sprache einfach, alltagsorientiert und belletristisch. Der Band 5 RG behandelt die Grundlagen (Maßsystem, Größenordnungen) und die Mechanik (Translation, Rotation, Kraft, Erhaltungssätze).

  6. Big Bang 8

    CERN Document Server

    Apolin, Martin

    2008-01-01

    Physik soll verständlich sein und Spaß machen! Deshalb beginnt jedes Kapitel in Big Bang mit einem motivierenden Überblick und Fragestellungen und geht dann von den Grundlagen zu den Anwendungen, vom Einfachen zum Komplizierten. Dabei bleibt die Sprache einfach, alltagsorientiert und belletristisch. Band 8 vermittelt auf verständliche Weise Relativitätstheorie, Kern- und Teilchenphysik (und deren Anwendungen in der Kosmologie und Astrophysik), Nanotechnologie sowie Bionik.

  7. Big Bang 6

    CERN Document Server

    Apolin, Martin

    2008-01-01

    Physik soll verständlich sein und Spaß machen! Deshalb beginnt jedes Kapitel in Big Bang mit einem motivierenden Überblick und Fragestellungen und geht dann von den Grundlagen zu den Anwendungen, vom Einfachen zum Komplizierten. Dabei bleibt die Sprache einfach, alltagsorientiert und belletristisch. Der Band 6 RG behandelt die Gravitation, Schwingungen und Wellen, Thermodynamik und eine Einführung in die Elektrizität anhand von Alltagsbeispielen und Querverbindungen zu anderen Disziplinen.

  8. Big Bang 7

    CERN Document Server

    Apolin, Martin

    2008-01-01

    Physik soll verständlich sein und Spaß machen! Deshalb beginnt jedes Kapitel in Big Bang mit einem motivierenden Überblick und Fragestellungen und geht dann von den Grundlagen zu den Anwendungen, vom Einfachen zum Komplizierten. Dabei bleibt die Sprache einfach, alltagsorientiert und belletristisch. In Band 7 werden neben einer Einführung auch viele aktuelle Aspekte von Quantenmechanik (z. Beamen) und Elektrodynamik (zB Elektrosmog), sowie die Klimaproblematik und die Chaostheorie behandelt.

  9. Big Bang Darkleosynthesis

    OpenAIRE

    Krnjaic, Gordan; Sigurdson, Kris

    2014-01-01

    In a popular class of models, dark matter comprises an asymmetric population of composite particles with short range interactions arising from a confined nonabelian gauge group. We show that coupling this sector to a well-motivated light mediator particle yields efficient darkleosynthesis , a dark-sector version of big-bang nucleosynthesis (BBN), in generic regions of parameter space. Dark matter self-interaction bounds typically require the confinement scale to be above ΛQCD , which generica...

  10. Big³. Editorial.

    Science.gov (United States)

    Lehmann, C U; Séroussi, B; Jaulent, M-C

    2014-05-22

    To provide an editorial introduction into the 2014 IMIA Yearbook of Medical Informatics with an overview of the content, the new publishing scheme, and upcoming 25th anniversary. A brief overview of the 2014 special topic, Big Data - Smart Health Strategies, and an outline of the novel publishing model is provided in conjunction with a call for proposals to celebrate the 25th anniversary of the Yearbook. 'Big Data' has become the latest buzzword in informatics and promise new approaches and interventions that can improve health, well-being, and quality of life. This edition of the Yearbook acknowledges the fact that we just started to explore the opportunities that 'Big Data' will bring. However, it will become apparent to the reader that its pervasive nature has invaded all aspects of biomedical informatics - some to a higher degree than others. It was our goal to provide a comprehensive view at the state of 'Big Data' today, explore its strengths and weaknesses, as well as its risks, discuss emerging trends, tools, and applications, and stimulate the development of the field through the aggregation of excellent survey papers and working group contributions to the topic. For the first time in history will the IMIA Yearbook be published in an open access online format allowing a broader readership especially in resource poor countries. For the first time, thanks to the online format, will the IMIA Yearbook be published twice in the year, with two different tracks of papers. We anticipate that the important role of the IMIA yearbook will further increase with these changes just in time for its 25th anniversary in 2016.

  11. Recent big flare

    International Nuclear Information System (INIS)

    Moriyama, Fumio; Miyazawa, Masahide; Yamaguchi, Yoshisuke

    1978-01-01

    The features of three big solar flares observed at Tokyo Observatory are described in this paper. The active region, McMath 14943, caused a big flare on September 16, 1977. The flare appeared on both sides of a long dark line which runs along the boundary of the magnetic field. Two-ribbon structure was seen. The electron density of the flare observed at Norikura Corona Observatory was 3 x 10 12 /cc. Several arc lines which connect both bright regions of different magnetic polarity were seen in H-α monochrome image. The active region, McMath 15056, caused a big flare on December 10, 1977. At the beginning, several bright spots were observed in the region between two main solar spots. Then, the area and the brightness increased, and the bright spots became two ribbon-shaped bands. A solar flare was observed on April 8, 1978. At first, several bright spots were seen around the solar spot in the active region, McMath 15221. Then, these bright spots developed to a large bright region. On both sides of a dark line along the magnetic neutral line, bright regions were generated. These developed to a two-ribbon flare. The time required for growth was more than one hour. A bright arc which connects two ribbons was seen, and this arc may be a loop prominence system. (Kato, T.)

  12. Big Data Technologies

    Science.gov (United States)

    Bellazzi, Riccardo; Dagliati, Arianna; Sacchi, Lucia; Segagni, Daniele

    2015-01-01

    The so-called big data revolution provides substantial opportunities to diabetes management. At least 3 important directions are currently of great interest. First, the integration of different sources of information, from primary and secondary care to administrative information, may allow depicting a novel view of patient’s care processes and of single patient’s behaviors, taking into account the multifaceted nature of chronic care. Second, the availability of novel diabetes technologies, able to gather large amounts of real-time data, requires the implementation of distributed platforms for data analysis and decision support. Finally, the inclusion of geographical and environmental information into such complex IT systems may further increase the capability of interpreting the data gathered and extract new knowledge from them. This article reviews the main concepts and definitions related to big data, it presents some efforts in health care, and discusses the potential role of big data in diabetes care. Finally, as an example, it describes the research efforts carried on in the MOSAIC project, funded by the European Commission. PMID:25910540

  13. The Shadow of Big Data: Data-Citizenship and Exclusion

    DEFF Research Database (Denmark)

    Rossi, Luca; Hjelholt, Morten; Neumayer, Christina

    2016-01-01

    The shadow of Big Data: data-citizenship and exclusion Big data are understood as being able to provide insights on human behaviour at an individual as well as at an aggregated societal level (Manyka et al. 2011). These insights are expected to be more detailed and precise than anything before...... thanks to the large volume of digital data and to the unobstrusive nature of the data collection (Fishleigh 2014). Within this perspective, these two dimensions (volume and unobstrusiveness) define contemporary big data techniques as a socio-technical offering to society, a live representation of itself...... this process "data-citizenship" emerges. Data-citizenship assumes that citizens will be visible to the state through the data they produce. On a general level data-citizenship shifts citizenship from an intrinsic status of a group of people to a status achieved through action. This approach assumes equal...

  14. Big bang and big crunch in matrix string theory

    OpenAIRE

    Bedford, J; Papageorgakis, C; Rodríguez-Gómez, D; Ward, J

    2007-01-01

    Following the holographic description of linear dilaton null Cosmologies with a Big Bang in terms of Matrix String Theory put forward by Craps, Sethi and Verlinde, we propose an extended background describing a Universe including both Big Bang and Big Crunch singularities. This belongs to a class of exact string backgrounds and is perturbative in the string coupling far away from the singularities, both of which can be resolved using Matrix String Theory. We provide a simple theory capable of...

  15. Big Data is a powerful tool for environmental improvements in the construction business

    Science.gov (United States)

    Konikov, Aleksandr; Konikov, Gregory

    2017-10-01

    The work investigates the possibility of applying the Big Data method as a tool to implement environmental improvements in the construction business. The method is recognized as effective in analyzing big volumes of heterogeneous data. It is noted that all preconditions exist for this method to be successfully used for resolution of environmental issues in the construction business. It is proven that the principal Big Data techniques (cluster analysis, crowd sourcing, data mixing and integration) can be applied in the sphere in question. It is concluded that Big Data is a truly powerful tool to implement environmental improvements in the construction business.

  16. NASA EOSDIS Evolution in the BigData Era

    Science.gov (United States)

    Lynnes, Christopher

    2015-01-01

    NASA's EOSDIS system faces several challenges in the Big Data Era. Although volumes are large (but not unmanageably so), the variety of different data collections is daunting. That variety also brings with it a large and diverse user community. One key evolution EOSDIS is working toward is to enable more science analysis to be performed close to the data.

  17. Technology for Mining the Big Data of MOOCs

    Science.gov (United States)

    O'Reilly, Una-May; Veeramachaneni, Kalyan

    2014-01-01

    Because MOOCs bring big data to the forefront, they confront learning science with technology challenges. We describe an agenda for developing technology that enables MOOC analytics. Such an agenda needs to efficiently address the detailed, low level, high volume nature of MOOC data. It also needs to help exploit the data's capacity to reveal, in…

  18. Disaggregating asthma: Big investigation versus big data.

    Science.gov (United States)

    Belgrave, Danielle; Henderson, John; Simpson, Angela; Buchan, Iain; Bishop, Christopher; Custovic, Adnan

    2017-02-01

    We are facing a major challenge in bridging the gap between identifying subtypes of asthma to understand causal mechanisms and translating this knowledge into personalized prevention and management strategies. In recent years, "big data" has been sold as a panacea for generating hypotheses and driving new frontiers of health care; the idea that the data must and will speak for themselves is fast becoming a new dogma. One of the dangers of ready accessibility of health care data and computational tools for data analysis is that the process of data mining can become uncoupled from the scientific process of clinical interpretation, understanding the provenance of the data, and external validation. Although advances in computational methods can be valuable for using unexpected structure in data to generate hypotheses, there remains a need for testing hypotheses and interpreting results with scientific rigor. We argue for combining data- and hypothesis-driven methods in a careful synergy, and the importance of carefully characterized birth and patient cohorts with genetic, phenotypic, biological, and molecular data in this process cannot be overemphasized. The main challenge on the road ahead is to harness bigger health care data in ways that produce meaningful clinical interpretation and to translate this into better diagnoses and properly personalized prevention and treatment plans. There is a pressing need for cross-disciplinary research with an integrative approach to data science, whereby basic scientists, clinicians, data analysts, and epidemiologists work together to understand the heterogeneity of asthma. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

  19. A Knowledge-Driven Geospatially Enabled Framework for Geological Big Data

    OpenAIRE

    Liang Wu; Lei Xue; Chaoling Li; Xia Lv; Zhanlong Chen; Baode Jiang; Mingqiang Guo; Zhong Xie

    2017-01-01

    Geologic survey procedures accumulate large volumes of structured and unstructured data. Fully exploiting the knowledge and information that are included in geological big data and improving the accessibility of large volumes of data are important endeavors. In this paper, which is based on the architecture of the geological survey information cloud-computing platform (GSICCP) and big-data-related technologies, we split geologic unstructured data into fragments and extract multi-dimensional f...

  20. Big data and educational research

    OpenAIRE

    Beneito-Montagut, Roser

    2017-01-01

    Big data and data analytics offer the promise to enhance teaching and learning, improve educational research and progress education governance. This chapter aims to contribute to the conceptual and methodological understanding of big data and analytics within educational research. It describes the opportunities and challenges that big data and analytics bring to education as well as critically explore the perils of applying a data driven approach to education. Despite the claimed value of the...

  1. The trashing of Big Green

    International Nuclear Information System (INIS)

    Felten, E.

    1990-01-01

    The Big Green initiative on California's ballot lost by a margin of 2-to-1. Green measures lost in five other states, shocking ecology-minded groups. According to the postmortem by environmentalists, Big Green was a victim of poor timing and big spending by the opposition. Now its supporters plan to break up the bill and try to pass some provisions in the Legislature

  2. The Big Data Tools Impact on Development of Simulation-Concerned Academic Disciplines

    Directory of Open Access Journals (Sweden)

    A. A. Sukhobokov

    2015-01-01

    Full Text Available The article gives a definition of Big Data on the basis of 5V (Volume, Variety, Velocity, Veracity, Value as well as shows examples of tasks that require using Big Data tools in a diversity of areas, namely: health, education, financial services, industry, agriculture, logistics, retail, information technology, telecommunications and others. An overview of Big Data tools is delivered, including open source products, IBM Bluemix and SAP HANA platforms. Examples of architecture of corporate data processing and management systems using Big Data tools are shown for big Internet companies and for enterprises in traditional industries. Within the overview, a classification of Big Data tools is proposed that fills gaps of previously developed similar classifications. The new classification contains 19 classes and allows embracing several hundreds of existing and emerging products.The uprise and use of Big Data tools, in addition to solving practical problems, affects the development of scientific disciplines concerning the simulation of technical, natural or socioeconomic systems and the solution of practical problems based on developed models. New schools arise in these disciplines. These new schools decide peculiar to each discipline tasks, but for systems with a much bigger number of internal elements and connections between them. Characteristics of the problems to be solved under new schools, not always meet the criteria for Big Data. It is suggested to identify the Big Data as a part of the theory of sorting and searching algorithms. In other disciplines the new schools are called by analogy with Big Data: Big Calculation in numerical methods, Big Simulation in imitational modeling, Big Management in the management of socio-economic systems, Big Optimal Control in the optimal control theory. The paper shows examples of tasks and methods to be developed within new schools. The educed tendency is not limited to the considered disciplines: there are

  3. Statistical methods and computing for big data

    Science.gov (United States)

    Wang, Chun; Chen, Ming-Hui; Schifano, Elizabeth; Wu, Jing

    2016-01-01

    Big data are data on a massive scale in terms of volume, intensity, and complexity that exceed the capacity of standard analytic tools. They present opportunities as well as challenges to statisticians. The role of computational statisticians in scientific discovery from big data analyses has been under-recognized even by peer statisticians. This article summarizes recent methodological and software developments in statistics that address the big data challenges. Methodologies are grouped into three classes: subsampling-based, divide and conquer, and online updating for stream data. As a new contribution, the online updating approach is extended to variable selection with commonly used criteria, and their performances are assessed in a simulation study with stream data. Software packages are summarized with focuses on the open source R and R packages, covering recent tools that help break the barriers of computer memory and computing power. Some of the tools are illustrated in a case study with a logistic regression for the chance of airline delay. PMID:27695593

  4. Big data for space situation awareness

    Science.gov (United States)

    Blasch, Erik; Pugh, Mark; Sheaff, Carolyn; Raquepas, Joe; Rocci, Peter

    2017-05-01

    Recent advances in big data (BD) have focused research on the volume, velocity, veracity, and variety of data. These developments enable new opportunities in information management, visualization, machine learning, and information fusion that have potential implications for space situational awareness (SSA). In this paper, we explore some of these BD trends as applicable for SSA towards enhancing the space operating picture. The BD developments could increase in measures of performance and measures of effectiveness for future management of the space environment. The global SSA influences include resident space object (RSO) tracking and characterization, cyber protection, remote sensing, and information management. The local satellite awareness can benefit from space weather, health monitoring, and spectrum management for situation space understanding. One area in big data of importance to SSA is value - getting the correct data/information at the right time, which corresponds to SSA visualization for the operator. A SSA big data example is presented supporting disaster relief for space situation awareness, assessment, and understanding.

  5. Statistical methods and computing for big data.

    Science.gov (United States)

    Wang, Chun; Chen, Ming-Hui; Schifano, Elizabeth; Wu, Jing; Yan, Jun

    2016-01-01

    Big data are data on a massive scale in terms of volume, intensity, and complexity that exceed the capacity of standard analytic tools. They present opportunities as well as challenges to statisticians. The role of computational statisticians in scientific discovery from big data analyses has been under-recognized even by peer statisticians. This article summarizes recent methodological and software developments in statistics that address the big data challenges. Methodologies are grouped into three classes: subsampling-based, divide and conquer, and online updating for stream data. As a new contribution, the online updating approach is extended to variable selection with commonly used criteria, and their performances are assessed in a simulation study with stream data. Software packages are summarized with focuses on the open source R and R packages, covering recent tools that help break the barriers of computer memory and computing power. Some of the tools are illustrated in a case study with a logistic regression for the chance of airline delay.

  6. Big data in fashion industry

    Science.gov (United States)

    Jain, S.; Bruniaux, J.; Zeng, X.; Bruniaux, P.

    2017-10-01

    Significant work has been done in the field of big data in last decade. The concept of big data includes analysing voluminous data to extract valuable information. In the fashion world, big data is increasingly playing a part in trend forecasting, analysing consumer behaviour, preference and emotions. The purpose of this paper is to introduce the term fashion data and why it can be considered as big data. It also gives a broad classification of the types of fashion data and briefly defines them. Also, the methodology and working of a system that will use this data is briefly described.

  7. Big Data Analytics An Overview

    Directory of Open Access Journals (Sweden)

    Jayshree Dwivedi

    2015-08-01

    Full Text Available Big data is a data beyond the storage capacity and beyond the processing power is called big data. Big data term is used for data sets its so large or complex that traditional data it involves data sets with sizes. Big data size is a constantly moving target year by year ranging from a few dozen terabytes to many petabytes of data means like social networking sites the amount of data produced by people is growing rapidly every year. Big data is not only a data rather it become a complete subject which includes various tools techniques and framework. It defines the epidemic possibility and evolvement of data both structured and unstructured. Big data is a set of techniques and technologies that require new forms of assimilate to uncover large hidden values from large datasets that are diverse complex and of a massive scale. It is difficult to work with using most relational database management systems and desktop statistics and visualization packages exacting preferably massively parallel software running on tens hundreds or even thousands of servers. Big data environment is used to grab organize and resolve the various types of data. In this paper we describe applications problems and tools of big data and gives overview of big data.

  8. Was there a big bang

    International Nuclear Information System (INIS)

    Narlikar, J.

    1981-01-01

    In discussing the viability of the big-bang model of the Universe relative evidence is examined including the discrepancies in the age of the big-bang Universe, the red shifts of quasars, the microwave background radiation, general theory of relativity aspects such as the change of the gravitational constant with time, and quantum theory considerations. It is felt that the arguments considered show that the big-bang picture is not as soundly established, either theoretically or observationally, as it is usually claimed to be, that the cosmological problem is still wide open and alternatives to the standard big-bang picture should be seriously investigated. (U.K.)

  9. How Big is Earth?

    Science.gov (United States)

    Thurber, Bonnie B.

    2015-08-01

    How Big is Earth celebrates the Year of Light. Using only the sunlight striking the Earth and a wooden dowel, students meet each other and then measure the circumference of the earth. Eratosthenes did it over 2,000 years ago. In Cosmos, Carl Sagan shared the process by which Eratosthenes measured the angle of the shadow cast at local noon when sunlight strikes a stick positioned perpendicular to the ground. By comparing his measurement to another made a distance away, Eratosthenes was able to calculate the circumference of the earth. How Big is Earth provides an online learning environment where students do science the same way Eratosthenes did. A notable project in which this was done was The Eratosthenes Project, conducted in 2005 as part of the World Year of Physics; in fact, we will be drawing on the teacher's guide developed by that project.How Big Is Earth? expands on the Eratosthenes project by providing an online learning environment provided by the iCollaboratory, www.icollaboratory.org, where teachers and students from Sweden, China, Nepal, Russia, Morocco, and the United States collaborate, share data, and reflect on their learning of science and astronomy. They are sharing their information and discussing their ideas/brainstorming the solutions in a discussion forum. There is an ongoing database of student measurements and another database to collect data on both teacher and student learning from surveys, discussions, and self-reflection done online.We will share our research about the kinds of learning that takes place only in global collaborations.The entrance address for the iCollaboratory is http://www.icollaboratory.org.

  10. Privacy and Big Data

    CERN Document Server

    Craig, Terence

    2011-01-01

    Much of what constitutes Big Data is information about us. Through our online activities, we leave an easy-to-follow trail of digital footprints that reveal who we are, what we buy, where we go, and much more. This eye-opening book explores the raging privacy debate over the use of personal data, with one undeniable conclusion: once data's been collected, we have absolutely no control over who uses it or how it is used. Personal data is the hottest commodity on the market today-truly more valuable than gold. We are the asset that every company, industry, non-profit, and government wants. Pri

  11. Big data naturally rescaled

    International Nuclear Information System (INIS)

    Stoop, Ruedi; Kanders, Karlis; Lorimer, Tom; Held, Jenny; Albert, Carlo

    2016-01-01

    We propose that a handle could be put on big data by looking at the systems that actually generate the data, rather than the data itself, realizing that there may be only few generic processes involved in this, each one imprinting its very specific structures in the space of systems, the traces of which translate into feature space. From this, we propose a practical computational clustering approach, optimized for coping with such data, inspired by how the human cortex is known to approach the problem.

  12. A Matrix Big Bang

    OpenAIRE

    Craps, Ben; Sethi, Savdeep; Verlinde, Erik

    2005-01-01

    The light-like linear dilaton background represents a particularly simple time-dependent 1/2 BPS solution of critical type IIA superstring theory in ten dimensions. Its lift to M-theory, as well as its Einstein frame metric, are singular in the sense that the geometry is geodesically incomplete and the Riemann tensor diverges along a light-like subspace of codimension one. We study this background as a model for a big bang type singularity in string theory/M-theory. We construct the dual Matr...

  13. [Big data and their perspectives in radiation therapy].

    Science.gov (United States)

    Guihard, Sébastien; Thariat, Juliette; Clavier, Jean-Baptiste

    2017-02-01

    The concept of big data indicates a change of scale in the use of data and data aggregation into large databases through improved computer technology. One of the current challenges in the creation of big data in the context of radiation therapy is the transformation of routine care items into dark data, i.e. data not yet collected, and the fusion of databases collecting different types of information (dose-volume histograms and toxicity data for example). Processes and infrastructures devoted to big data collection should not impact negatively on the doctor-patient relationship, the general process of care or the quality of the data collected. The use of big data requires a collective effort of physicians, physicists, software manufacturers and health authorities to create, organize and exploit big data in radiotherapy and, beyond, oncology. Big data involve a new culture to build an appropriate infrastructure legally and ethically. Processes and issues are discussed in this article. Copyright © 2016 Société Française du Cancer. Published by Elsevier Masson SAS. All rights reserved.

  14. Modeling and Analysis in Marine Big Data: Advances and Challenges

    Directory of Open Access Journals (Sweden)

    Dongmei Huang

    2015-01-01

    Full Text Available It is aware that big data has gathered tremendous attentions from academic research institutes, governments, and enterprises in all aspects of information sciences. With the development of diversity of marine data acquisition techniques, marine data grow exponentially in last decade, which forms marine big data. As an innovation, marine big data is a double-edged sword. On the one hand, there are many potential and highly useful values hidden in the huge volume of marine data, which is widely used in marine-related fields, such as tsunami and red-tide warning, prevention, and forecasting, disaster inversion, and visualization modeling after disasters. There is no doubt that the future competitions in marine sciences and technologies will surely converge into the marine data explorations. On the other hand, marine big data also brings about many new challenges in data management, such as the difficulties in data capture, storage, analysis, and applications, as well as data quality control and data security. To highlight theoretical methodologies and practical applications of marine big data, this paper illustrates a broad view about marine big data and its management, makes a survey on key methods and models, introduces an engineering instance that demonstrates the management architecture, and discusses the existing challenges.

  15. BIG Data - BIG Gains? Understanding the Link Between Big Data Analytics and Innovation

    OpenAIRE

    Niebel, Thomas; Rasel, Fabienne; Viete, Steffen

    2017-01-01

    This paper analyzes the relationship between firms’ use of big data analytics and their innovative performance for product innovations. Since big data technologies provide new data information practices, they create new decision-making possibilities, which firms can use to realize innovations. Applying German firm-level data we find suggestive evidence that big data analytics matters for the likelihood of becoming a product innovator as well as the market success of the firms’ product innovat...

  16. BIG data - BIG gains? Empirical evidence on the link between big data analytics and innovation

    OpenAIRE

    Niebel, Thomas; Rasel, Fabienne; Viete, Steffen

    2017-01-01

    This paper analyzes the relationship between firms’ use of big data analytics and their innovative performance in terms of product innovations. Since big data technologies provide new data information practices, they create novel decision-making possibilities, which are widely believed to support firms’ innovation process. Applying German firm-level data within a knowledge production function framework we find suggestive evidence that big data analytics is a relevant determinant for the likel...

  17. Big bang nucleosynthesis

    International Nuclear Information System (INIS)

    Fields, Brian D.; Olive, Keith A.

    2006-01-01

    We present an overview of the standard model of big bang nucleosynthesis (BBN), which describes the production of the light elements in the early universe. The theoretical prediction for the abundances of D, 3 He, 4 He, and 7 Li is discussed. We emphasize the role of key nuclear reactions and the methods by which experimental cross section uncertainties are propagated into uncertainties in the predicted abundances. The observational determination of the light nuclides is also discussed. Particular attention is given to the comparison between the predicted and observed abundances, which yields a measurement of the cosmic baryon content. The spectrum of anisotropies in the cosmic microwave background (CMB) now independently measures the baryon density to high precision; we show how the CMB data test BBN, and find that the CMB and the D and 4 He observations paint a consistent picture. This concordance stands as a major success of the hot big bang. On the other hand, 7 Li remains discrepant with the CMB-preferred baryon density; possible explanations are reviewed. Finally, moving beyond the standard model, primordial nucleosynthesis constraints on early universe and particle physics are also briefly discussed

  18. Was the big bang hot

    International Nuclear Information System (INIS)

    Wright, E.L.

    1983-01-01

    The author considers experiments to confirm the substantial deviations from a Planck curve in the Woody and Richards spectrum of the microwave background, and search for conducting needles in our galaxy. Spectral deviations and needle-shaped grains are expected for a cold Big Bang, but are not required by a hot Big Bang. (Auth.)

  19. Ethische aspecten van big data

    NARCIS (Netherlands)

    N. (Niek) van Antwerpen; Klaas Jan Mollema

    2017-01-01

    Big data heeft niet alleen geleid tot uitdagende technische vraagstukken, ook gaat het gepaard met allerlei nieuwe ethische en morele kwesties. Om verantwoord met big data om te gaan, moet ook over deze kwesties worden nagedacht. Want slecht datagebruik kan nadelige gevolgen hebben voor

  20. Fremtidens landbrug bliver big business

    DEFF Research Database (Denmark)

    Hansen, Henning Otte

    2016-01-01

    Landbrugets omverdensforhold og konkurrencevilkår ændres, og det vil nødvendiggøre en udvikling i retning af “big business“, hvor landbrugene bliver endnu større, mere industrialiserede og koncentrerede. Big business bliver en dominerende udvikling i dansk landbrug - men ikke den eneste...

  1. Human factors in Big Data

    NARCIS (Netherlands)

    Boer, J. de

    2016-01-01

    Since 2014 I am involved in various (research) projects that try to make the hype around Big Data more concrete and tangible for the industry and government. Big Data is about multiple sources of (real-time) data that can be analysed, transformed to information and be used to make 'smart' decisions.

  2. Passport to the Big Bang

    CERN Multimedia

    De Melis, Cinzia

    2013-01-01

    Le 2 juin 2013, le CERN inaugure le projet Passeport Big Bang lors d'un grand événement public. Affiche et programme. On 2 June 2013 CERN launches a scientific tourist trail through the Pays de Gex and the Canton of Geneva known as the Passport to the Big Bang. Poster and Programme.

  3. Big Data: Evaluating business value and firm performance

    OpenAIRE

    Vitari , Claudio; Raguseo , Elisabetta

    2016-01-01

    This report is an output of a research project co-financed by Grenoble Ecole de Management and Auvergne-Rhône-Alpes French region. This study was conducted with the aim of understanding how Information Technology (IT) provides new opportunities to firms, specifically focusing on the role of Big Data in creating value for the companies. Gartner defines Big Data as “high volume, velocity and/or variety information assets that demand cost-effective, innovative forms of information processing tha...

  4. Applications of Big Data in Education

    OpenAIRE

    Faisal Kalota

    2015-01-01

    Big Data and analytics have gained a huge momentum in recent years. Big Data feeds into the field of Learning Analytics (LA) that may allow academic institutions to better understand the learners' needs and proactively address them. Hence, it is important to have an understanding of Big Data and its applications. The purpose of this descriptive paper is to provide an overview of Big Data, the technologies used in Big Data, and some of the applications of Big Data in educa...

  5. The big data telescope

    International Nuclear Information System (INIS)

    Finkel, Elizabeth

    2017-01-01

    On a flat, red mulga plain in the outback of Western Australia, preparations are under way to build the most audacious telescope astronomers have ever dreamed of - the Square Kilometre Array (SKA). Next-generation telescopes usually aim to double the performance of their predecessors. The Australian arm of SKA will deliver a 168-fold leap on the best technology available today, to show us the universe as never before. It will tune into signals emitted just a million years after the Big Bang, when the universe was a sea of hydrogen gas, slowly percolating with the first galaxies. Their starlight illuminated the fledgling universe in what is referred to as the “cosmic dawn”.

  6. The Big Optical Array

    International Nuclear Information System (INIS)

    Mozurkewich, D.; Johnston, K.J.; Simon, R.S.

    1990-01-01

    This paper describes the design and the capabilities of the Naval Research Laboratory Big Optical Array (BOA), an interferometric optical array for high-resolution imaging of stars, stellar systems, and other celestial objects. There are four important differences between the BOA design and the design of Mark III Optical Interferometer on Mount Wilson (California). These include a long passive delay line which will be used in BOA to do most of the delay compensation, so that the fast delay line will have a very short travel; the beam combination in BOA will be done in triplets, to allow measurement of closure phase; the same light will be used for both star and fringe tracking; and the fringe tracker will use several wavelength channels

  7. Big nuclear accidents

    International Nuclear Information System (INIS)

    Marshall, W.

    1983-01-01

    Much of the debate on the safety of nuclear power focuses on the large number of fatalities that could, in theory, be caused by extremely unlikely but imaginable reactor accidents. This, along with the nuclear industry's inappropriate use of vocabulary during public debate, has given the general public a distorted impression of the safety of nuclear power. The way in which the probability and consequences of big nuclear accidents have been presented in the past is reviewed and recommendations for the future are made including the presentation of the long-term consequences of such accidents in terms of 'reduction in life expectancy', 'increased chance of fatal cancer' and the equivalent pattern of compulsory cigarette smoking. (author)

  8. Nonstandard big bang models

    International Nuclear Information System (INIS)

    Calvao, M.O.; Lima, J.A.S.

    1989-01-01

    The usual FRW hot big-bang cosmologies have been generalized by considering the equation of state ρ = Anm +(γ-1) -1 p, where m is the rest mass of the fluid particles and A is a dimensionless constant. Explicit analytic solutions are given for the flat case (ε=O). For large cosmological times these extended models behave as the standard Einstein-de Sitter universes regardless of the values of A and γ. Unlike the usual FRW flat case the deceleration parameter q is a time-dependent function and its present value, q≅ 1, obtained from the luminosity distance versus redshift relation, may be fitted by taking, for instance, A=1 and γ = 5/3 (monatomic relativistic gas with >> k B T). In all cases the universe cools obeying the same temperature law of the FRW models and it is shown that the age of the universe is only slightly modified. (author) [pt

  9. The Last Big Bang

    Energy Technology Data Exchange (ETDEWEB)

    McGuire, Austin D. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Meade, Roger Allen [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2016-09-13

    As one of the very few people in the world to give the “go/no go” decision to detonate a nuclear device, Austin “Mac” McGuire holds a very special place in the history of both the Los Alamos National Laboratory and the world. As Commander of Joint Task Force Unit 8.1.1, on Christmas Island in the spring and summer of 1962, Mac directed the Los Alamos data collection efforts for twelve of the last atmospheric nuclear detonations conducted by the United States. Since data collection was at the heart of nuclear weapon testing, it fell to Mac to make the ultimate decision to detonate each test device. He calls his experience THE LAST BIG BANG, since these tests, part of Operation Dominic, were characterized by the dramatic displays of the heat, light, and sounds unique to atmospheric nuclear detonations – never, perhaps, to be witnessed again.

  10. A matrix big bang

    International Nuclear Information System (INIS)

    Craps, Ben; Sethi, Savdeep; Verlinde, Erik

    2005-01-01

    The light-like linear dilaton background represents a particularly simple time-dependent 1/2 BPS solution of critical type-IIA superstring theory in ten dimensions. Its lift to M-theory, as well as its Einstein frame metric, are singular in the sense that the geometry is geodesically incomplete and the Riemann tensor diverges along a light-like subspace of codimension one. We study this background as a model for a big bang type singularity in string theory/M-theory. We construct the dual Matrix theory description in terms of a (1+1)-d supersymmetric Yang-Mills theory on a time-dependent world-sheet given by the Milne orbifold of (1+1)-d Minkowski space. Our model provides a framework in which the physics of the singularity appears to be under control

  11. A matrix big bang

    Energy Technology Data Exchange (ETDEWEB)

    Craps, Ben [Instituut voor Theoretische Fysica, Universiteit van Amsterdam, Valckenierstraat 65, 1018 XE Amsterdam (Netherlands); Sethi, Savdeep [Enrico Fermi Institute, University of Chicago, Chicago, IL 60637 (United States); Verlinde, Erik [Instituut voor Theoretische Fysica, Universiteit van Amsterdam, Valckenierstraat 65, 1018 XE Amsterdam (Netherlands)

    2005-10-15

    The light-like linear dilaton background represents a particularly simple time-dependent 1/2 BPS solution of critical type-IIA superstring theory in ten dimensions. Its lift to M-theory, as well as its Einstein frame metric, are singular in the sense that the geometry is geodesically incomplete and the Riemann tensor diverges along a light-like subspace of codimension one. We study this background as a model for a big bang type singularity in string theory/M-theory. We construct the dual Matrix theory description in terms of a (1+1)-d supersymmetric Yang-Mills theory on a time-dependent world-sheet given by the Milne orbifold of (1+1)-d Minkowski space. Our model provides a framework in which the physics of the singularity appears to be under control.

  12. DPF Big One

    International Nuclear Information System (INIS)

    Anon.

    1993-01-01

    At its latest venue at Fermilab from 10-14 November, the American Physical Society's Division of Particles and Fields meeting entered a new dimension. These regular meetings, which allow younger researchers to communicate with their peers, have been gaining popularity over the years (this was the seventh in the series), but nobody had expected almost a thousand participants and nearly 500 requests to give talks. Thus Fermilab's 800-seat auditorium had to be supplemented with another room with a video hookup, while the parallel sessions were organized into nine bewildering streams covering fourteen major physics topics. With the conventionality of the Standard Model virtually unchallenged, physics does not move fast these days. While most of the physics results had already been covered in principle at the International Conference on High Energy Physics held in Dallas in August (October, page 1), the Fermilab DPF meeting had a very different atmosphere. Major international meetings like Dallas attract big names from far and wide, and it is difficult in such an august atmosphere for young researchers to find a receptive audience. This was not the case at the DPF parallel sessions. The meeting also adopted a novel approach, with the parallels sandwiched between an initial day of plenaries to set the scene, and a final day of summaries. With the whole world waiting for the sixth ('top') quark to be discovered at Fermilab's Tevatron protonantiproton collider, the meeting began with updates from Avi Yagil and Ronald Madaras from the big detectors, CDF and DO respectively. Although rumours flew thick and fast, the Tevatron has not yet reached the top, although Yagil could show one intriguing event of a type expected from the heaviest quark

  13. DPF Big One

    Energy Technology Data Exchange (ETDEWEB)

    Anon.

    1993-01-15

    At its latest venue at Fermilab from 10-14 November, the American Physical Society's Division of Particles and Fields meeting entered a new dimension. These regular meetings, which allow younger researchers to communicate with their peers, have been gaining popularity over the years (this was the seventh in the series), but nobody had expected almost a thousand participants and nearly 500 requests to give talks. Thus Fermilab's 800-seat auditorium had to be supplemented with another room with a video hookup, while the parallel sessions were organized into nine bewildering streams covering fourteen major physics topics. With the conventionality of the Standard Model virtually unchallenged, physics does not move fast these days. While most of the physics results had already been covered in principle at the International Conference on High Energy Physics held in Dallas in August (October, page 1), the Fermilab DPF meeting had a very different atmosphere. Major international meetings like Dallas attract big names from far and wide, and it is difficult in such an august atmosphere for young researchers to find a receptive audience. This was not the case at the DPF parallel sessions. The meeting also adopted a novel approach, with the parallels sandwiched between an initial day of plenaries to set the scene, and a final day of summaries. With the whole world waiting for the sixth ('top') quark to be discovered at Fermilab's Tevatron protonantiproton collider, the meeting began with updates from Avi Yagil and Ronald Madaras from the big detectors, CDF and DO respectively. Although rumours flew thick and fast, the Tevatron has not yet reached the top, although Yagil could show one intriguing event of a type expected from the heaviest quark.

  14. Big bang and big crunch in matrix string theory

    International Nuclear Information System (INIS)

    Bedford, J.; Ward, J.; Papageorgakis, C.; Rodriguez-Gomez, D.

    2007-01-01

    Following the holographic description of linear dilaton null cosmologies with a big bang in terms of matrix string theory put forward by Craps, Sethi, and Verlinde, we propose an extended background describing a universe including both big bang and big crunch singularities. This belongs to a class of exact string backgrounds and is perturbative in the string coupling far away from the singularities, both of which can be resolved using matrix string theory. We provide a simple theory capable of describing the complete evolution of this closed universe

  15. Boosting Big National Lab Data

    Energy Technology Data Exchange (ETDEWEB)

    Kleese van Dam, Kerstin [Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

    2013-02-21

    Introduction: Big data. Love it or hate it, solving the world’s most intractable problems requires the ability to make sense of huge and complex sets of data and do it quickly. Speeding up the process – from hours to minutes or from weeks to days – is key to our success. One major source of such big data are physical experiments. As many will know, these physical experiments are commonly used to solve challenges in fields such as energy security, manufacturing, medicine, pharmacology, environmental protection and national security. Experiments use different instruments and sensor types to research for example the validity of new drugs, the base cause for diseases, more efficient energy sources, new materials for every day goods, effective methods for environmental cleanup, the optimal ingredients composition for chocolate or determine how to preserve valuable antics. This is done by experimentally determining the structure, properties and processes that govern biological systems, chemical processes and materials. The speed and quality at which we can acquire new insights from experiments directly influences the rate of scientific progress, industrial innovation and competitiveness. And gaining new groundbreaking insights, faster, is key to the economic success of our nations. Recent years have seen incredible advances in sensor technologies, from house size detector systems in large experiments such as the Large Hadron Collider and the ‘Eye of Gaia’ billion pixel camera detector to high throughput genome sequencing. These developments have led to an exponential increase in data volumes, rates and variety produced by instruments used for experimental work. This increase is coinciding with a need to analyze the experimental results at the time they are collected. This speed is required to optimize the data taking and quality, and also to enable new adaptive experiments, where the sample is manipulated as it is observed, e.g. a substance is injected into a

  16. Big Data in the Industry - Overview of Selected Issues

    Science.gov (United States)

    Gierej, Sylwia

    2017-12-01

    This article reviews selected issues related to the use of Big Data in the industry. The aim is to define the potential scope and forms of using large data sets in manufacturing companies. By systematically reviewing scientific and professional literature, selected issues related to the use of mass data analytics in production were analyzed. A definition of Big Data was presented, detailing its main attributes. The importance of mass data processing technology in the development of Industry 4.0 concept has been highlighted. Subsequently, attention was paid to issues such as production process optimization, decision making and mass production individualisation, and indicated the potential for large volumes of data. As a result, conclusions were drawn regarding the potential of using Big Data in the industry.

  17. BIG DATA IN SUPPLY CHAIN MANAGEMENT: AN EXPLORATORY STUDY

    Directory of Open Access Journals (Sweden)

    Gheorghe MILITARU

    2015-12-01

    Full Text Available The objective of this paper is to set a framework for examining the conditions under which the big data can create long-term profitability through developing dynamic operations and digital supply networks in supply chain. We investigate the extent to which big data analytics has the power to change the competitive landscape of industries that could offer operational, strategic and competitive advantages. This paper is based upon a qualitative study of the convergence of predictive analytics and big data in the field of supply chain management. Our findings indicate a need for manufacturers to introduce analytics tools, real-time data, and more flexible production techniques to improve their productivity in line with the new business model. By gathering and analysing vast volumes of data, analytics tools help companies to resource allocation and capital spends more effectively based on risk assessment. Finally, implications and directions for future research are discussed.

  18. BigOP: Generating Comprehensive Big Data Workloads as a Benchmarking Framework

    OpenAIRE

    Zhu, Yuqing; Zhan, Jianfeng; Weng, Chuliang; Nambiar, Raghunath; Zhang, Jinchao; Chen, Xingzhen; Wang, Lei

    2014-01-01

    Big Data is considered proprietary asset of companies, organizations, and even nations. Turning big data into real treasure requires the support of big data systems. A variety of commercial and open source products have been unleashed for big data storage and processing. While big data users are facing the choice of which system best suits their needs, big data system developers are facing the question of how to evaluate their systems with regard to general big data processing needs. System b...

  19. From big bang to big crunch and beyond

    International Nuclear Information System (INIS)

    Elitzur, Shmuel; Rabinovici, Eliezer; Giveon, Amit; Kutasov, David

    2002-01-01

    We study a quotient Conformal Field Theory, which describes a 3+1 dimensional cosmological spacetime. Part of this spacetime is the Nappi-Witten (NW) universe, which starts at a 'big bang' singularity, expands and then contracts to a 'big crunch' singularity at a finite time. The gauged WZW model contains a number of copies of the NW spacetime, with each copy connected to the preceding one and to the next one at the respective big bang/big crunch singularities. The sequence of NW spacetimes is further connected at the singularities to a series of non-compact static regions with closed timelike curves. These regions contain boundaries, on which the observables of the theory live. This suggests a holographic interpretation of the physics. (author)

  20. Google BigQuery analytics

    CERN Document Server

    Tigani, Jordan

    2014-01-01

    How to effectively use BigQuery, avoid common mistakes, and execute sophisticated queries against large datasets Google BigQuery Analytics is the perfect guide for business and data analysts who want the latest tips on running complex queries and writing code to communicate with the BigQuery API. The book uses real-world examples to demonstrate current best practices and techniques, and also explains and demonstrates streaming ingestion, transformation via Hadoop in Google Compute engine, AppEngine datastore integration, and using GViz with Tableau to generate charts of query results. In addit

  1. Empathy and the Big Five

    OpenAIRE

    Paulus, Christoph

    2016-01-01

    Del Barrio et al. (2004) haben vor mehr als 10 Jahren versucht, eine direkte Beziehung zwischen Empathie und den Big Five herzustellen. Im Mittel hatten in ihrer Stichprobe Frauen höhere Werte in der Empathie und auf den Big Five-Faktoren mit Ausnahme des Faktors Neurotizismus. Zusammenhänge zu Empathie fanden sie in den Bereichen Offenheit, Verträglichkeit, Gewissenhaftigkeit und Extraversion. In unseren Daten besitzen Frauen sowohl in der Empathie als auch den Big Five signifikant höhere We...

  2. "Big data" in economic history.

    Science.gov (United States)

    Gutmann, Myron P; Merchant, Emily Klancher; Roberts, Evan

    2018-03-01

    Big data is an exciting prospect for the field of economic history, which has long depended on the acquisition, keying, and cleaning of scarce numerical information about the past. This article examines two areas in which economic historians are already using big data - population and environment - discussing ways in which increased frequency of observation, denser samples, and smaller geographic units allow us to analyze the past with greater precision and often to track individuals, places, and phenomena across time. We also explore promising new sources of big data: organically created economic data, high resolution images, and textual corpora.

  3. Big Data as Information Barrier

    Directory of Open Access Journals (Sweden)

    Victor Ya. Tsvetkov

    2014-07-01

    Full Text Available The article covers analysis of ‘Big Data’ which has been discussed over last 10 years. The reasons and factors for the issue are revealed. It has proved that the factors creating ‘Big Data’ issue has existed for quite a long time, and from time to time, would cause the informational barriers. Such barriers were successfully overcome through the science and technologies. The conducted analysis refers the “Big Data” issue to a form of informative barrier. This issue may be solved correctly and encourages development of scientific and calculating methods.

  4. Homogeneous and isotropic big rips?

    CERN Document Server

    Giovannini, Massimo

    2005-01-01

    We investigate the way big rips are approached in a fully inhomogeneous description of the space-time geometry. If the pressure and energy densities are connected by a (supernegative) barotropic index, the spatial gradients and the anisotropic expansion decay as the big rip is approached. This behaviour is contrasted with the usual big-bang singularities. A similar analysis is performed in the case of sudden (quiescent) singularities and it is argued that the spatial gradients may well be non-negligible in the vicinity of pressure singularities.

  5. Big Data in Space Science

    OpenAIRE

    Barmby, Pauline

    2018-01-01

    It seems like “big data” is everywhere these days. In planetary science and astronomy, we’ve been dealing with large datasets for a long time. So how “big” is our data? How does it compare to the big data that a bank or an airline might have? What new tools do we need to analyze big datasets, and how can we make better use of existing tools? What kinds of science problems can we address with these? I’ll address these questions with examples including ESA’s Gaia mission, ...

  6. Rate Change Big Bang Theory

    Science.gov (United States)

    Strickland, Ken

    2013-04-01

    The Rate Change Big Bang Theory redefines the birth of the universe with a dramatic shift in energy direction and a new vision of the first moments. With rate change graph technology (RCGT) we can look back 13.7 billion years and experience every step of the big bang through geometrical intersection technology. The analysis of the Big Bang includes a visualization of the first objects, their properties, the astounding event that created space and time as well as a solution to the mystery of anti-matter.

  7. A Grey Theory Based Approach to Big Data Risk Management Using FMEA

    Directory of Open Access Journals (Sweden)

    Maisa Mendonça Silva

    2016-01-01

    Full Text Available Big data is the term used to denote enormous sets of data that differ from other classic databases in four main ways: (huge volume, (high velocity, (much greater variety, and (big value. In general, data are stored in a distributed fashion and on computing nodes as a result of which big data may be more susceptible to attacks by hackers. This paper presents a risk model for big data, which comprises Failure Mode and Effects Analysis (FMEA and Grey Theory, more precisely grey relational analysis. This approach has several advantages: it provides a structured approach in order to incorporate the impact of big data risk factors; it facilitates the assessment of risk by breaking down the overall risk to big data; and finally its efficient evaluation criteria can help enterprises reduce the risks associated with big data. In order to illustrate the applicability of our proposal in practice, a numerical example, with realistic data based on expert knowledge, was developed. The numerical example analyzes four dimensions, that is, managing identification and access, registering the device and application, managing the infrastructure, and data governance, and 20 failure modes concerning the vulnerabilities of big data. The results show that the most important aspect of risk to big data relates to data governance.

  8. Transforming Healthcare Delivery: Integrating Dynamic Simulation Modelling and Big Data in Health Economics and Outcomes Research.

    Science.gov (United States)

    Marshall, Deborah A; Burgos-Liz, Lina; Pasupathy, Kalyan S; Padula, William V; IJzerman, Maarten J; Wong, Peter K; Higashi, Mitchell K; Engbers, Jordan; Wiebe, Samuel; Crown, William; Osgood, Nathaniel D

    2016-02-01

    In the era of the Information Age and personalized medicine, healthcare delivery systems need to be efficient and patient-centred. The health system must be responsive to individual patient choices and preferences about their care, while considering the system consequences. While dynamic simulation modelling (DSM) and big data share characteristics, they present distinct and complementary value in healthcare. Big data and DSM are synergistic-big data offer support to enhance the application of dynamic models, but DSM also can greatly enhance the value conferred by big data. Big data can inform patient-centred care with its high velocity, volume, and variety (the three Vs) over traditional data analytics; however, big data are not sufficient to extract meaningful insights to inform approaches to improve healthcare delivery. DSM can serve as a natural bridge between the wealth of evidence offered by big data and informed decision making as a means of faster, deeper, more consistent learning from that evidence. We discuss the synergies between big data and DSM, practical considerations and challenges, and how integrating big data and DSM can be useful to decision makers to address complex, systemic health economics and outcomes questions and to transform healthcare delivery.

  9. Big Data is invading big places as CERN

    CERN Multimedia

    CERN. Geneva

    2017-01-01

    Big Data technologies are becoming more popular with the constant grow of data generation in different fields such as social networks, internet of things and laboratories like CERN. How is CERN making use of such technologies? How machine learning is applied at CERN with Big Data technologies? How much data we move and how it is analyzed? All these questions will be answered during the talk.

  10. The ethics of big data in big agriculture

    OpenAIRE

    Carbonell (Isabelle M.)

    2016-01-01

    This paper examines the ethics of big data in agriculture, focusing on the power asymmetry between farmers and large agribusinesses like Monsanto. Following the recent purchase of Climate Corp., Monsanto is currently the most prominent biotech agribusiness to buy into big data. With wireless sensors on tractors monitoring or dictating every decision a farmer makes, Monsanto can now aggregate large quantities of previously proprietary farming data, enabling a privileged position with unique in...

  11. An optimal big data workflow for biomedical image analysis

    Directory of Open Access Journals (Sweden)

    Aurelle Tchagna Kouanou

    Full Text Available Background and objective: In the medical field, data volume is increasingly growing, and traditional methods cannot manage it efficiently. In biomedical computation, the continuous challenges are: management, analysis, and storage of the biomedical data. Nowadays, big data technology plays a significant role in the management, organization, and analysis of data, using machine learning and artificial intelligence techniques. It also allows a quick access to data using the NoSQL database. Thus, big data technologies include new frameworks to process medical data in a manner similar to biomedical images. It becomes very important to develop methods and/or architectures based on big data technologies, for a complete processing of biomedical image data. Method: This paper describes big data analytics for biomedical images, shows examples reported in the literature, briefly discusses new methods used in processing, and offers conclusions. We argue for adapting and extending related work methods in the field of big data software, using Hadoop and Spark frameworks. These provide an optimal and efficient architecture for biomedical image analysis. This paper thus gives a broad overview of big data analytics to automate biomedical image diagnosis. A workflow with optimal methods and algorithm for each step is proposed. Results: Two architectures for image classification are suggested. We use the Hadoop framework to design the first, and the Spark framework for the second. The proposed Spark architecture allows us to develop appropriate and efficient methods to leverage a large number of images for classification, which can be customized with respect to each other. Conclusions: The proposed architectures are more complete, easier, and are adaptable in all of the steps from conception. The obtained Spark architecture is the most complete, because it facilitates the implementation of algorithms with its embedded libraries. Keywords: Biomedical images, Big

  12. A practical guide to big data research in psychology.

    Science.gov (United States)

    Chen, Eric Evan; Wojcik, Sean P

    2016-12-01

    The massive volume of data that now covers a wide variety of human behaviors offers researchers in psychology an unprecedented opportunity to conduct innovative theory- and data-driven field research. This article is a practical guide to conducting big data research, covering data management, acquisition, processing, and analytics (including key supervised and unsupervised learning data mining methods). It is accompanied by walkthrough tutorials on data acquisition, text analysis with latent Dirichlet allocation topic modeling, and classification with support vector machines. Big data practitioners in academia, industry, and the community have built a comprehensive base of tools and knowledge that makes big data research accessible to researchers in a broad range of fields. However, big data research does require knowledge of software programming and a different analytical mindset. For those willing to acquire the requisite skills, innovative analyses of unexpected or previously untapped data sources can offer fresh ways to develop, test, and extend theories. When conducted with care and respect, big data research can become an essential complement to traditional research. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

  13. Big climate data analysis

    Science.gov (United States)

    Mudelsee, Manfred

    2015-04-01

    The Big Data era has begun also in the climate sciences, not only in economics or molecular biology. We measure climate at increasing spatial resolution by means of satellites and look farther back in time at increasing temporal resolution by means of natural archives and proxy data. We use powerful supercomputers to run climate models. The model output of the calculations made for the IPCC's Fifth Assessment Report amounts to ~650 TB. The 'scientific evolution' of grid computing has started, and the 'scientific revolution' of quantum computing is being prepared. This will increase computing power, and data amount, by several orders of magnitude in the future. However, more data does not automatically mean more knowledge. We need statisticians, who are at the core of transforming data into knowledge. Statisticians notably also explore the limits of our knowledge (uncertainties, that is, confidence intervals and P-values). Mudelsee (2014 Climate Time Series Analysis: Classical Statistical and Bootstrap Methods. Second edition. Springer, Cham, xxxii + 454 pp.) coined the term 'optimal estimation'. Consider the hyperspace of climate estimation. It has many, but not infinite, dimensions. It consists of the three subspaces Monte Carlo design, method and measure. The Monte Carlo design describes the data generating process. The method subspace describes the estimation and confidence interval construction. The measure subspace describes how to detect the optimal estimation method for the Monte Carlo experiment. The envisaged large increase in computing power may bring the following idea of optimal climate estimation into existence. Given a data sample, some prior information (e.g. measurement standard errors) and a set of questions (parameters to be estimated), the first task is simple: perform an initial estimation on basis of existing knowledge and experience with such types of estimation problems. The second task requires the computing power: explore the hyperspace to

  14. Hey, big spender

    Energy Technology Data Exchange (ETDEWEB)

    Cope, G.

    2000-04-01

    Business to business electronic commerce is looming large in the future of the oil industry. It is estimated that by adopting e-commerce the industry could achieve bottom line savings of between $1.8 to $ 3.4 billion a year on annual gross revenues in excess of $ 30 billion. At present there are several teething problems to overcome such as inter-operability standards, which are at least two or three years away. Tying in electronically with specific suppliers is also an expensive proposition, although the big benefits are in fact in doing business with the same suppliers on a continuing basis. Despite these problems, 14 of the world's largest energy and petrochemical companies joined forces in mid-April to create a single Internet procurement marketplace for the industry's complex supply chain. The exchange was designed by B2B (business-to-business) software provider, Commerce One Inc., ; it will leverage the buying clout of these industry giants (BP Amoco, Royal Dutch Shell Group, Conoco, Occidental Petroleum, Phillips Petroleum, Unocal Corporation and Statoil among them), currently about $ 125 billion on procurement per year; they hope to save between 5 to 30 per cent depending on the product and the region involved. Other similar schemes such as Chevron and partners' Petrocosm Marketplace, Network Oil, a Houston-based Internet portal aimed at smaller petroleum companies, are also doing business in the $ 10 billion per annum range. e-Energy, a cooperative project between IBM Ericson and Telus Advertising is another neutral, virtual marketplace targeted at the oil and gas sector. PetroTRAX, a Calgary-based website plans to take online procurement and auction sales a big step forward by establishing a portal to handle any oil company's asset management needs. There are also a number of websites targeting specific needs: IndigoPool.com (acquisitions and divestitures) and WellBid.com (products related to upstream oil and gas operators) are just

  15. Hey, big spender

    International Nuclear Information System (INIS)

    Cope, G.

    2000-01-01

    Business to business electronic commerce is looming large in the future of the oil industry. It is estimated that by adopting e-commerce the industry could achieve bottom line savings of between $1.8 to $ 3.4 billion a year on annual gross revenues in excess of $ 30 billion. At present there are several teething problems to overcome such as inter-operability standards, which are at least two or three years away. Tying in electronically with specific suppliers is also an expensive proposition, although the big benefits are in fact in doing business with the same suppliers on a continuing basis. Despite these problems, 14 of the world's largest energy and petrochemical companies joined forces in mid-April to create a single Internet procurement marketplace for the industry's complex supply chain. The exchange was designed by B2B (business-to-business) software provider, Commerce One Inc., ; it will leverage the buying clout of these industry giants (BP Amoco, Royal Dutch Shell Group, Conoco, Occidental Petroleum, Phillips Petroleum, Unocal Corporation and Statoil among them), currently about $ 125 billion on procurement per year; they hope to save between 5 to 30 per cent depending on the product and the region involved. Other similar schemes such as Chevron and partners' Petrocosm Marketplace, Network Oil, a Houston-based Internet portal aimed at smaller petroleum companies, are also doing business in the $ 10 billion per annum range. e-Energy, a cooperative project between IBM Ericson and Telus Advertising is another neutral, virtual marketplace targeted at the oil and gas sector. PetroTRAX, a Calgary-based website plans to take online procurement and auction sales a big step forward by establishing a portal to handle any oil company's asset management needs. There are also a number of websites targeting specific needs: IndigoPool.com (acquisitions and divestitures) and WellBid.com (products related to upstream oil and gas operators) are just two examples. All in

  16. Methods and tools for big data visualization

    OpenAIRE

    Zubova, Jelena; Kurasova, Olga

    2015-01-01

    In this paper, methods and tools for big data visualization have been investigated. Challenges faced by the big data analysis and visualization have been identified. Technologies for big data analysis have been discussed. A review of methods and tools for big data visualization has been done. Functionalities of the tools have been demonstrated by examples in order to highlight their advantages and disadvantages.

  17. Measuring the Promise of Big Data Syllabi

    Science.gov (United States)

    Friedman, Alon

    2018-01-01

    Growing interest in Big Data is leading industries, academics and governments to accelerate Big Data research. However, how teachers should teach Big Data has not been fully examined. This article suggests criteria for redesigning Big Data syllabi in public and private degree-awarding higher education establishments. The author conducted a survey…

  18. Big Sky Carbon Sequestration Partnership

    Energy Technology Data Exchange (ETDEWEB)

    Susan Capalbo

    2005-12-31

    The Big Sky Carbon Sequestration Partnership, led by Montana State University, is comprised of research institutions, public entities and private sectors organizations, and the Confederated Salish and Kootenai Tribes and the Nez Perce Tribe. Efforts under this Partnership in Phase I are organized into four areas: (1) Evaluation of sources and carbon sequestration sinks that will be used to determine the location of pilot demonstrations in Phase II; (2) Development of GIS-based reporting framework that links with national networks; (3) Design of an integrated suite of monitoring, measuring, and verification technologies, market-based opportunities for carbon management, and an economic/risk assessment framework; (referred to below as the Advanced Concepts component of the Phase I efforts) and (4) Initiation of a comprehensive education and outreach program. As a result of the Phase I activities, the groundwork is in place to provide an assessment of storage capabilities for CO{sub 2} utilizing the resources found in the Partnership region (both geological and terrestrial sinks), that complements the ongoing DOE research agenda in Carbon Sequestration. The geology of the Big Sky Carbon Sequestration Partnership Region is favorable for the potential sequestration of enormous volume of CO{sub 2}. The United States Geological Survey (USGS 1995) identified 10 geologic provinces and 111 plays in the region. These provinces and plays include both sedimentary rock types characteristic of oil, gas, and coal productions as well as large areas of mafic volcanic rocks. Of the 10 provinces and 111 plays, 1 province and 4 plays are located within Idaho. The remaining 9 provinces and 107 plays are dominated by sedimentary rocks and located in the states of Montana and Wyoming. The potential sequestration capacity of the 9 sedimentary provinces within the region ranges from 25,000 to almost 900,000 million metric tons of CO{sub 2}. Overall every sedimentary formation investigated

  19. The BigBOSS Experiment

    Energy Technology Data Exchange (ETDEWEB)

    Schelgel, D.; Abdalla, F.; Abraham, T.; Ahn, C.; Allende Prieto, C.; Annis, J.; Aubourg, E.; Azzaro, M.; Bailey, S.; Baltay, C.; Baugh, C.; /APC, Paris /Brookhaven /IRFU, Saclay /Marseille, CPPM /Marseille, CPT /Durham U. / /IEU, Seoul /Fermilab /IAA, Granada /IAC, La Laguna

    2011-01-01

    BigBOSS will obtain observational constraints that will bear on three of the four 'science frontier' questions identified by the Astro2010 Cosmology and Fundamental Phyics Panel of the Decadal Survey: Why is the universe accelerating; what is dark matter and what are the properties of neutrinos? Indeed, the BigBOSS project was recommended for substantial immediate R and D support the PASAG report. The second highest ground-based priority from the Astro2010 Decadal Survey was the creation of a funding line within the NSF to support a 'Mid-Scale Innovations' program, and it used BigBOSS as a 'compelling' example for support. This choice was the result of the Decadal Survey's Program Priorization panels reviewing 29 mid-scale projects and recommending BigBOSS 'very highly'.

  20. Biophotonics: the big picture

    Science.gov (United States)

    Marcu, Laura; Boppart, Stephen A.; Hutchinson, Mark R.; Popp, Jürgen; Wilson, Brian C.

    2018-02-01

    The 5th International Conference on Biophotonics (ICOB) held April 30 to May 1, 2017, in Fremantle, Western Australia, brought together opinion leaders to discuss future directions for the field and opportunities to consider. The first session of the conference, "How to Set a Big Picture Biophotonics Agenda," was focused on setting the stage for developing a vision and strategies for translation and impact on society of biophotonic technologies. The invited speakers, panelists, and attendees engaged in discussions that focused on opportunities and promising applications for biophotonic techniques, challenges when working at the confluence of the physical and biological sciences, driving factors for advances of biophotonic technologies, and educational opportunities. We share a summary of the presentations and discussions. Three main themes from the conference are presented in this position paper that capture the current status, opportunities, challenges, and future directions of biophotonics research and key areas of applications: (1) biophotonics at the nano- to microscale level; (2) biophotonics at meso- to macroscale level; and (3) biophotonics and the clinical translation conundrum.

  1. Big Data, Small Sample.

    Science.gov (United States)

    Gerlovina, Inna; van der Laan, Mark J; Hubbard, Alan

    2017-05-20

    Multiple comparisons and small sample size, common characteristics of many types of "Big Data" including those that are produced by genomic studies, present specific challenges that affect reliability of inference. Use of multiple testing procedures necessitates calculation of very small tail probabilities of a test statistic distribution. Results based on large deviation theory provide a formal condition that is necessary to guarantee error rate control given practical sample sizes, linking the number of tests and the sample size; this condition, however, is rarely satisfied. Using methods that are based on Edgeworth expansions (relying especially on the work of Peter Hall), we explore the impact of departures of sampling distributions from typical assumptions on actual error rates. Our investigation illustrates how far the actual error rates can be from the declared nominal levels, suggesting potentially wide-spread problems with error rate control, specifically excessive false positives. This is an important factor that contributes to "reproducibility crisis". We also review some other commonly used methods (such as permutation and methods based on finite sampling inequalities) in their application to multiple testing/small sample data. We point out that Edgeworth expansions, providing higher order approximations to the sampling distribution, offer a promising direction for data analysis that could improve reliability of studies relying on large numbers of comparisons with modest sample sizes.

  2. Big bang darkleosynthesis

    Directory of Open Access Journals (Sweden)

    Gordan Krnjaic

    2015-12-01

    Full Text Available In a popular class of models, dark matter comprises an asymmetric population of composite particles with short range interactions arising from a confined nonabelian gauge group. We show that coupling this sector to a well-motivated light mediator particle yields efficient darkleosynthesis, a dark-sector version of big-bang nucleosynthesis (BBN, in generic regions of parameter space. Dark matter self-interaction bounds typically require the confinement scale to be above ΛQCD, which generically yields large (≫MeV/dark-nucleon binding energies. These bounds further suggest the mediator is relatively weakly coupled, so repulsive forces between dark-sector nuclei are much weaker than Coulomb repulsion between standard-model nuclei, which results in an exponential barrier-tunneling enhancement over standard BBN. Thus, darklei are easier to make and harder to break than visible species with comparable mass numbers. This process can efficiently yield a dominant population of states with masses significantly greater than the confinement scale and, in contrast to dark matter that is a fundamental particle, may allow the dominant form of dark matter to have high spin (S≫3/2, whose discovery would be smoking gun evidence for dark nuclei.

  3. Predicting big bang deuterium

    Energy Technology Data Exchange (ETDEWEB)

    Hata, N.; Scherrer, R.J.; Steigman, G.; Thomas, D.; Walker, T.P. [Department of Physics, Ohio State University, Columbus, Ohio 43210 (United States)

    1996-02-01

    We present new upper and lower bounds to the primordial abundances of deuterium and {sup 3}He based on observational data from the solar system and the interstellar medium. Independent of any model for the primordial production of the elements we find (at the 95{percent} C.L.): 1.5{times}10{sup {minus}5}{le}(D/H){sub {ital P}}{le}10.0{times}10{sup {minus}5} and ({sup 3}He/H){sub {ital P}}{le}2.6{times}10{sup {minus}5}. When combined with the predictions of standard big bang nucleosynthesis, these constraints lead to a 95{percent} C.L. bound on the primordial abundance deuterium: (D/H){sub best}=(3.5{sup +2.7}{sub {minus}1.8}){times}10{sup {minus}5}. Measurements of deuterium absorption in the spectra of high-redshift QSOs will directly test this prediction. The implications of this prediction for the primordial abundances of {sup 4}He and {sup 7}Li are discussed, as well as those for the universal density of baryons. {copyright} {ital 1996 The American Astronomical Society.}

  4. Big bang darkleosynthesis

    Science.gov (United States)

    Krnjaic, Gordan; Sigurdson, Kris

    2015-12-01

    In a popular class of models, dark matter comprises an asymmetric population of composite particles with short range interactions arising from a confined nonabelian gauge group. We show that coupling this sector to a well-motivated light mediator particle yields efficient darkleosynthesis, a dark-sector version of big-bang nucleosynthesis (BBN), in generic regions of parameter space. Dark matter self-interaction bounds typically require the confinement scale to be above ΛQCD, which generically yields large (≫MeV /dark-nucleon) binding energies. These bounds further suggest the mediator is relatively weakly coupled, so repulsive forces between dark-sector nuclei are much weaker than Coulomb repulsion between standard-model nuclei, which results in an exponential barrier-tunneling enhancement over standard BBN. Thus, darklei are easier to make and harder to break than visible species with comparable mass numbers. This process can efficiently yield a dominant population of states with masses significantly greater than the confinement scale and, in contrast to dark matter that is a fundamental particle, may allow the dominant form of dark matter to have high spin (S ≫ 3 / 2), whose discovery would be smoking gun evidence for dark nuclei.

  5. The role of big laboratories

    CERN Document Server

    Heuer, Rolf-Dieter

    2013-01-01

    This paper presents the role of big laboratories in their function as research infrastructures. Starting from the general definition and features of big laboratories, the paper goes on to present the key ingredients and issues, based on scientific excellence, for the successful realization of large-scale science projects at such facilities. The paper concludes by taking the example of scientific research in the field of particle physics and describing the structures and methods required to be implemented for the way forward.

  6. The role of big laboratories

    International Nuclear Information System (INIS)

    Heuer, R-D

    2013-01-01

    This paper presents the role of big laboratories in their function as research infrastructures. Starting from the general definition and features of big laboratories, the paper goes on to present the key ingredients and issues, based on scientific excellence, for the successful realization of large-scale science projects at such facilities. The paper concludes by taking the example of scientific research in the field of particle physics and describing the structures and methods required to be implemented for the way forward. (paper)

  7. Challenges of Big Data Analysis.

    Science.gov (United States)

    Fan, Jianqing; Han, Fang; Liu, Han

    2014-06-01

    Big Data bring new opportunities to modern society and challenges to data scientists. On one hand, Big Data hold great promises for discovering subtle population patterns and heterogeneities that are not possible with small-scale data. On the other hand, the massive sample size and high dimensionality of Big Data introduce unique computational and statistical challenges, including scalability and storage bottleneck, noise accumulation, spurious correlation, incidental endogeneity, and measurement errors. These challenges are distinguished and require new computational and statistical paradigm. This article gives overviews on the salient features of Big Data and how these features impact on paradigm change on statistical and computational methods as well as computing architectures. We also provide various new perspectives on the Big Data analysis and computation. In particular, we emphasize on the viability of the sparsest solution in high-confidence set and point out that exogeneous assumptions in most statistical methods for Big Data can not be validated due to incidental endogeneity. They can lead to wrong statistical inferences and consequently wrong scientific conclusions.

  8. Entering the 'big data' era in medicinal chemistry: molecular promiscuity analysis revisited.

    Science.gov (United States)

    Hu, Ye; Bajorath, Jürgen

    2017-06-01

    The 'big data' concept plays an increasingly important role in many scientific fields. Big data involves more than unprecedentedly large volumes of data that become available. Different criteria characterizing big data must be carefully considered in computational data mining, as we discuss herein focusing on medicinal chemistry. This is a scientific discipline where big data is beginning to emerge and provide new opportunities. For example, the ability of many drugs to specifically interact with multiple targets, termed promiscuity, forms the molecular basis of polypharmacology, a hot topic in drug discovery. Compound promiscuity analysis is an area that is much influenced by big data phenomena. Different results are obtained depending on chosen data selection and confidence criteria, as we also demonstrate.

  9. Reducing Racial Disparities in Breast Cancer Care: The Role of 'Big Data'.

    Science.gov (United States)

    Reeder-Hayes, Katherine E; Troester, Melissa A; Meyer, Anne-Marie

    2017-10-15

    Advances in a wide array of scientific technologies have brought data of unprecedented volume and complexity into the oncology research space. These novel big data resources are applied across a variety of contexts-from health services research using data from insurance claims, cancer registries, and electronic health records, to deeper and broader genomic characterizations of disease. Several forms of big data show promise for improving our understanding of racial disparities in breast cancer, and for powering more intelligent and far-reaching interventions to close the racial gap in breast cancer survival. In this article we introduce several major types of big data used in breast cancer disparities research, highlight important findings to date, and discuss how big data may transform breast cancer disparities research in ways that lead to meaningful, lifesaving changes in breast cancer screening and treatment. We also discuss key challenges that may hinder progress in using big data for cancer disparities research and quality improvement.

  10. Entering the ‘big data’ era in medicinal chemistry: molecular promiscuity analysis revisited

    Science.gov (United States)

    Hu, Ye; Bajorath, Jürgen

    2017-01-01

    The ‘big data’ concept plays an increasingly important role in many scientific fields. Big data involves more than unprecedentedly large volumes of data that become available. Different criteria characterizing big data must be carefully considered in computational data mining, as we discuss herein focusing on medicinal chemistry. This is a scientific discipline where big data is beginning to emerge and provide new opportunities. For example, the ability of many drugs to specifically interact with multiple targets, termed promiscuity, forms the molecular basis of polypharmacology, a hot topic in drug discovery. Compound promiscuity analysis is an area that is much influenced by big data phenomena. Different results are obtained depending on chosen data selection and confidence criteria, as we also demonstrate. PMID:28670471

  11. Generalized formal model of Big Data

    OpenAIRE

    Shakhovska, N.; Veres, O.; Hirnyak, M.

    2016-01-01

    This article dwells on the basic characteristic features of the Big Data technologies. It is analyzed the existing definition of the “big data” term. The article proposes and describes the elements of the generalized formal model of big data. It is analyzed the peculiarities of the application of the proposed model components. It is described the fundamental differences between Big Data technology and business analytics. Big Data is supported by the distributed file system Google File System ...

  12. Optimizing Hadoop Performance for Big Data Analytics in Smart Grid

    Directory of Open Access Journals (Sweden)

    Mukhtaj Khan

    2017-01-01

    Full Text Available The rapid deployment of Phasor Measurement Units (PMUs in power systems globally is leading to Big Data challenges. New high performance computing techniques are now required to process an ever increasing volume of data from PMUs. To that extent the Hadoop framework, an open source implementation of the MapReduce computing model, is gaining momentum for Big Data analytics in smart grid applications. However, Hadoop has over 190 configuration parameters, which can have a significant impact on the performance of the Hadoop framework. This paper presents an Enhanced Parallel Detrended Fluctuation Analysis (EPDFA algorithm for scalable analytics on massive volumes of PMU data. The novel EPDFA algorithm builds on an enhanced Hadoop platform whose configuration parameters are optimized by Gene Expression Programming. Experimental results show that the EPDFA is 29 times faster than the sequential DFA in processing PMU data and 1.87 times faster than a parallel DFA, which utilizes the default Hadoop configuration settings.

  13. Rasdaman for Big Spatial Raster Data

    Science.gov (United States)

    Hu, F.; Huang, Q.; Scheele, C. J.; Yang, C. P.; Yu, M.; Liu, K.

    2015-12-01

    Spatial raster data have grown exponentially over the past decade. Recent advancements on data acquisition technology, such as remote sensing, have allowed us to collect massive observation data of various spatial resolution and domain coverage. The volume, velocity, and variety of such spatial data, along with the computational intensive nature of spatial queries, pose grand challenge to the storage technologies for effective big data management. While high performance computing platforms (e.g., cloud computing) can be used to solve the computing-intensive issues in big data analysis, data has to be managed in a way that is suitable for distributed parallel processing. Recently, rasdaman (raster data manager) has emerged as a scalable and cost-effective database solution to store and retrieve massive multi-dimensional arrays, such as sensor, image, and statistics data. Within this paper, the pros and cons of using rasdaman to manage and query spatial raster data will be examined and compared with other common approaches, including file-based systems, relational databases (e.g., PostgreSQL/PostGIS), and NoSQL databases (e.g., MongoDB and Hive). Earth Observing System (EOS) data collected from NASA's Atmospheric Scientific Data Center (ASDC) will be used and stored in these selected database systems, and a set of spatial and non-spatial queries will be designed to benchmark their performance on retrieving large-scale, multi-dimensional arrays of EOS data. Lessons learnt from using rasdaman will be discussed as well.

  14. A Big Data Decision-making Mechanism for Food Supply Chain

    OpenAIRE

    Ji Guojun; Tan KimHua

    2017-01-01

    Many companies have captured and analyzed huge volumes of data to improve the decision mechanism of supply chain, this paper presents a big data harvest model that uses big data as inputs to make more informed decisions in the food supply chain. By introducing a method of Bayesian network, this paper integrates sample data and finds a cause-and-effect between data to predict market demand. Then the deduction graph model that translates foods demand into processes and divides processes into ta...

  15. A study on decision-making of food supply chain based on big data

    OpenAIRE

    Ji, Guojun; Hu, Limei; Tan, Kim Hua

    2017-01-01

    As more and more companies have captured and analyzed huge volumes of data to improve the performance of supply chain, this paper develops a big data harvest model that uses big data as inputs to make more informed production decisions in the food supply chain. By introducing a method of Bayesian network, this paper integrates sample data and finds a cause-and-effect between data to predict market demand. Then the deduction graph model that translates products demand into processes and divide...

  16. Perspectives on Policy and the Value of Nursing Science in a Big Data Era.

    Science.gov (United States)

    Gephart, Sheila M; Davis, Mary; Shea, Kimberly

    2018-01-01

    As data volume explodes, nurse scientists grapple with ways to adapt to the big data movement without jeopardizing its epistemic values and theoretical focus that celebrate while acknowledging the authority and unity of its body of knowledge. In this article, the authors describe big data and emphasize ways that nursing science brings value to its study. Collective nursing voices that call for more nursing engagement in the big data era are answered with ways to adapt and integrate theoretical and domain expertise from nursing into data science.

  17. 3rd International Symposium on Big Data and Cloud Computing Challenges

    CERN Document Server

    Neelanarayanan, V

    2016-01-01

    This proceedings volume contains selected papers that were presented in the 3rd International Symposium on Big data and Cloud Computing Challenges, 2016 held at VIT University, India on March 10 and 11. New research issues, challenges and opportunities shaping the future agenda in the field of Big Data and Cloud Computing are identified and presented throughout the book, which is intended for researchers, scholars, students, software developers and practitioners working at the forefront in their field. This book acts as a platform for exchanging ideas, setting questions for discussion, and sharing the experience in Big Data and Cloud Computing domain.

  18. [Big Data: the great opportunities and challenges to microbiome and other biomedical research].

    Science.gov (United States)

    Xu, Zhenjiang

    2015-02-01

    With the development of high-throughput technologies, biomedical data has been increasing exponentially in an explosive manner. This brings enormous opportunities and challenges to biomedical researchers on how to effectively utilize big data. Big data is different from traditional data in many ways, described as 3Vs - volume, variety and velocity. From the perspective of biomedical research, here I introduced the characteristics of big data, such as its messiness, re-usage and openness. Focusing on microbiome research of meta-analysis, the author discussed the prospective principles in data collection, challenges of privacy protection in data management, and the scalable tools in data analysis with examples from real life.

  19. Geospatial Big Data Handling Theory and Methods: A Review and Research Challenges

    DEFF Research Database (Denmark)

    Li, Songnian; Dragicevic, Suzana; Anton, François

    2016-01-01

    Big data has now become a strong focus of global interest that is increasingly attracting the attention of academia, industry, government and other organizations. Big data can be situated in the disciplinary area of traditional geospatial data handling theory and methods. The increasing volume...... for Photogrammetry and Remote Sensing (ISPRS) Technical Commission II (TC II) revisits the existing geospatial data handling methods and theories to determine if they are still capable of handling emerging geospatial big data. Further, the paper synthesises problems, major issues and challenges with current...... developments as well as recommending what needs to be developed further in the near future....

  20. Middleware for big data processing: test results

    Science.gov (United States)

    Gankevich, I.; Gaiduchok, V.; Korkhov, V.; Degtyarev, A.; Bogdanov, A.

    2017-12-01

    Dealing with large volumes of data is resource-consuming work which is more and more often delegated not only to a single computer but also to a whole distributed computing system at once. As the number of computers in a distributed system increases, the amount of effort put into effective management of the system grows. When the system reaches some critical size, much effort should be put into improving its fault tolerance. It is difficult to estimate when some particular distributed system needs such facilities for a given workload, so instead they should be implemented in a middleware which works efficiently with a distributed system of any size. It is also difficult to estimate whether a volume of data is large or not, so the middleware should also work with data of any volume. In other words, the purpose of the middleware is to provide facilities that adapt distributed computing system for a given workload. In this paper we introduce such middleware appliance. Tests show that this middleware is well-suited for typical HPC and big data workloads and its performance is comparable with well-known alternatives.

  1. Does Implementation of Big Data Analytics Improve Firms’ Market Value? Investors’ Reaction in Stock Market

    Directory of Open Access Journals (Sweden)

    Hansol Lee

    2017-06-01

    Full Text Available Recently, due to the development of social media, multimedia, and the Internet of Things (IoT, various types of data have increased. As the existing data analytics tools cannot cover this huge volume of data, big data analytics becomes one of the emerging technologies for business today. Considering that big data analytics is an up-to-date term, in the present study, we investigated the impact of implementing big data analytics in the short-term perspective. We used an event study methodology to investigate the changes in stock price caused by announcements on big data analytics solution investment. A total of 54 investment announcements of firms publicly traded in NASDAQ and NYSE from 2010 to 2015 were collected. Our results empirically demonstrate that announcement of firms’ investment on big data solution leads to positive stock market reactions. In addition, we also found that investments on small vendors’ solution with industry-oriented functions tend to result in higher abnormal returns than those on big vendors’ solution with general functions. Finally, our results also suggest that stock market investors highly evaluate big data analytics investments of big firms as compared to those of small firms.

  2. [Big data in official statistics].

    Science.gov (United States)

    Zwick, Markus

    2015-08-01

    The concept of "big data" stands to change the face of official statistics over the coming years, having an impact on almost all aspects of data production. The tasks of future statisticians will not necessarily be to produce new data, but rather to identify and make use of existing data to adequately describe social and economic phenomena. Until big data can be used correctly in official statistics, a lot of questions need to be answered and problems solved: the quality of data, data protection, privacy, and the sustainable availability are some of the more pressing issues to be addressed. The essential skills of official statisticians will undoubtedly change, and this implies a number of challenges to be faced by statistical education systems, in universities, and inside the statistical offices. The national statistical offices of the European Union have concluded a concrete strategy for exploring the possibilities of big data for official statistics, by means of the Big Data Roadmap and Action Plan 1.0. This is an important first step and will have a significant influence on implementing the concept of big data inside the statistical offices of Germany.

  3. Official statistics and Big Data

    Directory of Open Access Journals (Sweden)

    Peter Struijs

    2014-07-01

    Full Text Available The rise of Big Data changes the context in which organisations producing official statistics operate. Big Data provides opportunities, but in order to make optimal use of Big Data, a number of challenges have to be addressed. This stimulates increased collaboration between National Statistical Institutes, Big Data holders, businesses and universities. In time, this may lead to a shift in the role of statistical institutes in the provision of high-quality and impartial statistical information to society. In this paper, the changes in context, the opportunities, the challenges and the way to collaborate are addressed. The collaboration between the various stakeholders will involve each partner building on and contributing different strengths. For national statistical offices, traditional strengths include, on the one hand, the ability to collect data and combine data sources with statistical products and, on the other hand, their focus on quality, transparency and sound methodology. In the Big Data era of competing and multiplying data sources, they continue to have a unique knowledge of official statistical production methods. And their impartiality and respect for privacy as enshrined in law uniquely position them as a trusted third party. Based on this, they may advise on the quality and validity of information of various sources. By thus positioning themselves, they will be able to play their role as key information providers in a changing society.

  4. Big data for bipolar disorder.

    Science.gov (United States)

    Monteith, Scott; Glenn, Tasha; Geddes, John; Whybrow, Peter C; Bauer, Michael

    2016-12-01

    The delivery of psychiatric care is changing with a new emphasis on integrated care, preventative measures, population health, and the biological basis of disease. Fundamental to this transformation are big data and advances in the ability to analyze these data. The impact of big data on the routine treatment of bipolar disorder today and in the near future is discussed, with examples that relate to health policy, the discovery of new associations, and the study of rare events. The primary sources of big data today are electronic medical records (EMR), claims, and registry data from providers and payers. In the near future, data created by patients from active monitoring, passive monitoring of Internet and smartphone activities, and from sensors may be integrated with the EMR. Diverse data sources from outside of medicine, such as government financial data, will be linked for research. Over the long term, genetic and imaging data will be integrated with the EMR, and there will be more emphasis on predictive models. Many technical challenges remain when analyzing big data that relates to size, heterogeneity, complexity, and unstructured text data in the EMR. Human judgement and subject matter expertise are critical parts of big data analysis, and the active participation of psychiatrists is needed throughout the analytical process.

  5. Quantum fields in a big-crunch-big-bang spacetime

    International Nuclear Information System (INIS)

    Tolley, Andrew J.; Turok, Neil

    2002-01-01

    We consider quantum field theory on a spacetime representing the big-crunch-big-bang transition postulated in ekpyrotic or cyclic cosmologies. We show via several independent methods that an essentially unique matching rule holds connecting the incoming state, in which a single extra dimension shrinks to zero, to the outgoing state in which it reexpands at the same rate. For free fields in our construction there is no particle production from the incoming adiabatic vacuum. When interactions are included the particle production for fixed external momentum is finite at the tree level. We discuss a formal correspondence between our construction and quantum field theory on de Sitter spacetime

  6. Poker Player Behavior After Big Wins and Big Losses

    OpenAIRE

    Gary Smith; Michael Levere; Robert Kurtzman

    2009-01-01

    We find that experienced poker players typically change their style of play after winning or losing a big pot--most notably, playing less cautiously after a big loss, evidently hoping for lucky cards that will erase their loss. This finding is consistent with Kahneman and Tversky's (Kahneman, D., A. Tversky. 1979. Prospect theory: An analysis of decision under risk. Econometrica 47(2) 263-292) break-even hypothesis and suggests that when investors incur a large loss, it might be time to take ...

  7. Quest for Value in Big Earth Data

    Science.gov (United States)

    Kuo, Kwo-Sen; Oloso, Amidu O.; Rilee, Mike L.; Doan, Khoa; Clune, Thomas L.; Yu, Hongfeng

    2017-04-01

    Among all the V's of Big Data challenges, such as Volume, Variety, Velocity, Veracity, etc., we believe Value is the ultimate determinant, because a system delivering better value has a competitive edge over others. Although it is not straightforward to assess the value of scientific endeavors, we believe the ratio of scientific productivity increase to investment is a reasonable measure. Our research in Big Data approaches to data-intensive analysis for Earth Science has yielded some insights, as well as evidences, as to how optimal value might be attained. The first insight is that we should avoid, as much as possible, moving data through connections with relatively low bandwidth. That is, we recognize that moving data is expensive, albeit inevitable. They must at least be moved from the storage device into computer main memory and then to CPU registers for computation. When data must be moved it is better to move them via relatively high-bandwidth connections and avoid low-bandwidth ones. For this reason, a technology that can best exploit data locality will have an advantage over others. Data locality is easy to achieve and exploit with only one dataset. With multiple datasets, data colocation becomes important in addition to data locality. However, the organization of datasets can only be co-located for certain types of analyses. It is impossible for them to be co-located for all analyses. Therefore, our second insight is that we need to co-locate the datasets for the most commonly used analyses. In Earth Science, we believe the most common analysis requirement is "spatiotemporal coincidence". For example, when we analyze precipitation systems, we often would like to know the environment conditions "where and when" (i.e. at the same location and time) there is precipitation. This "where and when" indicates the "spatiotemporal coincidence" requirement. Thus, an associated insight is that datasets need to be partitioned per the physical dimensions, i.e. space

  8. A glossary for big data in population and public health: discussion and commentary on terminology and research methods.

    Science.gov (United States)

    Fuller, Daniel; Buote, Richard; Stanley, Kevin

    2017-11-01

    The volume and velocity of data are growing rapidly and big data analytics are being applied to these data in many fields. Population and public health researchers may be unfamiliar with the terminology and statistical methods used in big data. This creates a barrier to the application of big data analytics. The purpose of this glossary is to define terms used in big data and big data analytics and to contextualise these terms. We define the five Vs of big data and provide definitions and distinctions for data mining, machine learning and deep learning, among other terms. We provide key distinctions between big data and statistical analysis methods applied to big data. We contextualise the glossary by providing examples where big data analysis methods have been applied to population and public health research problems and provide brief guidance on how to learn big data analysis methods. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

  9. The challenges of big data.

    Science.gov (United States)

    Mardis, Elaine R

    2016-05-01

    The largely untapped potential of big data analytics is a feeding frenzy that has been fueled by the production of many next-generation-sequencing-based data sets that are seeking to answer long-held questions about the biology of human diseases. Although these approaches are likely to be a powerful means of revealing new biological insights, there are a number of substantial challenges that currently hamper efforts to harness the power of big data. This Editorial outlines several such challenges as a means of illustrating that the path to big data revelations is paved with perils that the scientific community must overcome to pursue this important quest. © 2016. Published by The Company of Biologists Ltd.

  10. Big Book of Windows Hacks

    CERN Document Server

    Gralla, Preston

    2008-01-01

    Bigger, better, and broader in scope, the Big Book of Windows Hacks gives you everything you need to get the most out of your Windows Vista or XP system, including its related applications and the hardware it runs on or connects to. Whether you want to tweak Vista's Aero interface, build customized sidebar gadgets and run them from a USB key, or hack the "unhackable" screensavers, you'll find quick and ingenious ways to bend these recalcitrant operating systems to your will. The Big Book of Windows Hacks focuses on Vista, the new bad boy on Microsoft's block, with hacks and workarounds that

  11. Sosiaalinen asiakassuhdejohtaminen ja big data

    OpenAIRE

    Toivonen, Topi-Antti

    2015-01-01

    Tässä tutkielmassa käsitellään sosiaalista asiakassuhdejohtamista sekä hyötyjä, joita siihen voidaan saada big datan avulla. Sosiaalinen asiakassuhdejohtaminen on terminä uusi ja monille tuntematon. Tutkimusta motivoi aiheen vähäinen tutkimus, suomenkielisen tutkimuksen puuttuminen kokonaan sekä sosiaalisen asiakassuhdejohtamisen mahdollinen olennainen rooli yritysten toiminnassa tulevaisuudessa. Big dataa käsittelevissä tutkimuksissa keskitytään monesti sen tekniseen puoleen, eikä sovellutuk...

  12. Do big gods cause anything?

    DEFF Research Database (Denmark)

    Geertz, Armin W.

    2014-01-01

    Dette er et bidrag til et review symposium vedrørende Ara Norenzayans bog Big Gods: How Religion Transformed Cooperation and Conflict (Princeton University Press 2013). Bogen er spændende men problematisk i forhold til kausalitet, ateisme og stereotyper om jægere-samlere.......Dette er et bidrag til et review symposium vedrørende Ara Norenzayans bog Big Gods: How Religion Transformed Cooperation and Conflict (Princeton University Press 2013). Bogen er spændende men problematisk i forhold til kausalitet, ateisme og stereotyper om jægere-samlere....

  13. Big Data and Social Media

    CERN Multimedia

    CERN. Geneva

    2018-01-01

    A critical analysis of the "keep everything" Big Data era, the impact on our lives of the information, at first glance "convenient for future use" that we make known about ourselves on the network. NB! The lecture will be recorded like all Academic Training lectures. Lecturer's biography: Father of the Internet, see https://internethalloffame.org/inductees/vint-cerf or https://en.wikipedia.org/wiki/Vint_Cerf The video on slide number 9 is from page https://www.gapminder.org/tools/#$state$time$value=2018&value;;&chart-type=bubbles   Keywords: Big Data, Internet, History, Applications, tools, privacy, technology, preservation, surveillance, google, Arpanet, CERN, Web  

  14. Baryon symmetric big bang cosmology

    International Nuclear Information System (INIS)

    Stecker, F.W.

    1978-01-01

    It is stated that the framework of baryon symmetric big bang (BSBB) cosmology offers our greatest potential for deducting the evolution of the Universe because its physical laws and processes have the minimum number of arbitrary assumptions about initial conditions in the big-bang. In addition, it offers the possibility of explaining the photon-baryon ratio in the Universe and how galaxies and galaxy clusters are formed. BSBB cosmology also provides the only acceptable explanation at present for the origin of the cosmic γ-ray background radiation. (author)

  15. Release plan for Big Pete

    International Nuclear Information System (INIS)

    Edwards, T.A.

    1996-11-01

    This release plan is to provide instructions for the Radiological Control Technician (RCT) to conduct surveys for the unconditional release of ''Big Pete,'' which was used in the removal of ''Spacers'' from the N-Reactor. Prior to performing surveys on the rear end portion of ''Big Pete,'' it shall be cleaned (i.e., free of oil, grease, caked soil, heavy dust). If no contamination is found, the vehicle may be released with the permission of the area RCT Supervisor. If contamination is found by any of the surveys, contact the cognizant Radiological Engineer for decontamination instructions

  16. Small quarks make big nuggets

    International Nuclear Information System (INIS)

    Deligeorges, S.

    1985-01-01

    After a brief recall on the classification of subatomic particles, this paper deals with quark nuggets, particle with more than three quarks, a big bag, which is called ''nuclearite''. Neutron stars, in fact, are big sacks of quarks, gigantic nuggets. Now, physicists try to calculate which type of nuggets of strange quark matter is stable, what has been the influence of quark nuggets on the primordial nucleosynthesis. At the present time, one says that if these ''nuggets'' exist, and in a large proportion, they may be candidates for the missing mass [fr

  17. [Big Data- challenges and risks].

    Science.gov (United States)

    Krauß, Manuela; Tóth, Tamás; Hanika, Heinrich; Kozlovszky, Miklós; Dinya, Elek

    2015-12-06

    The term "Big Data" is commonly used to describe the growing mass of information being created recently. New conclusions can be drawn and new services can be developed by the connection, processing and analysis of these information. This affects all aspects of life, including health and medicine. The authors review the application areas of Big Data, and present examples from health and other areas. However, there are several preconditions of the effective use of the opportunities: proper infrastructure, well defined regulatory environment with particular emphasis on data protection and privacy. These issues and the current actions for solution are also presented.

  18. Towards a big crunch dual

    Energy Technology Data Exchange (ETDEWEB)

    Hertog, Thomas E-mail: hertog@vulcan2.physics.ucsb.edu; Horowitz, Gary T

    2004-07-01

    We show there exist smooth asymptotically anti-de Sitter initial data which evolve to a big crunch singularity in a low energy supergravity limit of string theory. This opens up the possibility of using the dual conformal field theory to obtain a fully quantum description of the cosmological singularity. A preliminary study of this dual theory suggests that the big crunch is an endpoint of evolution even in the full string theory. We also show that any theory with scalar solitons must have negative energy solutions. The results presented here clarify our earlier work on cosmic censorship violation in N=8 supergravity. (author)

  19. The Inverted Big-Bang

    OpenAIRE

    Vaas, Ruediger

    2004-01-01

    Our universe appears to have been created not out of nothing but from a strange space-time dust. Quantum geometry (loop quantum gravity) makes it possible to avoid the ominous beginning of our universe with its physically unrealistic (i.e. infinite) curvature, extreme temperature, and energy density. This could be the long sought after explanation of the big-bang and perhaps even opens a window into a time before the big-bang: Space itself may have come from an earlier collapsing universe tha...

  20. Big Cities, Big Problems: Reason for the Elderly to Move?

    NARCIS (Netherlands)

    Fokkema, T.; de Jong-Gierveld, J.; Nijkamp, P.

    1996-01-01

    In many European countries, data on geographical patterns of internal elderly migration show that the elderly (55+) are more likely to leave than to move to the big cities. Besides emphasising the attractive features of the destination areas (pull factors), it is often assumed that this negative

  1. Big-Eyed Bugs Have Big Appetite for Pests

    Science.gov (United States)

    Many kinds of arthropod natural enemies (predators and parasitoids) inhabit crop fields in Arizona and can have a large negative impact on several pest insect species that also infest these crops. Geocoris spp., commonly known as big-eyed bugs, are among the most abundant insect predators in field c...

  2. Health level seven interoperability strategy: big data, incrementally structured.

    Science.gov (United States)

    Dolin, R H; Rogers, B; Jaffe, C

    2015-01-01

    Describe how the HL7 Clinical Document Architecture (CDA), a foundational standard in US Meaningful Use, contributes to a "big data, incrementally structured" interoperability strategy, whereby data structured incrementally gets large amounts of data flowing faster. We present cases showing how this approach is leveraged for big data analysis. To support the assertion that semi-structured narrative in CDA format can be a useful adjunct in an overall big data analytic approach, we present two case studies. The first assesses an organization's ability to generate clinical quality reports using coded data alone vs. coded data supplemented by CDA narrative. The second leverages CDA to construct a network model for referral management, from which additional observations can be gleaned. The first case shows that coded data supplemented by CDA narrative resulted in significant variances in calculated performance scores. In the second case, we found that the constructed network model enables the identification of differences in patient characteristics among different referral work flows. The CDA approach goes after data indirectly, by focusing first on the flow of narrative, which is then incrementally structured. A quantitative assessment of whether this approach will lead to a greater flow of data and ultimately a greater flow of structured data vs. other approaches is planned as a future exercise. Along with growing adoption of CDA, we are now seeing the big data community explore the standard, particularly given its potential to supply analytic en- gines with volumes of data previously not possible.

  3. A survey on Big Data Stream Mining

    African Journals Online (AJOL)

    pc

    2018-03-05

    Mar 5, 2018 ... Big Data can be static on one machine or distributed ... decision making, and process automation. Big data .... Concept Drifting: concept drifting mean the classifier .... transactions generated by a prefix tree structure. EstDec ...

  4. BIG DATA-DRIVEN MARKETING: AN ABSTRACT

    OpenAIRE

    Suoniemi, Samppa; Meyer-Waarden, Lars; Munzel, Andreas

    2017-01-01

    Customer information plays a key role in managing successful relationships with valuable customers. Big data customer analytics use (BD use), i.e., the extent to which customer information derived from big data analytics guides marketing decisions, helps firms better meet customer needs for competitive advantage. This study addresses three research questions: What are the key antecedents of big data customer analytics use? How, and to what extent, does big data customer an...

  5. Big data in Finnish financial services

    OpenAIRE

    Laurila, M. (Mikko)

    2017-01-01

    Abstract This thesis aims to explore the concept of big data, and create understanding of big data maturity in the Finnish financial services industry. The research questions of this thesis are “What kind of big data solutions are being implemented in the Finnish financial services sector?” and “Which factors impede faster implementation of big data solutions in the Finnish financial services sector?”. ...

  6. China: Big Changes Coming Soon

    Science.gov (United States)

    Rowen, Henry S.

    2011-01-01

    Big changes are ahead for China, probably abrupt ones. The economy has grown so rapidly for many years, over 30 years at an average of nine percent a year, that its size makes it a major player in trade and finance and increasingly in political and military matters. This growth is not only of great importance internationally, it is already having…

  7. Big data and urban governance

    NARCIS (Netherlands)

    Taylor, L.; Richter, C.; Gupta, J.; Pfeffer, K.; Verrest, H.; Ros-Tonen, M.

    2015-01-01

    This chapter examines the ways in which big data is involved in the rise of smart cities. Mobile phones, sensors and online applications produce streams of data which are used to regulate and plan the city, often in real time, but which presents challenges as to how the city’s functions are seen and

  8. Big data en gelijke behandeling

    NARCIS (Netherlands)

    Lammerant, Hans; de Hert, Paul; Blok, P.H.; Blok, P.H.

    2017-01-01

    In dit hoofdstuk bekijken we allereerst de voornaamste basisbegrippen inzake gelijke behandeling en discriminatie (paragraaf 6.2). Vervolgens kijken we haar het Nederlandse en Europese juridisch kader inzake non-discriminatie (paragraaf 6.3-6.5) en hoe die regels moeten worden toegepast op big

  9. Research Ethics in Big Data.

    Science.gov (United States)

    Hammer, Marilyn J

    2017-05-01

    The ethical conduct of research includes, in part, patient agreement to participate in studies and the protection of health information. In the evolving world of data science and the accessibility of large quantities of web-based data created by millions of individuals, novel methodologic approaches to answering research questions are emerging. This article explores research ethics in the context of big data.

  10. Big data e data science

    OpenAIRE

    Cavique, Luís

    2014-01-01

    Neste artigo foram apresentados os conceitos básicos de Big Data e a nova área a que deu origem, a Data Science. Em Data Science foi discutida e exemplificada a noção de redução da dimensionalidade dos dados.

  11. The Case for "Big History."

    Science.gov (United States)

    Christian, David

    1991-01-01

    Urges an approach to the teaching of history that takes the largest possible perspective, crossing time as well as space. Discusses the problems and advantages of such an approach. Describes a course on "big" history that begins with time, creation myths, and astronomy, and moves on to paleontology and evolution. (DK)

  12. Finding errors in big data

    NARCIS (Netherlands)

    Puts, Marco; Daas, Piet; de Waal, A.G.

    No data source is perfect. Mistakes inevitably creep in. Spotting errors is hard enough when dealing with survey responses from several thousand people, but the difficulty is multiplied hugely when that mysterious beast Big Data comes into play. Statistics Netherlands is about to publish its first

  13. Sampling Operations on Big Data

    Science.gov (United States)

    2015-11-29

    gories. These include edge sampling methods where edges are selected by a predetermined criteria; snowball sampling methods where algorithms start... Sampling Operations on Big Data Vijay Gadepally, Taylor Herr, Luke Johnson, Lauren Milechin, Maja Milosavljevic, Benjamin A. Miller Lincoln...process and disseminate information for discovery and exploration under real-time constraints. Common signal processing operations such as sampling and

  14. The International Big History Association

    Science.gov (United States)

    Duffy, Michael; Duffy, D'Neil

    2013-01-01

    IBHA, the International Big History Association, was organized in 2010 and "promotes the unified, interdisciplinary study and teaching of history of the Cosmos, Earth, Life, and Humanity." This is the vision that Montessori embraced long before the discoveries of modern science fleshed out the story of the evolving universe. "Big…

  15. NASA's Big Data Task Force

    Science.gov (United States)

    Holmes, C. P.; Kinter, J. L.; Beebe, R. F.; Feigelson, E.; Hurlburt, N. E.; Mentzel, C.; Smith, G.; Tino, C.; Walker, R. J.

    2017-12-01

    Two years ago NASA established the Ad Hoc Big Data Task Force (BDTF - https://science.nasa.gov/science-committee/subcommittees/big-data-task-force), an advisory working group with the NASA Advisory Council system. The scope of the Task Force included all NASA Big Data programs, projects, missions, and activities. The Task Force focused on such topics as exploring the existing and planned evolution of NASA's science data cyber-infrastructure that supports broad access to data repositories for NASA Science Mission Directorate missions; best practices within NASA, other Federal agencies, private industry and research institutions; and Federal initiatives related to big data and data access. The BDTF has completed its two-year term and produced several recommendations plus four white papers for NASA's Science Mission Directorate. This presentation will discuss the activities and results of the TF including summaries of key points from its focused study topics. The paper serves as an introduction to the papers following in this ESSI session.

  16. Big Math for Little Kids

    Science.gov (United States)

    Greenes, Carole; Ginsburg, Herbert P.; Balfanz, Robert

    2004-01-01

    "Big Math for Little Kids," a comprehensive program for 4- and 5-year-olds, develops and expands on the mathematics that children know and are capable of doing. The program uses activities and stories to develop ideas about number, shape, pattern, logical reasoning, measurement, operations on numbers, and space. The activities introduce the…

  17. BIG DATA IN BUSINESS ENVIRONMENT

    Directory of Open Access Journals (Sweden)

    Logica BANICA

    2015-06-01

    Full Text Available In recent years, dealing with a lot of data originating from social media sites and mobile communications among data from business environments and institutions, lead to the definition of a new concept, known as Big Data. The economic impact of the sheer amount of data produced in a last two years has increased rapidly. It is necessary to aggregate all types of data (structured and unstructured in order to improve current transactions, to develop new business models, to provide a real image of the supply and demand and thereby, generate market advantages. So, the companies that turn to Big Data have a competitive advantage over other firms. Looking from the perspective of IT organizations, they must accommodate the storage and processing Big Data, and provide analysis tools that are easily integrated into business processes. This paper aims to discuss aspects regarding the Big Data concept, the principles to build, organize and analyse huge datasets in the business environment, offering a three-layer architecture, based on actual software solutions. Also, the article refers to the graphical tools for exploring and representing unstructured data, Gephi and NodeXL.

  18. From Big Bang to Eternity?

    Indian Academy of Sciences (India)

    at different distances (that is, at different epochs in the past) to come to this ... that the expansion started billions of years ago from an explosive Big Bang. Recent research sheds new light on the key cosmological question about the distant ...

  19. Banking Wyoming big sagebrush seeds

    Science.gov (United States)

    Robert P. Karrfalt; Nancy Shaw

    2013-01-01

    Five commercially produced seed lots of Wyoming big sagebrush (Artemisia tridentata Nutt. var. wyomingensis (Beetle & Young) S.L. Welsh [Asteraceae]) were stored under various conditions for 5 y. Purity, moisture content as measured by equilibrium relative humidity, and storage temperature were all important factors to successful seed storage. Our results indicate...

  20. Big Data: Implications for Health System Pharmacy.

    Science.gov (United States)

    Stokes, Laura B; Rogers, Joseph W; Hertig, John B; Weber, Robert J

    2016-07-01

    Big Data refers to datasets that are so large and complex that traditional methods and hardware for collecting, sharing, and analyzing them are not possible. Big Data that is accurate leads to more confident decision making, improved operational efficiency, and reduced costs. The rapid growth of health care information results in Big Data around health services, treatments, and outcomes, and Big Data can be used to analyze the benefit of health system pharmacy services. The goal of this article is to provide a perspective on how Big Data can be applied to health system pharmacy. It will define Big Data, describe the impact of Big Data on population health, review specific implications of Big Data in health system pharmacy, and describe an approach for pharmacy leaders to effectively use Big Data. A few strategies involved in managing Big Data in health system pharmacy include identifying potential opportunities for Big Data, prioritizing those opportunities, protecting privacy concerns, promoting data transparency, and communicating outcomes. As health care information expands in its content and becomes more integrated, Big Data can enhance the development of patient-centered pharmacy services.

  1. Big data: een zoektocht naar instituties

    NARCIS (Netherlands)

    van der Voort, H.G.; Crompvoets, J

    2016-01-01

    Big data is a well-known phenomenon, even a buzzword nowadays. It refers to an abundance of data and new possibilities to process and use them. Big data is subject of many publications. Some pay attention to the many possibilities of big data, others warn us for their consequences. This special

  2. A SWOT Analysis of Big Data

    Science.gov (United States)

    Ahmadi, Mohammad; Dileepan, Parthasarati; Wheatley, Kathleen K.

    2016-01-01

    This is the decade of data analytics and big data, but not everyone agrees with the definition of big data. Some researchers see it as the future of data analysis, while others consider it as hype and foresee its demise in the near future. No matter how it is defined, big data for the time being is having its glory moment. The most important…

  3. Data, Data, Data : Big, Linked & Open

    NARCIS (Netherlands)

    Folmer, E.J.A.; Krukkert, D.; Eckartz, S.M.

    2013-01-01

    De gehele business en IT-wereld praat op dit moment over Big Data, een trend die medio 2013 Cloud Computing is gepasseerd (op basis van Google Trends). Ook beleidsmakers houden zich actief bezig met Big Data. Neelie Kroes, vice-president van de Europese Commissie, spreekt over de ‘Big Data

  4. A survey of big data research

    Science.gov (United States)

    Fang, Hua; Zhang, Zhaoyang; Wang, Chanpaul Jin; Daneshmand, Mahmoud; Wang, Chonggang; Wang, Honggang

    2015-01-01

    Big data create values for business and research, but pose significant challenges in terms of networking, storage, management, analytics and ethics. Multidisciplinary collaborations from engineers, computer scientists, statisticians and social scientists are needed to tackle, discover and understand big data. This survey presents an overview of big data initiatives, technologies and research in industries and academia, and discusses challenges and potential solutions. PMID:26504265

  5. The BigBoss Experiment

    Energy Technology Data Exchange (ETDEWEB)

    Schelgel, D.; Abdalla, F.; Abraham, T.; Ahn, C.; Allende Prieto, C.; Annis, J.; Aubourg, E.; Azzaro, M.; Bailey, S.; Baltay, C.; Baugh, C.; Bebek, C.; Becerril, S.; Blanton, M.; Bolton, A.; Bromley, B.; Cahn, R.; Carton, P.-H.; Cervanted-Cota, J.L.; Chu, Y.; Cortes, M.; /APC, Paris /Brookhaven /IRFU, Saclay /Marseille, CPPM /Marseille, CPT /Durham U. / /IEU, Seoul /Fermilab /IAA, Granada /IAC, La Laguna / /IAC, Mexico / / /Madrid, IFT /Marseille, Lab. Astrophys. / / /New York U. /Valencia U.

    2012-06-07

    BigBOSS is a Stage IV ground-based dark energy experiment to study baryon acoustic oscillations (BAO) and the growth of structure with a wide-area galaxy and quasar redshift survey over 14,000 square degrees. It has been conditionally accepted by NOAO in response to a call for major new instrumentation and a high-impact science program for the 4-m Mayall telescope at Kitt Peak. The BigBOSS instrument is a robotically-actuated, fiber-fed spectrograph capable of taking 5000 simultaneous spectra over a wavelength range from 340 nm to 1060 nm, with a resolution R = {lambda}/{Delta}{lambda} = 3000-4800. Using data from imaging surveys that are already underway, spectroscopic targets are selected that trace the underlying dark matter distribution. In particular, targets include luminous red galaxies (LRGs) up to z = 1.0, extending the BOSS LRG survey in both redshift and survey area. To probe the universe out to even higher redshift, BigBOSS will target bright [OII] emission line galaxies (ELGs) up to z = 1.7. In total, 20 million galaxy redshifts are obtained to measure the BAO feature, trace the matter power spectrum at smaller scales, and detect redshift space distortions. BigBOSS will provide additional constraints on early dark energy and on the curvature of the universe by measuring the Ly-alpha forest in the spectra of over 600,000 2.2 < z < 3.5 quasars. BigBOSS galaxy BAO measurements combined with an analysis of the broadband power, including the Ly-alpha forest in BigBOSS quasar spectra, achieves a FOM of 395 with Planck plus Stage III priors. This FOM is based on conservative assumptions for the analysis of broad band power (k{sub max} = 0.15), and could grow to over 600 if current work allows us to push the analysis to higher wave numbers (k{sub max} = 0.3). BigBOSS will also place constraints on theories of modified gravity and inflation, and will measure the sum of neutrino masses to 0.024 eV accuracy.

  6. Big-Leaf Mahogany on CITES Appendix II: Big Challenge, Big Opportunity

    Science.gov (United States)

    JAMES GROGAN; PAULO BARRETO

    2005-01-01

    On 15 November 2003, big-leaf mahogany (Swietenia macrophylla King, Meliaceae), the most valuable widely traded Neotropical timber tree, gained strengthened regulatory protection from its listing on Appendix II of the Convention on International Trade in Endangered Species ofWild Fauna and Flora (CITES). CITES is a United Nations-chartered agreement signed by 164...

  7. Megastore: structured storage for Big Data

    Directory of Open Access Journals (Sweden)

    Oswaldo Moscoso Zea

    2012-12-01

    Full Text Available Megastore es uno de los componentes principales de la infraestructura de datos de Google, elcual ha permitido el procesamiento y almacenamiento de grandes volúmenes de datos (BigData con alta escalabilidad, confiabilidad y seguridad. Las compañías e individuos que usanestá tecnología se están beneficiando al mismo tiempo de un servicio estable y de altadisponibilidad. En este artículo se realiza un análisis de la infraestructura de datos de Google,comenzando por una revisión de los componentes principales que se han implementado en losúltimos años hasta la creación de Megastore. Se presenta también un análisis de los aspectostécnicos más importantes que se han implementado en este sistema de almacenamiento y que le han permitido cumplir con los objetivos para los que fue creado.Abstract:Megastore is one of the building blocks of Google’s data infrastructure. It has allowed storingand processing operations of huge volumes of data (Big Data with high scalability, reliabilityand security. Companies and individuals using this technology benefit from a highly availableand stable service. In this paper an analysis of Google’s data infrastructure is made, startingwith a review of the core components that have been developed in recent years until theimplementation of Megastore. An analysis is also made of the most important

  8. Big Data Analysis of Human Genome Variations

    KAUST Repository

    Gojobori, Takashi

    2016-01-25

    Since the human genome draft sequence was in public for the first time in 2000, genomic analyses have been intensively extended to the population level. The following three international projects are good examples for large-scale studies of human genome variations: 1) HapMap Data (1,417 individuals) (http://hapmap.ncbi.nlm.nih.gov/downloads/genotypes/2010-08_phaseII+III/forward/), 2) HGDP (Human Genome Diversity Project) Data (940 individuals) (http://www.hagsc.org/hgdp/files.html), 3) 1000 genomes Data (2,504 individuals) http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/ If we can integrate all three data into a single volume of data, we should be able to conduct a more detailed analysis of human genome variations for a total number of 4,861 individuals (= 1,417+940+2,504 individuals). In fact, we successfully integrated these three data sets by use of information on the reference human genome sequence, and we conducted the big data analysis. In particular, we constructed a phylogenetic tree of about 5,000 human individuals at the genome level. As a result, we were able to identify clusters of ethnic groups, with detectable admixture, that were not possible by an analysis of each of the three data sets. Here, we report the outcome of this kind of big data analyses and discuss evolutionary significance of human genomic variations. Note that the present study was conducted in collaboration with Katsuhiko Mineta and Kosuke Goto at KAUST.

  9. Big Data in Health: a Literature Review from the Year 2005.

    Science.gov (United States)

    de la Torre Díez, Isabel; Cosgaya, Héctor Merino; Garcia-Zapirain, Begoña; López-Coronado, Miguel

    2016-09-01

    The information stored in healthcare systems has increased over the last ten years, leading it to be considered Big Data. There is a wealth of health information ready to be analysed. However, the sheer volume raises a challenge for traditional methods. The aim of this article is to conduct a cutting-edge study on Big Data in healthcare from 2005 to the present. This literature review will help researchers to know how Big Data has developed in the health industry and open up new avenues for research. Information searches have been made on various scientific databases such as Pubmed, Science Direct, Scopus and Web of Science for Big Data in healthcare. The search criteria were "Big Data" and "health" with a date range from 2005 to the present. A total of 9724 articles were found on the databases. 9515 articles were discarded as duplicates or for not having a title of interest to the study. 209 articles were read, with the resulting decision that 46 were useful for this study. 52.6 % of the articles used were found in Science Direct, 23.7 % in Pubmed, 22.1 % through Scopus and the remaining 2.6 % through the Web of Science. Big Data has undergone extremely high growth since 2011 and its use is becoming compulsory in developed nations and in an increasing number of developing nations. Big Data is a step forward and a cost reducer for public and private healthcare.

  10. Empowering Personalized Medicine with Big Data and Semantic Web Technology: Promises, Challenges, and Use Cases.

    Science.gov (United States)

    Panahiazar, Maryam; Taslimitehrani, Vahid; Jadhav, Ashutosh; Pathak, Jyotishman

    2014-10-01

    In healthcare, big data tools and technologies have the potential to create significant value by improving outcomes while lowering costs for each individual patient. Diagnostic images, genetic test results and biometric information are increasingly generated and stored in electronic health records presenting us with challenges in data that is by nature high volume, variety and velocity, thereby necessitating novel ways to store, manage and process big data. This presents an urgent need to develop new, scalable and expandable big data infrastructure and analytical methods that can enable healthcare providers access knowledge for the individual patient, yielding better decisions and outcomes. In this paper, we briefly discuss the nature of big data and the role of semantic web and data analysis for generating "smart data" which offer actionable information that supports better decision for personalized medicine. In our view, the biggest challenge is to create a system that makes big data robust and smart for healthcare providers and patients that can lead to more effective clinical decision-making, improved health outcomes, and ultimately, managing the healthcare costs. We highlight some of the challenges in using big data and propose the need for a semantic data-driven environment to address them. We illustrate our vision with practical use cases, and discuss a path for empowering personalized medicine using big data and semantic web technology.

  11. Creating value in health care through big data: opportunities and policy implications.

    Science.gov (United States)

    Roski, Joachim; Bo-Linn, George W; Andrews, Timothy A

    2014-07-01

    Big data has the potential to create significant value in health care by improving outcomes while lowering costs. Big data's defining features include the ability to handle massive data volume and variety at high velocity. New, flexible, and easily expandable information technology (IT) infrastructure, including so-called data lakes and cloud data storage and management solutions, make big-data analytics possible. However, most health IT systems still rely on data warehouse structures. Without the right IT infrastructure, analytic tools, visualization approaches, work flows, and interfaces, the insights provided by big data are likely to be limited. Big data's success in creating value in the health care sector may require changes in current polices to balance the potential societal benefits of big-data approaches and the protection of patients' confidentiality. Other policy implications of using big data are that many current practices and policies related to data use, access, sharing, privacy, and stewardship need to be revised. Project HOPE—The People-to-People Health Foundation, Inc.

  12. Big Bang Day : The Great Big Particle Adventure - 3. Origins

    CERN Multimedia

    2008-01-01

    In this series, comedian and physicist Ben Miller asks the CERN scientists what they hope to find. If the LHC is successful, it will explain the nature of the Universe around us in terms of a few simple ingredients and a few simple rules. But the Universe now was forged in a Big Bang where conditions were very different, and the rules were very different, and those early moments were crucial to determining how things turned out later. At the LHC they can recreate conditions as they were billionths of a second after the Big Bang, before atoms and nuclei existed. They can find out why matter and antimatter didn't mutually annihilate each other to leave behind a Universe of pure, brilliant light. And they can look into the very structure of space and time - the fabric of the Universe

  13. Antigravity and the big crunch/big bang transition

    Science.gov (United States)

    Bars, Itzhak; Chen, Shih-Hung; Steinhardt, Paul J.; Turok, Neil

    2012-08-01

    We point out a new phenomenon which seems to be generic in 4d effective theories of scalar fields coupled to Einstein gravity, when applied to cosmology. A lift of such theories to a Weyl-invariant extension allows one to define classical evolution through cosmological singularities unambiguously, and hence construct geodesically complete background spacetimes. An attractor mechanism ensures that, at the level of the effective theory, generic solutions undergo a big crunch/big bang transition by contracting to zero size, passing through a brief antigravity phase, shrinking to zero size again, and re-emerging into an expanding normal gravity phase. The result may be useful for the construction of complete bouncing cosmologies like the cyclic model.

  14. Antigravity and the big crunch/big bang transition

    Energy Technology Data Exchange (ETDEWEB)

    Bars, Itzhak [Department of Physics and Astronomy, University of Southern California, Los Angeles, CA 90089-2535 (United States); Chen, Shih-Hung [Perimeter Institute for Theoretical Physics, Waterloo, ON N2L 2Y5 (Canada); Department of Physics and School of Earth and Space Exploration, Arizona State University, Tempe, AZ 85287-1404 (United States); Steinhardt, Paul J., E-mail: steinh@princeton.edu [Department of Physics and Princeton Center for Theoretical Physics, Princeton University, Princeton, NJ 08544 (United States); Turok, Neil [Perimeter Institute for Theoretical Physics, Waterloo, ON N2L 2Y5 (Canada)

    2012-08-29

    We point out a new phenomenon which seems to be generic in 4d effective theories of scalar fields coupled to Einstein gravity, when applied to cosmology. A lift of such theories to a Weyl-invariant extension allows one to define classical evolution through cosmological singularities unambiguously, and hence construct geodesically complete background spacetimes. An attractor mechanism ensures that, at the level of the effective theory, generic solutions undergo a big crunch/big bang transition by contracting to zero size, passing through a brief antigravity phase, shrinking to zero size again, and re-emerging into an expanding normal gravity phase. The result may be useful for the construction of complete bouncing cosmologies like the cyclic model.

  15. Antigravity and the big crunch/big bang transition

    International Nuclear Information System (INIS)

    Bars, Itzhak; Chen, Shih-Hung; Steinhardt, Paul J.; Turok, Neil

    2012-01-01

    We point out a new phenomenon which seems to be generic in 4d effective theories of scalar fields coupled to Einstein gravity, when applied to cosmology. A lift of such theories to a Weyl-invariant extension allows one to define classical evolution through cosmological singularities unambiguously, and hence construct geodesically complete background spacetimes. An attractor mechanism ensures that, at the level of the effective theory, generic solutions undergo a big crunch/big bang transition by contracting to zero size, passing through a brief antigravity phase, shrinking to zero size again, and re-emerging into an expanding normal gravity phase. The result may be useful for the construction of complete bouncing cosmologies like the cyclic model.

  16. Solution of a braneworld big crunch/big bang cosmology

    International Nuclear Information System (INIS)

    McFadden, Paul L.; Turok, Neil; Steinhardt, Paul J.

    2007-01-01

    We solve for the cosmological perturbations in a five-dimensional background consisting of two separating or colliding boundary branes, as an expansion in the collision speed V divided by the speed of light c. Our solution permits a detailed check of the validity of four-dimensional effective theory in the vicinity of the event corresponding to the big crunch/big bang singularity. We show that the four-dimensional description fails at the first nontrivial order in (V/c) 2 . At this order, there is nontrivial mixing of the two relevant four-dimensional perturbation modes (the growing and decaying modes) as the boundary branes move from the narrowly separated limit described by Kaluza-Klein theory to the well-separated limit where gravity is confined to the positive-tension brane. We comment on the cosmological significance of the result and compute other quantities of interest in five-dimensional cosmological scenarios

  17. The ethics of big data in big agriculture

    Directory of Open Access Journals (Sweden)

    Isabelle M. Carbonell

    2016-03-01

    Full Text Available This paper examines the ethics of big data in agriculture, focusing on the power asymmetry between farmers and large agribusinesses like Monsanto. Following the recent purchase of Climate Corp., Monsanto is currently the most prominent biotech agribusiness to buy into big data. With wireless sensors on tractors monitoring or dictating every decision a farmer makes, Monsanto can now aggregate large quantities of previously proprietary farming data, enabling a privileged position with unique insights on a field-by-field basis into a third or more of the US farmland. This power asymmetry may be rebalanced through open-sourced data, and publicly-funded data analytic tools which rival Climate Corp. in complexity and innovation for use in the public domain.

  18. Simulating Smoke Filling in Big Halls by Computational Fluid Dynamics

    Directory of Open Access Journals (Sweden)

    W. K. Chow

    2011-01-01

    Full Text Available Many tall halls of big space volume were built and, to be built in many construction projects in the Far East, particularly Mainland China, Hong Kong, and Taiwan. Smoke is identified to be the key hazard to handle. Consequently, smoke exhaust systems are specified in the fire code in those areas. An update on applying Computational Fluid Dynamics (CFD in smoke exhaust design will be presented in this paper. Key points to note in CFD simulations on smoke filling due to a fire in a big hall will be discussed. Mathematical aspects concerning of discretization of partial differential equations and algorithms for solving the velocity-pressure linked equations are briefly outlined. Results predicted by CFD with different free boundary conditions are compared with those on room fire tests. Standards on grid size, relaxation factors, convergence criteria, and false diffusion should be set up for numerical experiments with CFD.

  19. Big Data and Health Economics: Strengths, Weaknesses, Opportunities and Threats.

    Science.gov (United States)

    Collins, Brendan

    2016-02-01

    'Big data' is the collective name for the increasing capacity of information systems to collect and store large volumes of data, which are often unstructured and time stamped, and to analyse these data by using regression and other statistical techniques. This is a review of the potential applications of big data and health economics, using a SWOT (strengths, weaknesses, opportunities, threats) approach. In health economics, large pseudonymized databases, such as the planned care.data programme in the UK, have the potential to increase understanding of how drugs work in the real world, taking into account adherence, co-morbidities, interactions and side effects. This 'real-world evidence' has applications in individualized medicine. More routine and larger-scale cost and outcomes data collection will make health economic analyses more disease specific and population specific but may require new skill sets. There is potential for biomonitoring and lifestyle data to inform health economic analyses and public health policy.

  20. Data Management and Preservation Planning for Big Science

    Directory of Open Access Journals (Sweden)

    Juan Bicarregui

    2013-06-01

    Full Text Available ‘Big Science’ - that is, science which involves large collaborations with dedicated facilities, and involving large data volumes and multinational investments – is often seen as different when it comes to data management and preservation planning. Big Science handles its data differently from other disciplines and has data management problems that are qualitatively different from other disciplines. In part, these differences arise from the quantities of data involved, but possibly more importantly from the cultural, organisational and technical distinctiveness of these academic cultures. Consequently, the data management systems are typically and rationally bespoke, but this means that the planning for data management and preservation (DMP must also be bespoke.These differences are such that ‘just read and implement the OAIS specification’ is reasonable Data Management and Preservation (DMP advice, but this bald prescription can and should be usefully supported by a methodological ‘toolkit’, including overviews, case-studies and costing models to provide guidance on developing best practice in DMP policy and infrastructure for these projects, as well as considering OAIS validation, audit and cost modelling.In this paper, we build on previous work with the LIGO collaboration to consider the role of DMP planning within these big science scenarios, and discuss how to apply current best practice. We discuss the result of the MaRDI-Gross project (Managing Research Data Infrastructures – Big Science, which has been developing a toolkit to provide guidelines on the application of best practice in DMP planning within big science projects. This is targeted primarily at projects’ engineering managers, but intending also to help funders collaborate on DMP plans which satisfy the requirements imposed on them.

  1. Big Data – Big Deal for Organization Design?

    OpenAIRE

    Janne J. Korhonen

    2014-01-01

    Analytics is an increasingly important source of competitive advantage. It has even been posited that big data will be the next strategic emphasis of organizations and that analytics capability will be manifested in organizational structure. In this article, I explore how analytics capability might be reflected in organizational structure using the notion of  “requisite organization” developed by Jaques (1998). Requisite organization argues that a new strategic emphasis requires the addition ...

  2. Nowcasting using news topics Big Data versus big bank

    OpenAIRE

    Thorsrud, Leif Anders

    2016-01-01

    The agents in the economy use a plethora of high frequency information, including news media, to guide their actions and thereby shape aggregate economic fluctuations. Traditional nowcasting approches have to a relatively little degree made use of such information. In this paper, I show how unstructured textual information in a business newspaper can be decomposed into daily news topics and used to nowcast quarterly GDP growth. Compared with a big bank of experts, here represented by o cial c...

  3. A Big Data Decision-making Mechanism for Food Supply Chain

    Directory of Open Access Journals (Sweden)

    Ji Guojun

    2017-01-01

    Full Text Available Many companies have captured and analyzed huge volumes of data to improve the decision mechanism of supply chain, this paper presents a big data harvest model that uses big data as inputs to make more informed decisions in the food supply chain. By introducing a method of Bayesian network, this paper integrates sample data and finds a cause-and-effect between data to predict market demand. Then the deduction graph model that translates foods demand into processes and divides processes into tasks and assets is presented, and an example of how big data in the food supply chain can be combined with Bayesian network and deduction graph model to guide production decision. Our conclusions indicate that the decision-making mechanism has vast potential by extracting value from big data.

  4. Big Data in Medicine is Driving Big Changes

    Science.gov (United States)

    Verspoor, K.

    2014-01-01

    Summary Objectives To summarise current research that takes advantage of “Big Data” in health and biomedical informatics applications. Methods Survey of trends in this work, and exploration of literature describing how large-scale structured and unstructured data sources are being used to support applications from clinical decision making and health policy, to drug design and pharmacovigilance, and further to systems biology and genetics. Results The survey highlights ongoing development of powerful new methods for turning that large-scale, and often complex, data into information that provides new insights into human health, in a range of different areas. Consideration of this body of work identifies several important paradigm shifts that are facilitated by Big Data resources and methods: in clinical and translational research, from hypothesis-driven research to data-driven research, and in medicine, from evidence-based practice to practice-based evidence. Conclusions The increasing scale and availability of large quantities of health data require strategies for data management, data linkage, and data integration beyond the limits of many existing information systems, and substantial effort is underway to meet those needs. As our ability to make sense of that data improves, the value of the data will continue to increase. Health systems, genetics and genomics, population and public health; all areas of biomedicine stand to benefit from Big Data and the associated technologies. PMID:25123716

  5. Big data is not a monolith

    CERN Document Server

    Ekbia, Hamid R; Mattioli, Michael

    2016-01-01

    Big data is ubiquitous but heterogeneous. Big data can be used to tally clicks and traffic on web pages, find patterns in stock trades, track consumer preferences, identify linguistic correlations in large corpuses of texts. This book examines big data not as an undifferentiated whole but contextually, investigating the varied challenges posed by big data for health, science, law, commerce, and politics. Taken together, the chapters reveal a complex set of problems, practices, and policies. The advent of big data methodologies has challenged the theory-driven approach to scientific knowledge in favor of a data-driven one. Social media platforms and self-tracking tools change the way we see ourselves and others. The collection of data by corporations and government threatens privacy while promoting transparency. Meanwhile, politicians, policy makers, and ethicists are ill-prepared to deal with big data's ramifications. The contributors look at big data's effect on individuals as it exerts social control throu...

  6. Big Data for Precision Medicine

    Directory of Open Access Journals (Sweden)

    Daniel Richard Leff

    2015-09-01

    Full Text Available This article focuses on the potential impact of big data analysis to improve health, prevent and detect disease at an earlier stage, and personalize interventions. The role that big data analytics may have in interrogating the patient electronic health record toward improved clinical decision support is discussed. We examine developments in pharmacogenetics that have increased our appreciation of the reasons why patients respond differently to chemotherapy. We also assess the expansion of online health communications and the way in which this data may be capitalized on in order to detect public health threats and control or contain epidemics. Finally, we describe how a new generation of wearable and implantable body sensors may improve wellbeing, streamline management of chronic diseases, and improve the quality of surgical implants.

  7. Big Data hvor N=1

    DEFF Research Database (Denmark)

    Bardram, Jakob Eyvind

    2017-01-01

    Forskningen vedrørende anvendelsen af ’big data’ indenfor sundhed er kun lige begyndt, og kan på sigt blive en stor hjælp i forhold til at tilrettelægge en mere personlig og helhedsorienteret sundhedsindsats for multisyge. Personlig sundhedsteknologi, som kort præsenteres i dette kapital, rummer et...... stor potentiale for at gennemføre ’big data’ analyser for den enkelte person, det vil sige hvor N=1. Der er store teknologiske udfordringer i at få lavet teknologier og metoder til at indsamle og håndtere personlige data, som kan deles, på tværs på en standardiseret, forsvarlig, robust, sikker og ikke...

  8. George and the big bang

    CERN Document Server

    Hawking, Lucy; Parsons, Gary

    2012-01-01

    George has problems. He has twin baby sisters at home who demand his parents’ attention. His beloved pig Freddy has been exiled to a farm, where he’s miserable. And worst of all, his best friend, Annie, has made a new friend whom she seems to like more than George. So George jumps at the chance to help Eric with his plans to run a big experiment in Switzerland that seeks to explore the earliest moment of the universe. But there is a conspiracy afoot, and a group of evildoers is planning to sabotage the experiment. Can George repair his friendship with Annie and piece together the clues before Eric’s experiment is destroyed forever? This engaging adventure features essays by Professor Stephen Hawking and other eminent physicists about the origins of the universe and ends with a twenty-page graphic novel that explains how the Big Bang happened—in reverse!

  9. Did the Big Bang begin?

    International Nuclear Information System (INIS)

    Levy-Leblond, J.

    1990-01-01

    It is argued that the age of the universe may well be numerically finite (20 billion years or so) and conceptually infinite. A new and natural time scale is defined on a physical basis using group-theoretical arguments. An additive notion of time is obtained according to which the age of the universe is indeed infinite. In other words, never did the Big Bang begin. This new time scale is not supposed to replace the ordinary cosmic time scale, but to supplement it (in the same way as rapidity has taken a place by the side of velocity in Einsteinian relativity). The question is discussed within the framework of conventional (big-bang) and classical (nonquantum) cosmology, but could easily be extended to more elaborate views, as the purpose is not so much to modify present theories as to reach a deeper understanding of their meaning

  10. Big Data and central banks

    OpenAIRE

    David Bholat

    2015-01-01

    This commentary recaps a Centre for Central Banking Studies event held at the Bank of England on 2–3 July 2014. The article covers three main points. First, it situates the Centre for Central Banking Studies event within the context of the Bank’s Strategic Plan and initiatives. Second, it summarises and reflects on major themes from the event. Third, the article links central banks’ emerging interest in Big Data approaches with their broader uptake by other economic agents.

  11. Big Bang or vacuum fluctuation

    International Nuclear Information System (INIS)

    Zel'dovich, Ya.B.

    1980-01-01

    Some general properties of vacuum fluctuations in quantum field theory are described. The connection between the ''energy dominance'' of the energy density of vacuum fluctuations in curved space-time and the presence of singularity is discussed. It is pointed out that a de-Sitter space-time (with the energy density of the vacuum fluctuations in the Einstein equations) that matches the expanding Friedman solution may describe the history of the Universe before the Big Bang. (P.L.)

  12. Big bang is not needed

    Energy Technology Data Exchange (ETDEWEB)

    Allen, A.D.

    1976-02-01

    Recent computer simulations indicate that a system of n gravitating masses breaks up, even when the total energy is negative. As a result, almost any initial phase-space distribution results in a universe that eventually expands under the Hubble law. Hence Hubble expansion implies little regarding an initial cosmic state. Especially it does not imply the singularly dense superpositioned state used in the big bang model.

  13. Big Data Comes to School

    Directory of Open Access Journals (Sweden)

    Bill Cope

    2016-03-01

    Full Text Available The prospect of “big data” at once evokes optimistic views of an information-rich future and concerns about surveillance that adversely impacts our personal and private lives. This overview article explores the implications of big data in education, focusing by way of example on data generated by student writing. We have chosen writing because it presents particular complexities, highlighting the range of processes for collecting and interpreting evidence of learning in the era of computer-mediated instruction and assessment as well as the challenges. Writing is significant not only because it is central to the core subject area of literacy; it is also an ideal medium for the representation of deep disciplinary knowledge across a number of subject areas. After defining what big data entails in education, we map emerging sources of evidence of learning that separately and together have the potential to generate unprecedented amounts of data: machine assessments, structured data embedded in learning, and unstructured data collected incidental to learning activity. Our case is that these emerging sources of evidence of learning have significant implications for the traditional relationships between assessment and instruction. Moreover, for educational researchers, these data are in some senses quite different from traditional evidentiary sources, and this raises a number of methodological questions. The final part of the article discusses implications for practice in an emerging field of education data science, including publication of data, data standards, and research ethics.

  14. A peek into the future of radiology using big data applications

    Directory of Open Access Journals (Sweden)

    Amit T Kharat

    2017-01-01

    Full Text Available Big data is extremely large amount of data which is available in the radiology department. Big data is identified by four Vs – Volume, Velocity, Variety, and Veracity. By applying different algorithmic tools and converting raw data to transformed data in such large datasets, there is a possibility of understanding and using radiology data for gaining new knowledge and insights. Big data analytics consists of 6Cs – Connection, Cloud, Cyber, Content, Community, and Customization. The global technological prowess and per-capita capacity to save digital information has roughly doubled every 40 months since the 1980's. By using big data, the planning and implementation of radiological procedures in radiology departments can be given a great boost. Potential applications of big data in the future are scheduling of scans, creating patient-specific personalized scanning protocols, radiologist decision support, emergency reporting, virtual quality assurance for the radiologist, etc. Targeted use of big data applications can be done for images by supporting the analytic process. Screening software tools designed on big data can be used to highlight a region of interest, such as subtle changes in parenchymal density, solitary pulmonary nodule, or focal hepatic lesions, by plotting its multidimensional anatomy. Following this, we can run more complex applications such as three-dimensional multi planar reconstructions (MPR, volumetric rendering (VR, and curved planar reconstruction, which consume higher system resources on targeted data subsets rather than querying the complete cross-sectional imaging dataset. This pre-emptive selection of dataset can substantially reduce the system requirements such as system memory, server load and provide prompt results. However, a word of caution, “big data should not become “dump data” due to inadequate and poor analysis and non-structured improperly stored data. In the near future, big data can ring in the

  15. A peek into the future of radiology using big data applications.

    Science.gov (United States)

    Kharat, Amit T; Singhal, Shubham

    2017-01-01

    Big data is extremely large amount of data which is available in the radiology department. Big data is identified by four Vs - Volume, Velocity, Variety, and Veracity. By applying different algorithmic tools and converting raw data to transformed data in such large datasets, there is a possibility of understanding and using radiology data for gaining new knowledge and insights. Big data analytics consists of 6Cs - Connection, Cloud, Cyber, Content, Community, and Customization. The global technological prowess and per-capita capacity to save digital information has roughly doubled every 40 months since the 1980's. By using big data, the planning and implementation of radiological procedures in radiology departments can be given a great boost. Potential applications of big data in the future are scheduling of scans, creating patient-specific personalized scanning protocols, radiologist decision support, emergency reporting, virtual quality assurance for the radiologist, etc. Targeted use of big data applications can be done for images by supporting the analytic process. Screening software tools designed on big data can be used to highlight a region of interest, such as subtle changes in parenchymal density, solitary pulmonary nodule, or focal hepatic lesions, by plotting its multidimensional anatomy. Following this, we can run more complex applications such as three-dimensional multi planar reconstructions (MPR), volumetric rendering (VR), and curved planar reconstruction, which consume higher system resources on targeted data subsets rather than querying the complete cross-sectional imaging dataset. This pre-emptive selection of dataset can substantially reduce the system requirements such as system memory, server load and provide prompt results. However, a word of caution, "big data should not become "dump data" due to inadequate and poor analysis and non-structured improperly stored data. In the near future, big data can ring in the era of personalized and

  16. A peek into the future of radiology using big data applications

    Science.gov (United States)

    Kharat, Amit T.; Singhal, Shubham

    2017-01-01

    Big data is extremely large amount of data which is available in the radiology department. Big data is identified by four Vs – Volume, Velocity, Variety, and Veracity. By applying different algorithmic tools and converting raw data to transformed data in such large datasets, there is a possibility of understanding and using radiology data for gaining new knowledge and insights. Big data analytics consists of 6Cs – Connection, Cloud, Cyber, Content, Community, and Customization. The global technological prowess and per-capita capacity to save digital information has roughly doubled every 40 months since the 1980's. By using big data, the planning and implementation of radiological procedures in radiology departments can be given a great boost. Potential applications of big data in the future are scheduling of scans, creating patient-specific personalized scanning protocols, radiologist decision support, emergency reporting, virtual quality assurance for the radiologist, etc. Targeted use of big data applications can be done for images by supporting the analytic process. Screening software tools designed on big data can be used to highlight a region of interest, such as subtle changes in parenchymal density, solitary pulmonary nodule, or focal hepatic lesions, by plotting its multidimensional anatomy. Following this, we can run more complex applications such as three-dimensional multi planar reconstructions (MPR), volumetric rendering (VR), and curved planar reconstruction, which consume higher system resources on targeted data subsets rather than querying the complete cross-sectional imaging dataset. This pre-emptive selection of dataset can substantially reduce the system requirements such as system memory, server load and provide prompt results. However, a word of caution, “big data should not become “dump data” due to inadequate and poor analysis and non-structured improperly stored data. In the near future, big data can ring in the era of personalized

  17. The Document Explosion in the World of Big Data--Curriculum Considerations

    Science.gov (United States)

    Liu, Michelle; Murphy, Diane

    2014-01-01

    Within the context of "big data", there is an increasing focus on the source of the large volumes of data now stored electronically. The greatest portion of this data is unstructured and comes from a variety of sources in a variety of formats, much of which does not conform to a consistent data model. As business and government…

  18. Big Data and Knowledge Management: A Possible Course to Combine Them Together

    Science.gov (United States)

    Hijazi, Sam

    2017-01-01

    Big data (BD) is the buzz phrase these days. Everyone is talking about its potential, its volume, its variety, and its velocity. Knowledge management (KM) has been around since the mid-1990s. The goals of KM have been to collect, store, categorize, mine, and process data into knowledge. The methods of knowledge acquisition varied from…

  19. After the Big Bang: What's Next in Design Education? Time to Relax?

    Science.gov (United States)

    Fleischmann, Katja

    2015-01-01

    The article "Big Bang technology: What's next in design education, radical innovation or incremental change?" (Fleischmann, 2013) appeared in the "Journal of Learning Design" Volume 6, Issue 3 in 2013. Two years on, Associate Professor Fleischmann reflects upon her original article within this article. Although it has only been…

  20. Turning big bang into big bounce. I. Classical dynamics

    Science.gov (United States)

    Dzierżak, Piotr; Małkiewicz, Przemysław; Piechocki, Włodzimierz

    2009-11-01

    The big bounce (BB) transition within a flat Friedmann-Robertson-Walker model is analyzed in the setting of loop geometry underlying the loop cosmology. We solve the constraint of the theory at the classical level to identify physical phase space and find the Lie algebra of the Dirac observables. We express energy density of matter and geometrical functions in terms of the observables. It is the modification of classical theory by the loop geometry that is responsible for BB. The classical energy scale specific to BB depends on a parameter that should be fixed either by cosmological data or determined theoretically at quantum level, otherwise the energy scale stays unknown.

  1. Big Data Knowledge in Global Health Education.

    Science.gov (United States)

    Olayinka, Olaniyi; Kekeh, Michele; Sheth-Chandra, Manasi; Akpinar-Elci, Muge

    The ability to synthesize and analyze massive amounts of data is critical to the success of organizations, including those that involve global health. As countries become highly interconnected, increasing the risk for pandemics and outbreaks, the demand for big data is likely to increase. This requires a global health workforce that is trained in the effective use of big data. To assess implementation of big data training in global health, we conducted a pilot survey of members of the Consortium of Universities of Global Health. More than half the respondents did not have a big data training program at their institution. Additionally, the majority agreed that big data training programs will improve global health deliverables, among other favorable outcomes. Given the observed gap and benefits, global health educators may consider investing in big data training for students seeking a career in global health. Copyright © 2017 Icahn School of Medicine at Mount Sinai. Published by Elsevier Inc. All rights reserved.

  2. DB2 11 the database for big data & analytics

    CERN Document Server

    Molaro, Cristian; Purcell, Terry

    2013-01-01

    The landscape of today's business is shaped by the mountains of data being produced, with rapid growth in the volume, variety, and velocity of data due to the explosion of smart devices, mobile applications, cloud computing, and social media. Much of this growth has been in unstructured data; however, by 2020, internet business transactions-business-to-business and business-to-consumer-are predicted to reach 450 billion per day. Smart organizations are seeking innovative ways to turn this explosion of data, called big data, i

  3. Big Data, jejich skladování a možnosti využití

    OpenAIRE

    Macek, Jáchym

    2013-01-01

    The content of this bachelor's thesis is to analyze work with data especially with large-volume unstructured data, thus Big Data. The thesis is retrieval and contents informational survey based on questionnaires and interviews. The aim is to evaluate and approximate Big Data theme, their storage, tools for their management and opportunities of its exploitation to the reader from technological and business point of view. The objective for the practical part is a survey. The thesis is divided i...

  4. Big Data Strategy for Telco: Network Transformation

    OpenAIRE

    F. Amin; S. Feizi

    2014-01-01

    Big data has the potential to improve the quality of services; enable infrastructure that businesses depend on to adapt continually and efficiently; improve the performance of employees; help organizations better understand customers; and reduce liability risks. Analytics and marketing models of fixed and mobile operators are falling short in combating churn and declining revenue per user. Big Data presents new method to reverse the way and improve profitability. The benefits of Big Data and ...

  5. Big Data in Shipping - Challenges and Opportunities

    OpenAIRE

    Rødseth, Ørnulf Jan; Perera, Lokukaluge Prasad; Mo, Brage

    2016-01-01

    Big Data is getting popular in shipping where large amounts of information is collected to better understand and improve logistics, emissions, energy consumption and maintenance. Constraints to the use of big data include cost and quality of on-board sensors and data acquisition systems, satellite communication, data ownership and technical obstacles to effective collection and use of big data. New protocol standards may simplify the process of collecting and organizing the data, including in...

  6. Big Data in Action for Government : Big Data Innovation in Public Services, Policy, and Engagement

    OpenAIRE

    World Bank

    2017-01-01

    Governments have an opportunity to harness big data solutions to improve productivity, performance and innovation in service delivery and policymaking processes. In developing countries, governments have an opportunity to adopt big data solutions and leapfrog traditional administrative approaches

  7. Big Data for Infectious Disease Surveillance and Modeling.

    Science.gov (United States)

    Bansal, Shweta; Chowell, Gerardo; Simonsen, Lone; Vespignani, Alessandro; Viboud, Cécile

    2016-12-01

    We devote a special issue of the Journal of Infectious Diseases to review the recent advances of big data in strengthening disease surveillance, monitoring medical adverse events, informing transmission models, and tracking patient sentiments and mobility. We consider a broad definition of big data for public health, one encompassing patient information gathered from high-volume electronic health records and participatory surveillance systems, as well as mining of digital traces such as social media, Internet searches, and cell-phone logs. We introduce nine independent contributions to this special issue and highlight several cross-cutting areas that require further research, including representativeness, biases, volatility, and validation, and the need for robust statistical and hypotheses-driven analyses. Overall, we are optimistic that the big-data revolution will vastly improve the granularity and timeliness of available epidemiological information, with hybrid systems augmenting rather than supplanting traditional surveillance systems, and better prospects for accurate infectious diseases models and forecasts. Published by Oxford University Press for the Infectious Diseases Society of America 2016. This work is written by (a) US Government employee(s) and is in the public domain in the US.

  8. BIGCHEM: Challenges and Opportunities for Big Data Analysis in Chemistry.

    Science.gov (United States)

    Tetko, Igor V; Engkvist, Ola; Koch, Uwe; Reymond, Jean-Louis; Chen, Hongming

    2016-12-01

    The increasing volume of biomedical data in chemistry and life sciences requires the development of new methods and approaches for their handling. Here, we briefly discuss some challenges and opportunities of this fast growing area of research with a focus on those to be addressed within the BIGCHEM project. The article starts with a brief description of some available resources for "Big Data" in chemistry and a discussion of the importance of data quality. We then discuss challenges with visualization of millions of compounds by combining chemical and biological data, the expectations from mining the "Big Data" using advanced machine-learning methods, and their applications in polypharmacology prediction and target de-convolution in phenotypic screening. We show that the efficient exploration of billions of molecules requires the development of smart strategies. We also address the issue of secure information sharing without disclosing chemical structures, which is critical to enable bi-party or multi-party data sharing. Data sharing is important in the context of the recent trend of "open innovation" in pharmaceutical industry, which has led to not only more information sharing among academics and pharma industries but also the so-called "precompetitive" collaboration between pharma companies. At the end we highlight the importance of education in "Big Data" for further progress of this area. © 2016 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA.

  9. Big data in complex systems challenges and opportunities

    CERN Document Server

    Azar, Ahmad; Snasael, Vaclav; Kacprzyk, Janusz; Abawajy, Jemal

    2015-01-01

    This volume provides challenges and Opportunities with updated, in-depth material on the application of Big data to complex systems in order to find solutions for the challenges and problems facing big data sets applications. Much data today is not natively in structured format; for example, tweets and blogs are weakly structured pieces of text, while images and video are structured for storage and display, but not for semantic content and search. Therefore transforming such content into a structured format for later analysis is a major challenge. Data analysis, organization, retrieval, and modeling are other  foundational challenges treated in this book. The material of this book will be useful for researchers and practitioners in the field of big data as well as advanced undergraduate and graduate  students. Each of the 17 chapters in the book opens with a chapter abstract and key terms list. The chapters are organized along the lines of problem description, related works, and analysis of the results and ...

  10. Ideal MHD B limits in the BIG DEE tokamak

    International Nuclear Information System (INIS)

    Helton, F.J.; Bernard, L.C.; Greene, J.M.

    1983-01-01

    Using D-D reactions, tokamak reactors become economically attractive when B (the ratio of volume averaged pressure to magnetic pressure) exceeds 5 percent. Ideal MID instabilities are of great concern because they have the potential to limit B below this range and so extensive studies have been done to determine ideal MHD B limits. As B increases with inverse aspect ratio, elongation and triangularity, the Doublet III upgrade machine -- BIG DEE -- is particularly suited to study the possibility of very high B. The authors have done computations to determine ideal MHD B limits for various plasma shapes and elongations in BIG DEE. They have determined that for q at the plasma surface greater than 2, B is limited by the ballooning mode if the wall is reasonably close to the plasma surface (d/a < 1.5 where d and a are the wall and plasma radii respectively). On the other hand, for q at the plasma surface less than 2, the n=1 external kink is unstable even with a wall close by. Thus, relevant values of limiting B can be obtained by assuming that the external kink limits the value of q at the limiter to a value greater than 2 and that the ballooning modes limit B. Under this assumption, a relevant B limit for the BIG DEE would be over 18%. For such an equilibrium, the wall position necessary to stabilize the n=1 and n=2 modes is 2a and the equilibrium is stable for n=3

  11. Big data optimization recent developments and challenges

    CERN Document Server

    2016-01-01

    The main objective of this book is to provide the necessary background to work with big data by introducing some novel optimization algorithms and codes capable of working in the big data setting as well as introducing some applications in big data optimization for both academics and practitioners interested, and to benefit society, industry, academia, and government. Presenting applications in a variety of industries, this book will be useful for the researchers aiming to analyses large scale data. Several optimization algorithms for big data including convergent parallel algorithms, limited memory bundle algorithm, diagonal bundle method, convergent parallel algorithms, network analytics, and many more have been explored in this book.

  12. Traffic information computing platform for big data

    Energy Technology Data Exchange (ETDEWEB)

    Duan, Zongtao, E-mail: ztduan@chd.edu.cn; Li, Ying, E-mail: ztduan@chd.edu.cn; Zheng, Xibin, E-mail: ztduan@chd.edu.cn; Liu, Yan, E-mail: ztduan@chd.edu.cn; Dai, Jiting, E-mail: ztduan@chd.edu.cn; Kang, Jun, E-mail: ztduan@chd.edu.cn [Chang' an University School of Information Engineering, Xi' an, China and Shaanxi Engineering and Technical Research Center for Road and Traffic Detection, Xi' an (China)

    2014-10-06

    Big data environment create data conditions for improving the quality of traffic information service. The target of this article is to construct a traffic information computing platform for big data environment. Through in-depth analysis the connotation and technology characteristics of big data and traffic information service, a distributed traffic atomic information computing platform architecture is proposed. Under the big data environment, this type of traffic atomic information computing architecture helps to guarantee the traffic safety and efficient operation, more intelligent and personalized traffic information service can be used for the traffic information users.

  13. Traffic information computing platform for big data

    International Nuclear Information System (INIS)

    Duan, Zongtao; Li, Ying; Zheng, Xibin; Liu, Yan; Dai, Jiting; Kang, Jun

    2014-01-01

    Big data environment create data conditions for improving the quality of traffic information service. The target of this article is to construct a traffic information computing platform for big data environment. Through in-depth analysis the connotation and technology characteristics of big data and traffic information service, a distributed traffic atomic information computing platform architecture is proposed. Under the big data environment, this type of traffic atomic information computing architecture helps to guarantee the traffic safety and efficient operation, more intelligent and personalized traffic information service can be used for the traffic information users

  14. Big data analytics with R and Hadoop

    CERN Document Server

    Prajapati, Vignesh

    2013-01-01

    Big Data Analytics with R and Hadoop is a tutorial style book that focuses on all the powerful big data tasks that can be achieved by integrating R and Hadoop.This book is ideal for R developers who are looking for a way to perform big data analytics with Hadoop. This book is also aimed at those who know Hadoop and want to build some intelligent applications over Big data with R packages. It would be helpful if readers have basic knowledge of R.

  15. BigDataBench: a Big Data Benchmark Suite from Internet Services

    OpenAIRE

    Wang, Lei; Zhan, Jianfeng; Luo, Chunjie; Zhu, Yuqing; Yang, Qiang; He, Yongqiang; Gao, Wanling; Jia, Zhen; Shi, Yingjie; Zhang, Shujie; Zheng, Chen; Lu, Gang; Zhan, Kent; Li, Xiaona; Qiu, Bizhu

    2014-01-01

    As architecture, systems, and data management communities pay greater attention to innovative big data systems and architectures, the pressure of benchmarking and evaluating these systems rises. Considering the broad use of big data systems, big data benchmarks must include diversity of data and workloads. Most of the state-of-the-art big data benchmarking efforts target evaluating specific types of applications or system software stacks, and hence they are not qualified for serving the purpo...

  16. The faces of Big Science.

    Science.gov (United States)

    Schatz, Gottfried

    2014-06-01

    Fifty years ago, academic science was a calling with few regulations or financial rewards. Today, it is a huge enterprise confronted by a plethora of bureaucratic and political controls. This change was not triggered by specific events or decisions but reflects the explosive 'knee' in the exponential growth that science has sustained during the past three-and-a-half centuries. Coming to terms with the demands and benefits of 'Big Science' is a major challenge for today's scientific generation. Since its foundation 50 years ago, the European Molecular Biology Organization (EMBO) has been of invaluable help in meeting this challenge.

  17. Big Data and central banks

    Directory of Open Access Journals (Sweden)

    David Bholat

    2015-04-01

    Full Text Available This commentary recaps a Centre for Central Banking Studies event held at the Bank of England on 2–3 July 2014. The article covers three main points. First, it situates the Centre for Central Banking Studies event within the context of the Bank’s Strategic Plan and initiatives. Second, it summarises and reflects on major themes from the event. Third, the article links central banks’ emerging interest in Big Data approaches with their broader uptake by other economic agents.

  18. Inhomogeneous Big Bang Nucleosynthesis Revisited

    OpenAIRE

    Lara, J. F.; Kajino, T.; Mathews, G. J.

    2006-01-01

    We reanalyze the allowed parameters for inhomogeneous big bang nucleosynthesis in light of the WMAP constraints on the baryon-to-photon ratio and a recent measurement which has set the neutron lifetime to be 878.5 +/- 0.7 +/- 0.3 seconds. For a set baryon-to-photon ratio the new lifetime reduces the mass fraction of He4 by 0.0015 but does not significantly change the abundances of other isotopes. This enlarges the region of concordance between He4 and deuterium in the parameter space of the b...

  19. BIG GEO DATA MANAGEMENT: AN EXPLORATION WITH SOCIAL MEDIA AND TELECOMMUNICATIONS OPEN DATA

    Directory of Open Access Journals (Sweden)

    C. Arias Munoz

    2016-06-01

    Full Text Available The term Big Data has been recently used to define big, highly varied, complex data sets, which are created and updated at a high speed and require faster processing, namely, a reduced time to filter and analyse relevant data. These data is also increasingly becoming Open Data (data that can be freely distributed made public by the government, agencies, private enterprises and among others. There are at least two issues that can obstruct the availability and use of Open Big Datasets: Firstly, the gathering and geoprocessing of these datasets are very computationally intensive; hence, it is necessary to integrate high-performance solutions, preferably internet based, to achieve the goals. Secondly, the problems of heterogeneity and inconsistency in geospatial data are well known and affect the data integration process, but is particularly problematic for Big Geo Data. Therefore, Big Geo Data integration will be one of the most challenging issues to solve. With these applications, we demonstrate that is possible to provide processed Big Geo Data to common users, using open geospatial standards and technologies. NoSQL databases like MongoDB and frameworks like RASDAMAN could offer different functionalities that facilitate working with larger volumes and more heterogeneous geospatial data sources.

  20. Big Geo Data Management: AN Exploration with Social Media and Telecommunications Open Data

    Science.gov (United States)

    Arias Munoz, C.; Brovelli, M. A.; Corti, S.; Zamboni, G.

    2016-06-01

    The term Big Data has been recently used to define big, highly varied, complex data sets, which are created and updated at a high speed and require faster processing, namely, a reduced time to filter and analyse relevant data. These data is also increasingly becoming Open Data (data that can be freely distributed) made public by the government, agencies, private enterprises and among others. There are at least two issues that can obstruct the availability and use of Open Big Datasets: Firstly, the gathering and geoprocessing of these datasets are very computationally intensive; hence, it is necessary to integrate high-performance solutions, preferably internet based, to achieve the goals. Secondly, the problems of heterogeneity and inconsistency in geospatial data are well known and affect the data integration process, but is particularly problematic for Big Geo Data. Therefore, Big Geo Data integration will be one of the most challenging issues to solve. With these applications, we demonstrate that is possible to provide processed Big Geo Data to common users, using open geospatial standards and technologies. NoSQL databases like MongoDB and frameworks like RASDAMAN could offer different functionalities that facilitate working with larger volumes and more heterogeneous geospatial data sources.

  1. Challenges and potential solutions for big data implementations in developing countries.

    Science.gov (United States)

    Luna, D; Mayan, J C; García, M J; Almerares, A A; Househ, M

    2014-08-15

    The volume of data, the velocity with which they are generated, and their variety and lack of structure hinder their use. This creates the need to change the way information is captured, stored, processed, and analyzed, leading to the paradigm shift called Big Data. To describe the challenges and possible solutions for developing countries when implementing Big Data projects in the health sector. A non-systematic review of the literature was performed in PubMed and Google Scholar. The following keywords were used: "big data", "developing countries", "data mining", "health information systems", and "computing methodologies". A thematic review of selected articles was performed. There are challenges when implementing any Big Data program including exponential growth of data, special infrastructure needs, need for a trained workforce, need to agree on interoperability standards, privacy and security issues, and the need to include people, processes, and policies to ensure their adoption. Developing countries have particular characteristics that hinder further development of these projects. The advent of Big Data promises great opportunities for the healthcare field. In this article, we attempt to describe the challenges developing countries would face and enumerate the options to be used to achieve successful implementations of Big Data programs.

  2. SIDELOADING – INGESTION OF LARGE POINT CLOUDS INTO THE APACHE SPARK BIG DATA ENGINE

    Directory of Open Access Journals (Sweden)

    J. Boehm

    2016-06-01

    Full Text Available In the geospatial domain we have now reached the point where data volumes we handle have clearly grown beyond the capacity of most desktop computers. This is particularly true in the area of point cloud processing. It is therefore naturally lucrative to explore established big data frameworks for big geospatial data. The very first hurdle is the import of geospatial data into big data frameworks, commonly referred to as data ingestion. Geospatial data is typically encoded in specialised binary file formats, which are not naturally supported by the existing big data frameworks. Instead such file formats are supported by software libraries that are restricted to single CPU execution. We present an approach that allows the use of existing point cloud file format libraries on the Apache Spark big data framework. We demonstrate the ingestion of large volumes of point cloud data into a compute cluster. The approach uses a map function to distribute the data ingestion across the nodes of a cluster. We test the capabilities of the proposed method to load billions of points into a commodity hardware compute cluster and we discuss the implications on scalability and performance. The performance is benchmarked against an existing native Apache Spark data import implementation.

  3. Sideloading - Ingestion of Large Point Clouds Into the Apache Spark Big Data Engine

    Science.gov (United States)

    Boehm, J.; Liu, K.; Alis, C.

    2016-06-01

    In the geospatial domain we have now reached the point where data volumes we handle have clearly grown beyond the capacity of most desktop computers. This is particularly true in the area of point cloud processing. It is therefore naturally lucrative to explore established big data frameworks for big geospatial data. The very first hurdle is the import of geospatial data into big data frameworks, commonly referred to as data ingestion. Geospatial data is typically encoded in specialised binary file formats, which are not naturally supported by the existing big data frameworks. Instead such file formats are supported by software libraries that are restricted to single CPU execution. We present an approach that allows the use of existing point cloud file format libraries on the Apache Spark big data framework. We demonstrate the ingestion of large volumes of point cloud data into a compute cluster. The approach uses a map function to distribute the data ingestion across the nodes of a cluster. We test the capabilities of the proposed method to load billions of points into a commodity hardware compute cluster and we discuss the implications on scalability and performance. The performance is benchmarked against an existing native Apache Spark data import implementation.

  4. Big data - smart health strategies. Findings from the yearbook 2014 special theme.

    Science.gov (United States)

    Koutkias, V; Thiessard, F

    2014-08-15

    To select best papers published in 2013 in the field of big data and smart health strategies, and summarize outstanding research efforts. A systematic search was performed using two major bibliographic databases for relevant journal papers. The references obtained were reviewed in a two-stage process, starting with a blinded review performed by the two section editors, and followed by a peer review process operated by external reviewers recognized as experts in the field. The complete review process selected four best papers, illustrating various aspects of the special theme, among them: (a) using large volumes of unstructured data and, specifically, clinical notes from Electronic Health Records (EHRs) for pharmacovigilance; (b) knowledge discovery via querying large volumes of complex (both structured and unstructured) biological data using big data technologies and relevant tools; (c) methodologies for applying cloud computing and big data technologies in the field of genomics, and (d) system architectures enabling high-performance access to and processing of large datasets extracted from EHRs. The potential of big data in biomedicine has been pinpointed in various viewpoint papers and editorials. The review of current scientific literature illustrated a variety of interesting methods and applications in the field, but still the promises exceed the current outcomes. As we are getting closer towards a solid foundation with respect to common understanding of relevant concepts and technical aspects, and the use of standardized technologies and tools, we can anticipate to reach the potential that big data offer for personalized medicine and smart health strategies in the near future.

  5. Earth Science Data Analysis in the Era of Big Data

    Science.gov (United States)

    Kuo, K.-S.; Clune, T. L.; Ramachandran, R.

    2014-01-01

    Anyone with even a cursory interest in information technology cannot help but recognize that "Big Data" is one of the most fashionable catchphrases of late. From accurate voice and facial recognition, language translation, and airfare prediction and comparison, to monitoring the real-time spread of flu, Big Data techniques have been applied to many seemingly intractable problems with spectacular successes. They appear to be a rewarding way to approach many currently unsolved problems. Few fields of research can claim a longer history with problems involving voluminous data than Earth science. The problems we are facing today with our Earth's future are more complex and carry potentially graver consequences than the examples given above. How has our climate changed? Beside natural variations, what is causing these changes? What are the processes involved and through what mechanisms are these connected? How will they impact life as we know it? In attempts to answer these questions, we have resorted to observations and numerical simulations with ever-finer resolutions, which continue to feed the "data deluge." Plausibly, many Earth scientists are wondering: How will Big Data technologies benefit Earth science research? As an example from the global water cycle, one subdomain among many in Earth science, how would these technologies accelerate the analysis of decades of global precipitation to ascertain the changes in its characteristics, to validate these changes in predictive climate models, and to infer the implications of these changes to ecosystems, economies, and public health? Earth science researchers need a viable way to harness the power of Big Data technologies to analyze large volumes and varieties of data with velocity and veracity. Beyond providing speedy data analysis capabilities, Big Data technologies can also play a crucial, albeit indirect, role in boosting scientific productivity by facilitating effective collaboration within an analysis environment

  6. Comparative validity of brief to medium-length Big Five and Big Six personality questionnaires

    NARCIS (Netherlands)

    Thalmayer, A.G.; Saucier, G.; Eigenhuis, A.

    2011-01-01

    A general consensus on the Big Five model of personality attributes has been highly generative for the field of personality psychology. Many important psychological and life outcome correlates with Big Five trait dimensions have been established. But researchers must choose between multiple Big Five

  7. Comparative Validity of Brief to Medium-Length Big Five and Big Six Personality Questionnaires

    Science.gov (United States)

    Thalmayer, Amber Gayle; Saucier, Gerard; Eigenhuis, Annemarie

    2011-01-01

    A general consensus on the Big Five model of personality attributes has been highly generative for the field of personality psychology. Many important psychological and life outcome correlates with Big Five trait dimensions have been established. But researchers must choose between multiple Big Five inventories when conducting a study and are…

  8. Big Data en surveillance, deel 1 : Definities en discussies omtrent Big Data

    NARCIS (Netherlands)

    Timan, Tjerk

    2016-01-01

    Naar aanleiding van een (vrij kort) college over surveillance en Big Data, werd me gevraagd iets dieper in te gaan op het thema, definities en verschillende vraagstukken die te maken hebben met big data. In dit eerste deel zal ik proberen e.e.a. uiteen te zetten betreft Big Data theorie en

  9. 77 FR 27245 - Big Stone National Wildlife Refuge, Big Stone and Lac Qui Parle Counties, MN

    Science.gov (United States)

    2012-05-09

    ... DEPARTMENT OF THE INTERIOR Fish and Wildlife Service [FWS-R3-R-2012-N069; FXRS1265030000S3-123-FF03R06000] Big Stone National Wildlife Refuge, Big Stone and Lac Qui Parle Counties, MN AGENCY: Fish and... plan (CCP) and environmental assessment (EA) for Big Stone National Wildlife Refuge (Refuge, NWR) for...

  10. Big game hunting practices, meanings, motivations and constraints: a survey of Oregon big game hunters

    Science.gov (United States)

    Suresh K. Shrestha; Robert C. Burns

    2012-01-01

    We conducted a self-administered mail survey in September 2009 with randomly selected Oregon hunters who had purchased big game hunting licenses/tags for the 2008 hunting season. Survey questions explored hunting practices, the meanings of and motivations for big game hunting, the constraints to big game hunting participation, and the effects of age, years of hunting...

  11. Big Data in Science and Healthcare: A Review of Recent Literature and Perspectives

    Science.gov (United States)

    Miron-Shatz, T.; Lau, A. Y. S.; Paton, C.

    2014-01-01

    Summary Objectives As technology continues to evolve and rise in various industries, such as healthcare, science, education, and gaming, a sophisticated concept known as Big Data is surfacing. The concept of analytics aims to understand data. We set out to portray and discuss perspectives of the evolving use of Big Data in science and healthcare and, to examine some of the opportunities and challenges. Methods A literature review was conducted to highlight the implications associated with the use of Big Data in scientific research and healthcare innovations, both on a large and small scale. Results Scientists and health-care providers may learn from one another when it comes to understanding the value of Big Data and analytics. Small data, derived by patients and consumers, also requires analytics to become actionable. Connectivism provides a framework for the use of Big Data and analytics in the areas of science and healthcare. This theory assists individuals to recognize and synthesize how human connections are driving the increase in data. Despite the volume and velocity of Big Data, it is truly about technology connecting humans and assisting them to construct knowledge in new ways. Concluding Thoughts The concept of Big Data and associated analytics are to be taken seriously when approaching the use of vast volumes of both structured and unstructured data in science and health-care. Future exploration of issues surrounding data privacy, confidentiality, and education are needed. A greater focus on data from social media, the quantified self-movement, and the application of analytics to “small data” would also be useful. PMID:25123717

  12. An analysis of cross-sectional differences in big and non-big public accounting firms' audit programs

    NARCIS (Netherlands)

    Blokdijk, J.H. (Hans); Drieenhuizen, F.; Stein, M.T.; Simunic, D.A.

    2006-01-01

    A significant body of prior research has shown that audits by the Big 5 (now Big 4) public accounting firms are quality differentiated relative to non-Big 5 audits. This result can be derived analytically by assuming that Big 5 and non-Big 5 firms face different loss functions for "audit failures"

  13. Baryon symmetric big bang cosmology

    Science.gov (United States)

    Stecker, F. W.

    1978-01-01

    Both the quantum theory and Einsteins theory of special relativity lead to the supposition that matter and antimatter were produced in equal quantities during the big bang. It is noted that local matter/antimatter asymmetries may be reconciled with universal symmetry by assuming (1) a slight imbalance of matter over antimatter in the early universe, annihilation, and a subsequent remainder of matter; (2) localized regions of excess for one or the other type of matter as an initial condition; and (3) an extremely dense, high temperature state with zero net baryon number; i.e., matter/antimatter symmetry. Attention is given to the third assumption, which is the simplest and the most in keeping with current knowledge of the cosmos, especially as pertains the universality of 3 K background radiation. Mechanisms of galaxy formation are discussed, whereby matter and antimatter might have collided and annihilated each other, or have coexisted (and continue to coexist) at vast distances. It is pointed out that baryon symmetric big bang cosmology could probably be proved if an antinucleus could be detected in cosmic radiation.

  14. Georges et le big bang

    CERN Document Server

    Hawking, Lucy; Parsons, Gary

    2011-01-01

    Georges et Annie, sa meilleure amie, sont sur le point d'assister à l'une des plus importantes expériences scientifiques de tous les temps : explorer les premiers instants de l'Univers, le Big Bang ! Grâce à Cosmos, leur super ordinateur, et au Grand Collisionneur de hadrons créé par Éric, le père d'Annie, ils vont enfin pouvoir répondre à cette question essentielle : pourquoi existons nous ? Mais Georges et Annie découvrent qu'un complot diabolique se trame. Pire, c'est toute la recherche scientifique qui est en péril ! Entraîné dans d'incroyables aventures, Georges ira jusqu'aux confins de la galaxie pour sauver ses amis...Une plongée passionnante au coeur du Big Bang. Les toutes dernières théories de Stephen Hawking et des plus grands scientifiques actuels.

  15. Astronomical Surveys and Big Data

    Directory of Open Access Journals (Sweden)

    Mickaelian Areg M.

    2016-03-01

    Full Text Available Recent all-sky and large-area astronomical surveys and their catalogued data over the whole range of electromagnetic spectrum, from γ-rays to radio waves, are reviewed, including such as Fermi-GLAST and INTEGRAL in γ-ray, ROSAT, XMM and Chandra in X-ray, GALEX in UV, SDSS and several POSS I and POSS II-based catalogues (APM, MAPS, USNO, GSC in the optical range, 2MASS in NIR, WISE and AKARI IRC in MIR, IRAS and AKARI FIS in FIR, NVSS and FIRST in radio range, and many others, as well as the most important surveys giving optical images (DSS I and II, SDSS, etc., proper motions (Tycho, USNO, Gaia, variability (GCVS, NSVS, ASAS, Catalina, Pan-STARRS, and spectroscopic data (FBS, SBS, Case, HQS, HES, SDSS, CALIFA, GAMA. An overall understanding of the coverage along the whole wavelength range and comparisons between various surveys are given: galaxy redshift surveys, QSO/AGN, radio, Galactic structure, and Dark Energy surveys. Astronomy has entered the Big Data era, with Astrophysical Virtual Observatories and Computational Astrophysics playing an important role in using and analyzing big data for new discoveries.

  16. Big data in oncologic imaging.

    Science.gov (United States)

    Regge, Daniele; Mazzetti, Simone; Giannini, Valentina; Bracco, Christian; Stasi, Michele

    2017-06-01

    Cancer is a complex disease and unfortunately understanding how the components of the cancer system work does not help understand the behavior of the system as a whole. In the words of the Greek philosopher Aristotle "the whole is greater than the sum of parts." To date, thanks to improved information technology infrastructures, it is possible to store data from each single cancer patient, including clinical data, medical images, laboratory tests, and pathological and genomic information. Indeed, medical archive storage constitutes approximately one-third of total global storage demand and a large part of the data are in the form of medical images. The opportunity is now to draw insight on the whole to the benefit of each individual patient. In the oncologic patient, big data analysis is at the beginning but several useful applications can be envisaged including development of imaging biomarkers to predict disease outcome, assessing the risk of X-ray dose exposure or of renal damage following the administration of contrast agents, and tracking and optimizing patient workflow. The aim of this review is to present current evidence of how big data derived from medical images may impact on the diagnostic pathway of the oncologic patient.

  17. Leveraging Mobile Network Big Data for Developmental Policy ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    Some argue that big data and big data users offer advantages to generate evidence. ... Supported by IDRC, this research focused on transportation planning in urban ... Using mobile network big data for land use classification CPRsouth 2015.

  18. excess molar volumes, and refractive index of binary mixtures

    African Journals Online (AJOL)

    Preferred Customer

    because (a) water molecules have hydroxyl group which can make stronger hydrogen bonding than methanol and (b) water molecules and glycerol have suitable kinetic energy for bulk volumes at high temperature. Thus, the mixture of glycerol + water have big excess molar volume than methanol. The hydrogen bonding ...

  19. A New Look at Big History

    Science.gov (United States)

    Hawkey, Kate

    2014-01-01

    The article sets out a "big history" which resonates with the priorities of our own time. A globalizing world calls for new spacial scales to underpin what the history curriculum addresses, "big history" calls for new temporal scales, while concern over climate change calls for a new look at subject boundaries. The article…

  20. A little big history of Tiananmen

    NARCIS (Netherlands)

    Quaedackers, E.; Grinin, L.E.; Korotayev, A.V.; Rodrigue, B.H.

    2011-01-01

    This contribution aims at demonstrating the usefulness of studying small-scale subjects such as Tiananmen, or the Gate of Heavenly Peace, in Beijing - from a Big History perspective. By studying such a ‘little big history’ of Tiananmen, previously overlooked yet fundamental explanations for why

  1. What is beyond the big five?

    Science.gov (United States)

    Saucier, G; Goldberg, L R

    1998-08-01

    Previous investigators have proposed that various kinds of person-descriptive content--such as differences in attitudes or values, in sheer evaluation, in attractiveness, or in height and girth--are not adequately captured by the Big Five Model. We report on a rather exhaustive search for reliable sources of Big Five-independent variation in data from person-descriptive adjectives. Fifty-three candidate clusters were developed in a college sample using diverse approaches and sources. In a nonstudent adult sample, clusters were evaluated with respect to a minimax criterion: minimum multiple correlation with factors from Big Five markers and maximum reliability. The most clearly Big Five-independent clusters referred to Height, Girth, Religiousness, Employment Status, Youthfulness and Negative Valence (or low-base-rate attributes). Clusters referring to Fashionableness, Sensuality/Seductiveness, Beauty, Masculinity, Frugality, Humor, Wealth, Prejudice, Folksiness, Cunning, and Luck appeared to be potentially beyond the Big Five, although each of these clusters demonstrated Big Five multiple correlations of .30 to .45, and at least one correlation of .20 and over with a Big Five factor. Of all these content areas, Religiousness, Negative Valence, and the various aspects of Attractiveness were found to be represented by a substantial number of distinct, common adjectives. Results suggest directions for supplementing the Big Five when one wishes to extend variable selection outside the domain of personality traits as conventionally defined.

  2. Big Data Analytics and Its Applications

    Directory of Open Access Journals (Sweden)

    Mashooque A. Memon

    2017-10-01

    Full Text Available The term, Big Data, has been authored to refer to the extensive heave of data that can't be managed by traditional data handling methods or techniques. The field of Big Data plays an indispensable role in various fields, such as agriculture, banking, data mining, education, chemistry, finance, cloud computing, marketing, health care stocks. Big data analytics is the method for looking at big data to reveal hidden patterns, incomprehensible relationship and other important data that can be utilize to resolve on enhanced decisions. There has been a perpetually expanding interest for big data because of its fast development and since it covers different areas of applications. Apache Hadoop open source technology created in Java and keeps running on Linux working framework was used. The primary commitment of this exploration is to display an effective and free solution for big data application in a distributed environment, with its advantages and indicating its easy use. Later on, there emerge to be a required for an analytical review of new developments in the big data technology. Healthcare is one of the best concerns of the world. Big data in healthcare imply to electronic health data sets that are identified with patient healthcare and prosperity. Data in the healthcare area is developing past managing limit of the healthcare associations and is relied upon to increment fundamentally in the coming years.

  3. Big data and software defined networks

    CERN Document Server

    Taheri, Javid

    2018-01-01

    Big Data Analytics and Software Defined Networking (SDN) are helping to drive the management of data usage of the extraordinary increase of computer processing power provided by Cloud Data Centres (CDCs). This new book investigates areas where Big-Data and SDN can help each other in delivering more efficient services.

  4. Big Science and Long-tail Science

    CERN Document Server

    2008-01-01

    Jim Downing and I were privileged to be the guests of Salavtore Mele at CERN yesterday and to see the Atlas detector of the Large Hadron Collider . This is a wow experience - although I knew it was big, I hadnt realised how big.

  5. The Death of the Big Men

    DEFF Research Database (Denmark)

    Martin, Keir

    2010-01-01

    Recently Tolai people og Papua New Guinea have adopted the term 'Big Shot' to decribe an emerging post-colonial political elite. The mergence of the term is a negative moral evaluation of new social possibilities that have arisen as a consequence of the Big Shots' privileged position within a glo...

  6. An embedding for the big bang

    Science.gov (United States)

    Wesson, Paul S.

    1994-01-01

    A cosmological model is given that has good physical properties for the early and late universe but is a hypersurface in a flat five-dimensional manifold. The big bang can therefore be regarded as an effect of a choice of coordinates in a truncated higher-dimensional geometry. Thus the big bang is in some sense a geometrical illusion.

  7. Probing the pre-big bang universe

    International Nuclear Information System (INIS)

    Veneziano, G.

    2000-01-01

    Superstring theory suggests a new cosmology whereby a long inflationary phase preceded a non singular big bang-like event. After discussing how pre-big bang inflation naturally arises from an almost trivial initial state of the Universe, I will describe how present or near-future experiments can provide sensitive probes of how the Universe behaved in the pre-bang era

  8. Starting Small, Thinking Big - Continuum Magazine | NREL

    Science.gov (United States)

    , Thinking Big Stories NREL Helps Agencies Target New Federal Sustainability Goals Student Engagements Help solar power in the territory. Photo by Don Buchanan, VIEO Starting Small, Thinking Big NREL helps have used these actions to optimize that energy use.'" NREL's cross-organizational work supports

  9. Exploiting big data for critical care research.

    Science.gov (United States)

    Docherty, Annemarie B; Lone, Nazir I

    2015-10-01

    Over recent years the digitalization, collection and storage of vast quantities of data, in combination with advances in data science, has opened up a new era of big data. In this review, we define big data, identify examples of critical care research using big data, discuss the limitations and ethical concerns of using these large datasets and finally consider scope for future research. Big data refers to datasets whose size, complexity and dynamic nature are beyond the scope of traditional data collection and analysis methods. The potential benefits to critical care are significant, with faster progress in improving health and better value for money. Although not replacing clinical trials, big data can improve their design and advance the field of precision medicine. However, there are limitations to analysing big data using observational methods. In addition, there are ethical concerns regarding maintaining confidentiality of patients who contribute to these datasets. Big data have the potential to improve medical care and reduce costs, both by individualizing medicine, and bringing together multiple sources of data about individual patients. As big data become increasingly mainstream, it will be important to maintain public confidence by safeguarding data security, governance and confidentiality.

  10. Practice variation in Big-4 transparency reports

    NARCIS (Netherlands)

    Girdhar, Sakshi; Jeppesen, K.K.

    2018-01-01

    Purpose The purpose of this paper is to examine the transparency reports published by the Big-4 public accounting firms in the UK, Germany and Denmark to understand the determinants of their content within the networks of big accounting firms. Design/methodology/approach The study draws on a

  11. Big data analysis for smart farming

    NARCIS (Netherlands)

    Kempenaar, C.; Lokhorst, C.; Bleumer, E.J.B.; Veerkamp, R.F.; Been, Th.; Evert, van F.K.; Boogaardt, M.J.; Ge, L.; Wolfert, J.; Verdouw, C.N.; Bekkum, van Michael; Feldbrugge, L.; Verhoosel, Jack P.C.; Waaij, B.D.; Persie, van M.; Noorbergen, H.

    2016-01-01

    In this report we describe results of a one-year TO2 institutes project on the development of big data technologies within the milk production chain. The goal of this project is to ‘create’ an integration platform for big data analysis for smart farming and to develop a show case. This includes both

  12. Big Data as a Driver for Clinical Decision Support Systems: A Learning Health Systems Perspective

    Directory of Open Access Journals (Sweden)

    Arianna Dagliati

    2018-05-01

    Full Text Available Big data technologies are nowadays providing health care with powerful instruments to gather and analyze large volumes of heterogeneous data collected for different purposes, including clinical care, administration, and research. This makes possible to design IT infrastructures that favor the implementation of the so-called “Learning Healthcare System Cycle,” where healthcare practice and research are part of a unique and synergic process. In this paper we highlight how “Big Data enabled” integrated data collections may support clinical decision-making together with biomedical research. Two effective implementations are reported, concerning decision support in Diabetes and in Inherited Arrhythmogenic Diseases.

  13. How Big Data Fast Tracked Human Mobility Research and the Lessons for Animal Movement Ecology

    Directory of Open Access Journals (Sweden)

    Michele Thums

    2018-02-01

    Full Text Available The rise of the internet coupled with technological innovations such as smartphones have generated massive volumes of geo-referenced data (big data on human mobility. This has allowed the number of studies of human mobility to rapidly overtake those of animal movement. Today, telemetry studies of animals are also approaching big data status. Here, we review recent advances in studies of human mobility and identify the opportunities they present for advancing our understanding of animal movement. We describe key analytical techniques, potential bottlenecks and a roadmap for progress toward a synthesis of movement patterns of wild animals.

  14. How Big Data Fast Tracked Human Mobility Research and the Lessons for Animal Movement Ecology

    KAUST Repository

    Thums, Michele; Ferná ndez-Gracia, Juan; Sequeira, Ana M. M.; Eguí luz, Ví ctor M.; Duarte, Carlos M.; Meekan, Mark G.

    2018-01-01

    The rise of the internet coupled with technological innovations such as smartphones have generated massive volumes of geo-referenced data (big data) on human mobility. This has allowed the number of studies of human mobility to rapidly overtake those of animal movement. Today, telemetry studies of animals are also approaching big data status. Here, we review recent advances in studies of human mobility and identify the opportunities they present for advancing our understanding of animal movement. We describe key analytical techniques, potential bottlenecks and a roadmap for progress toward a synthesis of movement patterns of wild animals.

  15. Big data in pharmacy practice: current use, challenges, and the future.

    Science.gov (United States)

    Ma, Carolyn; Smith, Helen Wong; Chu, Cherie; Juarez, Deborah T

    2015-01-01

    Pharmacy informatics is defined as the use and integration of data, information, knowledge, technology, and automation in the medication-use process for the purpose of improving health outcomes. The term "big data" has been coined and is often defined in three V's: volume, velocity, and variety. This paper describes three major areas in which pharmacy utilizes big data, including: 1) informed decision making (clinical pathways and clinical practice guidelines); 2) improved care delivery in health care settings such as hospitals and community pharmacy practice settings; and 3) quality performance measurement for the Centers for Medicare and Medicaid and medication management activities such as tracking medication adherence and medication reconciliation.

  16. 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.

  17. How Big Data Fast Tracked Human Mobility Research and the Lessons for Animal Movement Ecology

    KAUST Repository

    Thums, Michele

    2018-02-13

    The rise of the internet coupled with technological innovations such as smartphones have generated massive volumes of geo-referenced data (big data) on human mobility. This has allowed the number of studies of human mobility to rapidly overtake those of animal movement. Today, telemetry studies of animals are also approaching big data status. Here, we review recent advances in studies of human mobility and identify the opportunities they present for advancing our understanding of animal movement. We describe key analytical techniques, potential bottlenecks and a roadmap for progress toward a synthesis of movement patterns of wild animals.

  18. Cloud Based Big Data Infrastructure: Architectural Components and Automated Provisioning

    OpenAIRE

    Demchenko, Yuri; Turkmen, Fatih; Blanchet, Christophe; Loomis, Charles; Laat, Caees de

    2016-01-01

    This paper describes the general architecture and functional components of the cloud based Big Data Infrastructure (BDI). The proposed BDI architecture is based on the analysis of the emerging Big Data and data intensive technologies and supported by the definition of the Big Data Architecture Framework (BDAF) that defines the following components of the Big Data technologies: Big Data definition, Data Management including data lifecycle and data structures, Big Data Infrastructure (generical...

  19. Big Data in radiation therapy: challenges and opportunities.

    Science.gov (United States)

    Lustberg, Tim; van Soest, Johan; Jochems, Arthur; Deist, Timo; van Wijk, Yvonka; Walsh, Sean; Lambin, Philippe; Dekker, Andre

    2017-01-01

    Data collected and generated by radiation oncology can be classified by the Volume, Variety, Velocity and Veracity (4Vs) of Big Data because they are spread across different care providers and not easily shared owing to patient privacy protection. The magnitude of the 4Vs is substantial in oncology, especially owing to imaging modalities and unclear data definitions. To create useful models ideally all data of all care providers are understood and learned from; however, this presents challenges in the guise of poor data quality, patient privacy concerns, geographical spread, interoperability and large volume. In radiation oncology, there are many efforts to collect data for research and innovation purposes. Clinical trials are the gold standard when proving any hypothesis that directly affects the patient. Collecting data in registries with strict predefined rules is also a common approach to find answers. A third approach is to develop data stores that can be used by modern machine learning techniques to provide new insights or answer hypotheses. We believe all three approaches have their strengths and weaknesses, but they should all strive to create Findable, Accessible, Interoperable, Reusable (FAIR) data. To learn from these data, we need distributed learning techniques, sending machine learning algorithms to FAIR data stores around the world, learning from trial data, registries and routine clinical data rather than trying to centralize all data. To improve and personalize medicine, rapid learning platforms must be able to process FAIR "Big Data" to evaluate current clinical practice and to guide further innovation.

  20. The ethics of biomedical big data

    CERN Document Server

    Mittelstadt, Brent Daniel

    2016-01-01

    This book presents cutting edge research on the new ethical challenges posed by biomedical Big Data technologies and practices. ‘Biomedical Big Data’ refers to the analysis of aggregated, very large datasets to improve medical knowledge and clinical care. The book describes the ethical problems posed by aggregation of biomedical datasets and re-use/re-purposing of data, in areas such as privacy, consent, professionalism, power relationships, and ethical governance of Big Data platforms. Approaches and methods are discussed that can be used to address these problems to achieve the appropriate balance between the social goods of biomedical Big Data research and the safety and privacy of individuals. Seventeen original contributions analyse the ethical, social and related policy implications of the analysis and curation of biomedical Big Data, written by leading experts in the areas of biomedical research, medical and technology ethics, privacy, governance and data protection. The book advances our understan...

  1. 2nd INNS Conference on Big Data

    CERN Document Server

    Manolopoulos, Yannis; Iliadis, Lazaros; Roy, Asim; Vellasco, Marley

    2017-01-01

    The book offers a timely snapshot of neural network technologies as a significant component of big data analytics platforms. It promotes new advances and research directions in efficient and innovative algorithmic approaches to analyzing big data (e.g. deep networks, nature-inspired and brain-inspired algorithms); implementations on different computing platforms (e.g. neuromorphic, graphics processing units (GPUs), clouds, clusters); and big data analytics applications to solve real-world problems (e.g. weather prediction, transportation, energy management). The book, which reports on the second edition of the INNS Conference on Big Data, held on October 23–25, 2016, in Thessaloniki, Greece, depicts an interesting collaborative adventure of neural networks with big data and other learning technologies.

  2. Practice Variation in Big-4 Transparency Reports

    DEFF Research Database (Denmark)

    Girdhar, Sakshi; Klarskov Jeppesen, Kim

    2018-01-01

    Purpose: The purpose of this paper is to examine the transparency reports published by the Big-4 public accounting firms in the UK, Germany and Denmark to understand the determinants of their content within the networks of big accounting firms. Design/methodology/approach: The study draws...... on a qualitative research approach, in which the content of transparency reports is analyzed and semi-structured interviews are conducted with key people from the Big-4 firms who are responsible for developing the transparency reports. Findings: The findings show that the content of transparency reports...... is inconsistent and the transparency reporting practice is not uniform within the Big-4 networks. Differences were found in the way in which the transparency reporting practices are coordinated globally by the respective central governing bodies of the Big-4. The content of the transparency reports...

  3. Ethics and Epistemology in Big Data Research.

    Science.gov (United States)

    Lipworth, Wendy; Mason, Paul H; Kerridge, Ian; Ioannidis, John P A

    2017-12-01

    Biomedical innovation and translation are increasingly emphasizing research using "big data." The hope is that big data methods will both speed up research and make its results more applicable to "real-world" patients and health services. While big data research has been embraced by scientists, politicians, industry, and the public, numerous ethical, organizational, and technical/methodological concerns have also been raised. With respect to technical and methodological concerns, there is a view that these will be resolved through sophisticated information technologies, predictive algorithms, and data analysis techniques. While such advances will likely go some way towards resolving technical and methodological issues, we believe that the epistemological issues raised by big data research have important ethical implications and raise questions about the very possibility of big data research achieving its goals.

  4. The Big bang and the Quantum

    Science.gov (United States)

    Ashtekar, Abhay

    2010-06-01

    General relativity predicts that space-time comes to an end and physics comes to a halt at the big-bang. Recent developments in loop quantum cosmology have shown that these predictions cannot be trusted. Quantum geometry effects can resolve singularities, thereby opening new vistas. Examples are: The big bang is replaced by a quantum bounce; the `horizon problem' disappears; immediately after the big bounce, there is a super-inflationary phase with its own phenomenological ramifications; and, in presence of a standard inflation potential, initial conditions are naturally set for a long, slow roll inflation independently of what happens in the pre-big bang branch. As in my talk at the conference, I will first discuss the foundational issues and then the implications of the new Planck scale physics near the Big Bang.

  5. Intelligent search in Big Data

    Science.gov (United States)

    Birialtsev, E.; Bukharaev, N.; Gusenkov, A.

    2017-10-01

    An approach to data integration, aimed on the ontology-based intelligent search in Big Data, is considered in the case when information objects are represented in the form of relational databases (RDB), structurally marked by their schemes. The source of information for constructing an ontology and, later on, the organization of the search are texts in natural language, treated as semi-structured data. For the RDBs, these are comments on the names of tables and their attributes. Formal definition of RDBs integration model in terms of ontologies is given. Within framework of the model universal RDB representation ontology, oil production subject domain ontology and linguistic thesaurus of subject domain language are built. Technique of automatic SQL queries generation for subject domain specialists is proposed. On the base of it, information system for TATNEFT oil-producing company RDBs was implemented. Exploitation of the system showed good relevance with majority of queries.

  6. Big Data in Transport Geography

    DEFF Research Database (Denmark)

    Reinau, Kristian Hegner; Agerholm, Niels; Lahrmann, Harry Spaabæk

    for studies that explicitly compare the quality of this new type of data to traditional data sources. With the current focus on Big Data in the transport field, public transport planners are increasingly looking towards smart card data to analyze and optimize flows of passengers. However, in many cases...... it is not all public transport passengers in a city, region or country with a smart card system that uses the system, and in such cases, it is important to know what biases smart card data has in relation to giving a complete view upon passenger flows. This paper therefore analyses the quality and biases...... of smart card data in Denmark, where public transport passengers may use a smart card, may pay with cash for individual trips or may hold a season ticket for a certain route. By analyzing smart card data collected in Denmark in relation to data on sales of cash tickets, sales of season tickets, manual...

  7. Was the Big Bang hot?

    Science.gov (United States)

    Wright, E. L.

    1983-01-01

    Techniques for verifying the spectrum defined by Woody and Richards (WR, 1981), which serves as a base for dust-distorted models of the 3 K background, are discussed. WR detected a sharp deviation from the Planck curve in the 3 K background. The absolute intensity of the background may be determined by the frequency dependence of the dipole anisotropy of the background or the frequency dependence effect in galactic clusters. Both methods involve the Doppler shift; analytical formulae are defined for characterization of the dipole anisotropy. The measurement of the 30-300 GHz spectra of cold galactic dust may reveal the presence of significant amounts of needle-shaped grains, which would in turn support a theory of a cold Big Bang.

  8. Big Bang nucleosynthesis in crisis?

    International Nuclear Information System (INIS)

    Hata, N.; Scherrer, R.J.; Steigman, G.; Thomas, D.; Walker, T.P.; Bludman, S.; Langacker, P.

    1995-01-01

    A new evaluation of the constraint on the number of light neutrino species (N ν ) from big bang nucleosynthesis suggests a discrepancy between the predicted light element abundances and those inferred from observations, unless the inferred primordial 4 He abundance has been underestimated by 0.014±0.004 (1σ) or less than 10% (95% C.L.) of 3 He survives stellar processing. With the quoted systematic errors in the observed abundances and a conservative chemical evolution parametrization, the best fit to the combined data is N ν =2.1±0.3 (1σ) and the upper limit is N ν ν =3) at the 98.6% C.L. copyright 1995 The American Physical Society

  9. Internet of Things and big data technologies for next generation healthcare

    CERN Document Server

    Dey, Nilanjan; Ashour, Amira

    2017-01-01

    This comprehensive book focuses on better big-data security for healthcare organizations. Following an extensive introduction to the Internet of Things (IoT) in healthcare including challenging topics and scenarios, it offers an in-depth analysis of medical body area networks with the 5th generation of IoT communication technology along with its nanotechnology. It also describes a novel strategic framework and computationally intelligent model to measure possible security vulnerabilities in the context of e-health. Moreover, the book addresses healthcare systems that handle large volumes of data driven by patients’ records and health/personal information, including big-data-based knowledge management systems to support clinical decisions. Several of the issues faced in storing/processing big data are presented along with the available tools, technologies and algorithms to deal with those problems as well as a case study in healthcare analytics. Addressing trust, privacy, and security issues as well as the I...

  10. Big Data and Dementia: Charting the Route Ahead for Research, Ethics, and Policy.

    Science.gov (United States)

    Ienca, Marcello; Vayena, Effy; Blasimme, Alessandro

    2018-01-01

    Emerging trends in pervasive computing and medical informatics are creating the possibility for large-scale collection, sharing, aggregation and analysis of unprecedented volumes of data, a phenomenon commonly known as big data. In this contribution, we review the existing scientific literature on big data approaches to dementia, as well as commercially available mobile-based applications in this domain. Our analysis suggests that big data approaches to dementia research and care hold promise for improving current preventive and predictive models, casting light on the etiology of the disease, enabling earlier diagnosis, optimizing resource allocation, and delivering more tailored treatments to patients with specific disease trajectories. Such promissory outlook, however, has not materialized yet, and raises a number of technical, scientific, ethical, and regulatory challenges. This paper provides an assessment of these challenges and charts the route ahead for research, ethics, and policy.

  11. Big Data and Dementia: Charting the Route Ahead for Research, Ethics, and Policy

    Directory of Open Access Journals (Sweden)

    Marcello Ienca

    2018-02-01

    Full Text Available Emerging trends in pervasive computing and medical informatics are creating the possibility for large-scale collection, sharing, aggregation and analysis of unprecedented volumes of data, a phenomenon commonly known as big data. In this contribution, we review the existing scientific literature on big data approaches to dementia, as well as commercially available mobile-based applications in this domain. Our analysis suggests that big data approaches to dementia research and care hold promise for improving current preventive and predictive models, casting light on the etiology of the disease, enabling earlier diagnosis, optimizing resource allocation, and delivering more tailored treatments to patients with specific disease trajectories. Such promissory outlook, however, has not materialized yet, and raises a number of technical, scientific, ethical, and regulatory challenges. This paper provides an assessment of these challenges and charts the route ahead for research, ethics, and policy.

  12. Reflection on Quality Assurance System of Higher Vocational Education under Big Data Era

    Directory of Open Access Journals (Sweden)

    Jiang Xinlan

    2015-01-01

    Full Text Available Big data has the features like Volume, Variety, Value and Velocity. Here come the new opportunities and challenges for construction of Chinese quality assurance system of higher vocational education under big data era. There are problems in current quality assurance system of higher vocational education, such as imperfect main body, non-formation of internally and externally incorporated quality assurance system, non-scientific security standard and insufficiency in security investment. The construction of higher vocational education under big data era requires a change in the idea of quality assurance system construction to realize the multiple main bodies and multiple layers development trend for educational quality assurance system, and strengthen the construction of information platform for quality assurance system.

  13. [Big data in medicine and healthcare].

    Science.gov (United States)

    Rüping, Stefan

    2015-08-01

    Healthcare is one of the business fields with the highest Big Data potential. According to the prevailing definition, Big Data refers to the fact that data today is often too large and heterogeneous and changes too quickly to be stored, processed, and transformed into value by previous technologies. The technological trends drive Big Data: business processes are more and more executed electronically, consumers produce more and more data themselves - e.g. in social networks - and finally ever increasing digitalization. Currently, several new trends towards new data sources and innovative data analysis appear in medicine and healthcare. From the research perspective, omics-research is one clear Big Data topic. In practice, the electronic health records, free open data and the "quantified self" offer new perspectives for data analytics. Regarding analytics, significant advances have been made in the information extraction from text data, which unlocks a lot of data from clinical documentation for analytics purposes. At the same time, medicine and healthcare is lagging behind in the adoption of Big Data approaches. This can be traced to particular problems regarding data complexity and organizational, legal, and ethical challenges. The growing uptake of Big Data in general and first best-practice examples in medicine and healthcare in particular, indicate that innovative solutions will be coming. This paper gives an overview of the potentials of Big Data in medicine and healthcare.

  14. [Relevance of big data for molecular diagnostics].

    Science.gov (United States)

    Bonin-Andresen, M; Smiljanovic, B; Stuhlmüller, B; Sörensen, T; Grützkau, A; Häupl, T

    2018-04-01

    Big data analysis raises the expectation that computerized algorithms may extract new knowledge from otherwise unmanageable vast data sets. What are the algorithms behind the big data discussion? In principle, high throughput technologies in molecular research already introduced big data and the development and application of analysis tools into the field of rheumatology some 15 years ago. This includes especially omics technologies, such as genomics, transcriptomics and cytomics. Some basic methods of data analysis are provided along with the technology, however, functional analysis and interpretation requires adaptation of existing or development of new software tools. For these steps, structuring and evaluating according to the biological context is extremely important and not only a mathematical problem. This aspect has to be considered much more for molecular big data than for those analyzed in health economy or epidemiology. Molecular data are structured in a first order determined by the applied technology and present quantitative characteristics that follow the principles of their biological nature. These biological dependencies have to be integrated into software solutions, which may require networks of molecular big data of the same or even different technologies in order to achieve cross-technology confirmation. More and more extensive recording of molecular processes also in individual patients are generating personal big data and require new strategies for management in order to develop data-driven individualized interpretation concepts. With this perspective in mind, translation of information derived from molecular big data will also require new specifications for education and professional competence.

  15. The Need for a Definition of Big Data for Nursing Science: A Case Study of Disaster Preparedness

    Science.gov (United States)

    Wong, Ho Ting; Chiang, Vico Chung Lim; Choi, Kup Sze; Loke, Alice Yuen

    2016-01-01

    The rapid development of technology has made enormous volumes of data available and achievable anytime and anywhere around the world. Data scientists call this change a data era and have introduced the term “Big Data”, which has drawn the attention of nursing scholars. Nevertheless, the concept of Big Data is quite fuzzy and there is no agreement on its definition among researchers of different disciplines. Without a clear consensus on this issue, nursing scholars who are relatively new to the concept may consider Big Data to be merely a dataset of a bigger size. Having a suitable definition for nurse researchers in their context of research and practice is essential for the advancement of nursing research. In view of the need for a better understanding on what Big Data is, the aim in this paper is to explore and discuss the concept. Furthermore, an example of a Big Data research study on disaster nursing preparedness involving six million patient records is used for discussion. The example demonstrates that a Big Data analysis can be conducted from many more perspectives than would be possible in traditional sampling, and is superior to traditional sampling. Experience gained from the process of using Big Data in this study will shed light on future opportunities for conducting evidence-based nursing research to achieve competence in disaster nursing. PMID:27763525

  16. The Need for a Definition of Big Data for Nursing Science: A Case Study of Disaster Preparedness

    Directory of Open Access Journals (Sweden)

    Ho Ting Wong

    2016-10-01

    Full Text Available The rapid development of technology has made enormous volumes of data available and achievable anytime and anywhere around the world. Data scientists call this change a data era and have introduced the term “Big Data”, which has drawn the attention of nursing scholars. Nevertheless, the concept of Big Data is quite fuzzy and there is no agreement on its definition among researchers of different disciplines. Without a clear consensus on this issue, nursing scholars who are relatively new to the concept may consider Big Data to be merely a dataset of a bigger size. Having a suitable definition for nurse researchers in their context of research and practice is essential for the advancement of nursing research. In view of the need for a better understanding on what Big Data is, the aim in this paper is to explore and discuss the concept. Furthermore, an example of a Big Data research study on disaster nursing preparedness involving six million patient records is used for discussion. The example demonstrates that a Big Data analysis can be conducted from many more perspectives than would be possible in traditional sampling, and is superior to traditional sampling. Experience gained from the process of using Big Data in this study will shed light on future opportunities for conducting evidence-based nursing research to achieve competence in disaster nursing.

  17. The Promise and Potential Perils of Big Data for Advancing Symptom Management Research in Populations at Risk for Health Disparities.

    Science.gov (United States)

    Bakken, Suzanne; Reame, Nancy

    2016-01-01

    Symptom management research is a core area of nursing science and one of the priorities for the National Institute of Nursing Research, which specifically focuses on understanding the biological and behavioral aspects of symptoms such as pain and fatigue, with the goal of developing new knowledge and new strategies for improving patient health and quality of life. The types and volume of data related to the symptom experience, symptom management strategies, and outcomes are increasingly accessible for research. Traditional data streams are now complemented by consumer-generated (i.e., quantified self) and "omic" data streams. Thus, the data available for symptom science can be considered big data. The purposes of this chapter are to (a) briefly summarize the current drivers for the use of big data in research; (b) describe the promise of big data and associated data science methods for advancing symptom management research; (c) explicate the potential perils of big data and data science from the perspective of the ethical principles of autonomy, beneficence, and justice; and (d) illustrate strategies for balancing the promise and the perils of big data through a case study of a community at high risk for health disparities. Big data and associated data science methods offer the promise of multidimensional data sources and new methods to address significant research gaps in symptom management. If nurse scientists wish to apply big data and data science methods to advance symptom management research and promote health equity, they must carefully consider both the promise and perils.

  18. The Need for a Definition of Big Data for Nursing Science: A Case Study of Disaster Preparedness.

    Science.gov (United States)

    Wong, Ho Ting; Chiang, Vico Chung Lim; Choi, Kup Sze; Loke, Alice Yuen

    2016-10-17

    The rapid development of technology has made enormous volumes of data available and achievable anytime and anywhere around the world. Data scientists call this change a data era and have introduced the term "Big Data", which has drawn the attention of nursing scholars. Nevertheless, the concept of Big Data is quite fuzzy and there is no agreement on its definition among researchers of different disciplines. Without a clear consensus on this issue, nursing scholars who are relatively new to the concept may consider Big Data to be merely a dataset of a bigger size. Having a suitable definition for nurse researchers in their context of research and practice is essential for the advancement of nursing research. In view of the need for a better understanding on what Big Data is, the aim in this paper is to explore and discuss the concept. Furthermore, an example of a Big Data research study on disaster nursing preparedness involving six million patient records is used for discussion. The example demonstrates that a Big Data analysis can be conducted from many more perspectives than would be possible in traditional sampling, and is superior to traditional sampling. Experience gained from the process of using Big Data in this study will shed light on future opportunities for conducting evidence-based nursing research to achieve competence in disaster nursing.

  19. Processing Solutions for Big Data in Astronomy

    Science.gov (United States)

    Fillatre, L.; Lepiller, D.

    2016-09-01

    This paper gives a simple introduction to processing solutions applied to massive amounts of data. It proposes a general presentation of the Big Data paradigm. The Hadoop framework, which is considered as the pioneering processing solution for Big Data, is described together with YARN, the integrated Hadoop tool for resource allocation. This paper also presents the main tools for the management of both the storage (NoSQL solutions) and computing capacities (MapReduce parallel processing schema) of a cluster of machines. Finally, more recent processing solutions like Spark are discussed. Big Data frameworks are now able to run complex applications while keeping the programming simple and greatly improving the computing speed.

  20. Big Data as Governmentality in International Development

    DEFF Research Database (Denmark)

    Flyverbom, Mikkel; Madsen, Anders Koed; Rasche, Andreas

    2017-01-01

    Statistics have long shaped the field of visibility for the governance of development projects. The introduction of big data has altered the field of visibility. Employing Dean's “analytics of government” framework, we analyze two cases—malaria tracking in Kenya and monitoring of food prices...... in Indonesia. Our analysis shows that big data introduces a bias toward particular types of visualizations. What problems are being made visible through big data depends to some degree on how the underlying data is visualized and who is captured in the visualizations. It is also influenced by technical factors...

  1. Astroinformatics: the big data of the universe

    OpenAIRE

    Barmby, Pauline

    2016-01-01

    In astrophysics we like to think that our field was the originator of big data, back when it had to be carried around in big sky charts and books full of tables. These days, it's easier to move astrophysics data around, but we still have a lot of it, and upcoming telescope  facilities will generate even more. I discuss how astrophysicists approach big data in general, and give examples from some Western Physics & Astronomy research projects.  I also give an overview of ho...

  2. Big Data and historical social science

    Directory of Open Access Journals (Sweden)

    Peter Bearman

    2015-11-01

    Full Text Available “Big Data” can revolutionize historical social science if it arises from substantively important contexts and is oriented towards answering substantively important questions. Such data may be especially important for answering previously largely intractable questions about the timing and sequencing of events, and of event boundaries. That said, “Big Data” makes no difference for social scientists and historians whose accounts rest on narrative sentences. Since such accounts are the norm, the effects of Big Data on the practice of historical social science may be more limited than one might wish.

  3. Hot big bang or slow freeze?

    Science.gov (United States)

    Wetterich, C.

    2014-09-01

    We confront the big bang for the beginning of the universe with an equivalent picture of a slow freeze - a very cold and slowly evolving universe. In the freeze picture the masses of elementary particles increase and the gravitational constant decreases with cosmic time, while the Newtonian attraction remains unchanged. The freeze and big bang pictures both describe the same observations or physical reality. We present a simple ;crossover model; without a big bang singularity. In the infinite past space-time is flat. Our model is compatible with present observations, describing the generation of primordial density fluctuations during inflation as well as the present transition to a dark energy-dominated universe.

  4. Smart Information Management in Health Big Data.

    Science.gov (United States)

    Muteba A, Eustache

    2017-01-01

    The smart information management system (SIMS) is concerned with the organization of anonymous patient records in a big data and their extraction in order to provide needful real-time intelligence. The purpose of the present study is to highlight the design and the implementation of the smart information management system. We emphasis, in one hand, the organization of a big data in flat file in simulation of nosql database, and in the other hand, the extraction of information based on lookup table and cache mechanism. The SIMS in the health big data aims the identification of new therapies and approaches to delivering care.

  5. Modeling of the Nuclear Power Plant Life cycle for 'Big Data' Management System: A Systems Engineering Viewpoint

    International Nuclear Information System (INIS)

    Ha, Bui Hoang; Khanh, Tran Quang Diep; Shakirah, Wan; Kahar, Wan Abdul; Jung, Jae Cheon

    2012-01-01

    Together with the significant development of Internet and Web technologies, the rapid evolution of 'Big Data' idea has been observed since it is first introduced in 1941 as an 'information explosion'(OED). Using the '3Vs' model, as proposed by Gartner, 'Big Data' can be defined as 'high volume, high velocity, and/or high variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization.' Big Data technologies and tools have been developed to address the way large quantities of data are stored, accessed and presented for manipulation or analysis. The idea also focuses on how the users can easily access and extract the 'useful and right' data, information, or even knowledge from the 'Big Data'.

  6. Big questions, big science: meeting the challenges of global ecology.

    Science.gov (United States)

    Schimel, David; Keller, Michael

    2015-04-01

    Ecologists are increasingly tackling questions that require significant infrastucture, large experiments, networks of observations, and complex data and computation. Key hypotheses in ecology increasingly require more investment, and larger data sets to be tested than can be collected by a single investigator's or s group of investigator's labs, sustained for longer than a typical grant. Large-scale projects are expensive, so their scientific return on the investment has to justify the opportunity cost-the science foregone because resources were expended on a large project rather than supporting a number of individual projects. In addition, their management must be accountable and efficient in the use of significant resources, requiring the use of formal systems engineering and project management to mitigate risk of failure. Mapping the scientific method into formal project management requires both scientists able to work in the context, and a project implementation team sensitive to the unique requirements of ecology. Sponsoring agencies, under pressure from external and internal forces, experience many pressures that push them towards counterproductive project management but a scientific community aware and experienced in large project science can mitigate these tendencies. For big ecology to result in great science, ecologists must become informed, aware and engaged in the advocacy and governance of large ecological projects.

  7. Forget the hype or reality. Big data presents new opportunities in Earth Science.

    Science.gov (United States)

    Lee, T. J.

    2015-12-01

    Earth science is arguably one of the most mature science discipline which constantly acquires, curates, and utilizes a large volume of data with diverse variety. We deal with big data before there is big data. For example, while developing the EOS program in the 1980s, the EOS data and information system (EOSDIS) was developed to manage the vast amount of data acquired by the EOS fleet of satellites. EOSDIS continues to be a shining example of modern science data systems in the past two decades. With the explosion of internet, the usage of social media, and the provision of sensors everywhere, the big data era has bring new challenges. First, Goggle developed the search algorithm and a distributed data management system. The open source communities quickly followed up and developed Hadoop file system to facility the map reduce workloads. The internet continues to generate tens of petabytes of data every day. There is a significant shortage of algorithms and knowledgeable manpower to mine the data. In response, the federal government developed the big data programs that fund research and development projects and training programs to tackle these new challenges. Meanwhile, comparatively to the internet data explosion, Earth science big data problem has become quite small. Nevertheless, the big data era presents an opportunity for Earth science to evolve. We learned about the MapReduce algorithms, in memory data mining, machine learning, graph analysis, and semantic web technologies. How do we apply these new technologies to our discipline and bring the hype to Earth? In this talk, I will discuss how we might want to apply some of the big data technologies to our discipline and solve many of our challenging problems. More importantly, I will propose new Earth science data system architecture to enable new type of scientific inquires.

  8. Seasonal shifts in the diet of the big brown bat (Eptesicus fuscus), Fort Collins, Colorado

    Science.gov (United States)

    Valdez, Ernest W.; O'Shea, Thomas J.

    2014-01-01

    Recent analyses suggest that the big brown bat (Eptesicus fuscus) may be less of a beetle specialist (Coleoptera) in the western United States than previously thought, and that its diet might also vary with temperature. We tested the hypothesis that big brown bats might opportunistically prey on moths by analyzing insect fragments in guano pellets from 30 individual bats (27 females and 3 males) captured while foraging in Fort Collins, Colorado, during May, late July–early August, and late September 2002. We found that bats sampled 17–20 May (n = 12 bats) had a high (81–83%) percentage of volume of lepidopterans in guano, with the remainder (17–19% volume) dipterans and no coleopterans. From 28 May–9 August (n = 17 bats) coleopterans dominated (74–98% volume). On 20 September (n = 1 bat) lepidopterans were 99% of volume in guano. Migratory miller moths (Euxoa auxiliaris) were unusually abundant in Fort Collins in spring and autumn of 2002 and are known agricultural pests as larvae (army cutworms), suggesting that seasonal dietary flexibility in big brown bats has economic benefits.

  9. Infectious Disease Surveillance in the Big Data Era: Towards Faster and Locally Relevant Systems

    Science.gov (United States)

    Simonsen, Lone; Gog, Julia R.; Olson, Don; Viboud, Cécile

    2016-01-01

    While big data have proven immensely useful in fields such as marketing and earth sciences, public health is still relying on more traditional surveillance systems and awaiting the fruits of a big data revolution. A new generation of big data surveillance systems is needed to achieve rapid, flexible, and local tracking of infectious diseases, especially for emerging pathogens. In this opinion piece, we reflect on the long and distinguished history of disease surveillance and discuss recent developments related to use of big data. We start with a brief review of traditional systems relying on clinical and laboratory reports. We then examine how large-volume medical claims data can, with great spatiotemporal resolution, help elucidate local disease patterns. Finally, we review efforts to develop surveillance systems based on digital and social data streams, including the recent rise and fall of Google Flu Trends. We conclude by advocating for increased use of hybrid systems combining information from traditional surveillance and big data sources, which seems the most promising option moving forward. Throughout the article, we use influenza as an exemplar of an emerging and reemerging infection which has traditionally been considered a model system for surveillance and modeling. PMID:28830112

  10. How to Generate Economic and Sustainability Reports from Big Data? Qualifications of Process Industry

    Directory of Open Access Journals (Sweden)

    Esa Hämäläinen

    2017-11-01

    Full Text Available Big Data may introduce new opportunities, and for this reason it has become a mantra among most industries. This paper focuses on examining how to develop cost and sustainable reporting by utilizing Big Data that covers economic values, production volumes, and emission information. We assume strongly that this use supports cleaner production, while at the same time offers more information for revenue and profitability development. We argue that Big Data brings company-wide business benefits if data queries and interfaces are built to be interactive, intuitive, and user-friendly. The amount of information related to operations, costs, emissions, and the supply chain would increase enormously if Big Data was used in various manufacturing industries. It is essential to expose the relevant correlations between different attributes and data fields. Proper algorithm design and programming are key to making the most of Big Data. This paper introduces ideas on how to refine raw data into valuable information, which can serve many types of end users, decision makers, and even external auditors. Concrete examples are given through an industrial paper mill case, which covers environmental aspects, cost-efficiency management, and process design.

  11. Big Data technology in traffic: A case study of automatic counters

    Directory of Open Access Journals (Sweden)

    Janković Slađana R.

    2016-01-01

    Full Text Available Modern information and communication technologies together with intelligent devices provide a continuous inflow of large amounts of data that are used by traffic and transport systems. Collecting traffic data does not represent a challenge nowadays, but the issues remains in relation to storing and processing increasing amounts of data. In this paper we have investigated the possibilities of using Big Data technology to store and process data in the transport domain. The term Big Data refers to a large volume of information resource, its velocity and variety, far beyond the capabilities of commonly used software for storing, processing and data management. In our case study, Apache™ Hadoop® Big Data was used for processing data collected from 10 automatic traffic counters set up in Novi Sad and its surroundings. Indicators of traffic load which were calculated using the Big Data platforms were presented using tables and graphs in Microsoft Office Excel tool. The visualization and geolocation of the obtained indicators were performed using the Microsoft Business Intelligence (BI tools such as: Excel Power View and Excel Power Map. This case study showed that Big Data technologies combined with the BI tools can be used as a reliable support in monitoring of the traffic management systems.

  12. Implementing Operational Analytics using Big Data Technologies to Detect and Predict Sensor Anomalies

    Science.gov (United States)

    Coughlin, J.; Mital, R.; Nittur, S.; SanNicolas, B.; Wolf, C.; Jusufi, R.

    2016-09-01

    Operational analytics when combined with Big Data technologies and predictive techniques have been shown to be valuable in detecting mission critical sensor anomalies that might be missed by conventional analytical techniques. Our approach helps analysts and leaders make informed and rapid decisions by analyzing large volumes of complex data in near real-time and presenting it in a manner that facilitates decision making. It provides cost savings by being able to alert and predict when sensor degradations pass a critical threshold and impact mission operations. Operational analytics, which uses Big Data tools and technologies, can process very large data sets containing a variety of data types to uncover hidden patterns, unknown correlations, and other relevant information. When combined with predictive techniques, it provides a mechanism to monitor and visualize these data sets and provide insight into degradations encountered in large sensor systems such as the space surveillance network. In this study, data from a notional sensor is simulated and we use big data technologies, predictive algorithms and operational analytics to process the data and predict sensor degradations. This study uses data products that would commonly be analyzed at a site. This study builds on a big data architecture that has previously been proven valuable in detecting anomalies. This paper outlines our methodology of implementing an operational analytic solution through data discovery, learning and training of data modeling and predictive techniques, and deployment. Through this methodology, we implement a functional architecture focused on exploring available big data sets and determine practical analytic, visualization, and predictive technologies.

  13. MapFactory - Towards a mapping design pattern for big geospatial data

    Science.gov (United States)

    Rautenbach, Victoria; Coetzee, Serena

    2018-05-01

    With big geospatial data emerging, cartographers and geographic information scientists have to find new ways of dealing with the volume, variety, velocity, and veracity (4Vs) of the data. This requires the development of tools that allow processing, filtering, analysing, and visualising of big data through multidisciplinary collaboration. In this paper, we present the MapFactory design pattern that will be used for the creation of different maps according to the (input) design specification for big geospatial data. The design specification is based on elements from ISO19115-1:2014 Geographic information - Metadata - Part 1: Fundamentals that would guide the design and development of the map or set of maps to be produced. The results of the exploratory research suggest that the MapFactory design pattern will help with software reuse and communication. The MapFactory design pattern will aid software developers to build the tools that are required to automate map making with big geospatial data. The resulting maps would assist cartographers and others to make sense of big geospatial data.

  14. Infectious Disease Surveillance in the Big Data Era: Towards Faster and Locally Relevant Systems.

    Science.gov (United States)

    Simonsen, Lone; Gog, Julia R; Olson, Don; Viboud, Cécile

    2016-12-01

    While big data have proven immensely useful in fields such as marketing and earth sciences, public health is still relying on more traditional surveillance systems and awaiting the fruits of a big data revolution. A new generation of big data surveillance systems is needed to achieve rapid, flexible, and local tracking of infectious diseases, especially for emerging pathogens. In this opinion piece, we reflect on the long and distinguished history of disease surveillance and discuss recent developments related to use of big data. We start with a brief review of traditional systems relying on clinical and laboratory reports. We then examine how large-volume medical claims data can, with great spatiotemporal resolution, help elucidate local disease patterns. Finally, we review efforts to develop surveillance systems based on digital and social data streams, including the recent rise and fall of Google Flu Trends. We conclude by advocating for increased use of hybrid systems combining information from traditional surveillance and big data sources, which seems the most promising option moving forward. Throughout the article, we use influenza as an exemplar of an emerging and reemerging infection which has traditionally been considered a model system for surveillance and modeling. Published by Oxford University Press for the Infectious Diseases Society of America 2016. This work is written by (a) US Government employee(s) and is in the public domain in the US.

  15. NOAA Big Data Partnership RFI

    Science.gov (United States)

    de la Beaujardiere, J.

    2014-12-01

    In February 2014, the US National Oceanic and Atmospheric Administration (NOAA) issued a Big Data Request for Information (RFI) from industry and other organizations (e.g., non-profits, research laboratories, and universities) to assess capability and interest in establishing partnerships to position a copy of NOAA's vast data holdings in the Cloud, co-located with easy and affordable access to analytical capabilities. This RFI was motivated by a number of concerns. First, NOAA's data facilities do not necessarily have sufficient network infrastructure to transmit all available observations and numerical model outputs to all potential users, or sufficient infrastructure to support simultaneous computation by many users. Second, the available data are distributed across multiple services and data facilities, making it difficult to find and integrate data for cross-domain analysis and decision-making. Third, large datasets require users to have substantial network, storage, and computing capabilities of their own in order to fully interact with and exploit the latent value of the data. Finally, there may be commercial opportunities for value-added products and services derived from our data. Putting a working copy of data in the Cloud outside of NOAA's internal networks and infrastructures should reduce demands and risks on our systems, and should enable users to interact with multiple datasets and create new lines of business (much like the industries built on government-furnished weather or GPS data). The NOAA Big Data RFI therefore solicited information on technical and business approaches regarding possible partnership(s) that -- at no net cost to the government and minimum impact on existing data facilities -- would unleash the commercial potential of its environmental observations and model outputs. NOAA would retain the master archival copy of its data. Commercial partners would not be permitted to charge fees for access to the NOAA data they receive, but

  16. BIG SKY CARBON SEQUESTRATION PARTNERSHIP

    Energy Technology Data Exchange (ETDEWEB)

    Susan M. Capalbo

    2005-01-31

    The Big Sky Carbon Sequestration Partnership, led by Montana State University, is comprised of research institutions, public entities and private sectors organizations, and the Confederated Salish and Kootenai Tribes and the Nez Perce Tribe. Efforts under this Partnership in Phase I fall into four areas: evaluation of sources and carbon sequestration sinks that will be used to determine the location of pilot demonstrations in Phase II; development of GIS-based reporting framework that links with national networks; designing an integrated suite of monitoring, measuring, and verification technologies and assessment frameworks; and initiating a comprehensive education and outreach program. The groundwork is in place to provide an assessment of storage capabilities for CO{sub 2} utilizing the resources found in the Partnership region (both geological and terrestrial sinks), that would complement the ongoing DOE research. Efforts are underway to showcase the architecture of the GIS framework and initial results for sources and sinks. The region has a diverse array of geological formations that could provide storage options for carbon in one or more of its three states. Likewise, initial estimates of terrestrial sinks indicate a vast potential for increasing and maintaining soil C on forested, agricultural, and reclaimed lands. Both options include the potential for offsetting economic benefits to industry and society. Steps have been taken to assure that the GIS-based framework is consistent among types of sinks within the Big Sky Partnership area and with the efforts of other western DOE partnerships. The Partnership recognizes the critical importance of measurement, monitoring, and verification technologies to support not only carbon trading but all policies and programs that DOE and other agencies may want to pursue in support of GHG mitigation. The efforts in developing and implementing MMV technologies for geological sequestration reflect this concern. Research is

  17. Big Data and HPC collocation: Using HPC idle resources for Big Data Analytics

    OpenAIRE

    MERCIER , Michael; Glesser , David; Georgiou , Yiannis; Richard , Olivier

    2017-01-01

    International audience; Executing Big Data workloads upon High Performance Computing (HPC) infrastractures has become an attractive way to improve their performances. However, the collocation of HPC and Big Data workloads is not an easy task, mainly because of their core concepts' differences. This paper focuses on the challenges related to the scheduling of both Big Data and HPC workloads on the same computing platform. In classic HPC workloads, the rigidity of jobs tends to create holes in ...

  18. Semantic Web Technologies and Big Data Infrastructures: SPARQL Federated Querying of Heterogeneous Big Data Stores

    OpenAIRE

    Konstantopoulos, Stasinos; Charalambidis, Angelos; Mouchakis, Giannis; Troumpoukis, Antonis; Jakobitsch, Jürgen; Karkaletsis, Vangelis

    2016-01-01

    The ability to cross-link large scale data with each other and with structured Semantic Web data, and the ability to uniformly process Semantic Web and other data adds value to both the Semantic Web and to the Big Data community. This paper presents work in progress towards integrating Big Data infrastructures with Semantic Web technologies, allowing for the cross-linking and uniform retrieval of data stored in both Big Data infrastructures and Semantic Web data. The technical challenges invo...

  19. 2015 OLC Lidar DEM: Big Wood, ID

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Quantum Spatial has collected Light Detection and Ranging (LiDAR) data for the Oregon LiDAR Consortium (OLC) Big Wood 2015 study area. This study area is located in...

  20. Big Data Components for Business Process Optimization

    Directory of Open Access Journals (Sweden)

    Mircea Raducu TRIFU

    2016-01-01

    Full Text Available In these days, more and more people talk about Big Data, Hadoop, noSQL and so on, but very few technical people have the necessary expertise and knowledge to work with those concepts and technologies. The present issue explains one of the concept that stand behind two of those keywords, and this is the map reduce concept. MapReduce model is the one that makes the Big Data and Hadoop so powerful, fast, and diverse for business process optimization. MapReduce is a programming model with an implementation built to process and generate large data sets. In addition, it is presented the benefits of integrating Hadoop in the context of Business Intelligence and Data Warehousing applications. The concepts and technologies behind big data let organizations to reach a variety of objectives. Like other new information technologies, the main important objective of big data technology is to bring dramatic cost reduction.

  1. Scaling big data with Hadoop and Solr

    CERN Document Server

    Karambelkar, Hrishikesh Vijay

    2015-01-01

    This book is aimed at developers, designers, and architects who would like to build big data enterprise search solutions for their customers or organizations. No prior knowledge of Apache Hadoop and Apache Solr/Lucene technologies is required.

  2. Big Data and Analytics in Healthcare.

    Science.gov (United States)

    Tan, S S-L; Gao, G; Koch, S

    2015-01-01

    This editorial is part of the Focus Theme of Methods of Information in Medicine on "Big Data and Analytics in Healthcare". The amount of data being generated in the healthcare industry is growing at a rapid rate. This has generated immense interest in leveraging the availability of healthcare data (and "big data") to improve health outcomes and reduce costs. However, the nature of healthcare data, and especially big data, presents unique challenges in processing and analyzing big data in healthcare. This Focus Theme aims to disseminate some novel approaches to address these challenges. More specifically, approaches ranging from efficient methods of processing large clinical data to predictive models that could generate better predictions from healthcare data are presented.

  3. Statistical Challenges in Modeling Big Brain Signals

    KAUST Repository

    Yu, Zhaoxia

    2017-11-01

    Brain signal data are inherently big: massive in amount, complex in structure, and high in dimensions. These characteristics impose great challenges for statistical inference and learning. Here we review several key challenges, discuss possible solutions, and highlight future research directions.

  4. NDE Big Data Framework, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — NDE data has become "Big Data", and is overwhelming the abilities of NDE technicians and commercially available tools to deal with it. In the current state of the...

  5. Cosmic relics from the big bang

    International Nuclear Information System (INIS)

    Hall, L.J.

    1988-12-01

    A brief introduction to the big bang picture of the early universe is given. Dark matter is discussed; particularly its implications for elementary particle physics. A classification scheme for dark matter relics is given. 21 refs., 11 figs., 1 tab

  6. ARC Code TI: BigView

    Data.gov (United States)

    National Aeronautics and Space Administration — BigView allows for interactive panning and zooming of images of arbitrary size on desktop PCs running linux. Additionally, it can work in a multi-screen environment...

  7. Don't Trust the Big Man

    National Research Council Canada - National Science Library

    Coerr, Stanton S

    2008-01-01

    .... A full-length analysis of the Democratic Republic of the Congo and its full-scale insurgencies since 1960 -- at the hands of Big Man leaders Lumumba, Mobutu, and Kabila -- provide object lessons...

  8. Quantum nature of the big bang.

    Science.gov (United States)

    Ashtekar, Abhay; Pawlowski, Tomasz; Singh, Parampreet

    2006-04-14

    Some long-standing issues concerning the quantum nature of the big bang are resolved in the context of homogeneous isotropic models with a scalar field. Specifically, the known results on the resolution of the big-bang singularity in loop quantum cosmology are significantly extended as follows: (i) the scalar field is shown to serve as an internal clock, thereby providing a detailed realization of the "emergent time" idea; (ii) the physical Hilbert space, Dirac observables, and semiclassical states are constructed rigorously; (iii) the Hamiltonian constraint is solved numerically to show that the big bang is replaced by a big bounce. Thanks to the nonperturbative, background independent methods, unlike in other approaches the quantum evolution is deterministic across the deep Planck regime.

  9. Big Data in food and agriculture

    Directory of Open Access Journals (Sweden)

    Kelly Bronson

    2016-06-01

    Full Text Available Farming is undergoing a digital revolution. Our existing review of current Big Data applications in the agri-food sector has revealed several collection and analytics tools that may have implications for relationships of power between players in the food system (e.g. between farmers and large corporations. For example, Who retains ownership of the data generated by applications like Monsanto Corproation's Weed I.D . “app”? Are there privacy implications with the data gathered by John Deere's precision agricultural equipment? Systematically tracing the digital revolution in agriculture, and charting the affordances as well as the limitations of Big Data applied to food and agriculture, should be a broad research goal for Big Data scholarship. Such a goal brings data scholarship into conversation with food studies and it allows for a focus on the material consequences of big data in society.

  10. Fisicos argentinos reproduciran el Big Bang

    CERN Multimedia

    De Ambrosio, Martin

    2008-01-01

    Two groups of argentine physicists from La Plata and Buenos Aires Universities work in a sery of experiments who while recreate the conditions of the big explosion that was at the origin of the universe. (1 page)

  11. Statistical Challenges in Modeling Big Brain Signals

    KAUST Repository

    Yu, Zhaoxia; Pluta, Dustin; Shen, Tong; Chen, Chuansheng; Xue, Gui; Ombao, Hernando

    2017-01-01

    Brain signal data are inherently big: massive in amount, complex in structure, and high in dimensions. These characteristics impose great challenges for statistical inference and learning. Here we review several key challenges, discuss possible

  12. Bioinformatics clouds for big data manipulation

    KAUST Repository

    Dai, Lin; Gao, Xin; Guo, Yan; Xiao, Jingfa; Zhang, Zhang

    2012-01-01

    -supplied cloud computing delivered over the Internet, in order to handle the vast quantities of biological data generated by high-throughput experimental technologies. Albeit relatively new, cloud computing promises to address big data storage and analysis issues

  13. Big data business models: Challenges and opportunities

    Directory of Open Access Journals (Sweden)

    Ralph Schroeder

    2016-12-01

    Full Text Available This paper, based on 28 interviews from a range of business leaders and practitioners, examines the current state of big data use in business, as well as the main opportunities and challenges presented by big data. It begins with an account of the current landscape and what is meant by big data. Next, it draws distinctions between the ways organisations use data and provides a taxonomy of big data business models. We observe a variety of different business models, depending not only on sector, but also on whether the main advantages derive from analytics capabilities or from having ready access to valuable data sources. Some major challenges emerge from this account, including data quality and protectiveness about sharing data. The conclusion discusses these challenges, and points to the tensions and differing perceptions about how data should be governed as between business practitioners, the promoters of open data, and the wider public.

  14. Cosmic relics from the big bang

    Energy Technology Data Exchange (ETDEWEB)

    Hall, L.J.

    1988-12-01

    A brief introduction to the big bang picture of the early universe is given. Dark matter is discussed; particularly its implications for elementary particle physics. A classification scheme for dark matter relics is given. 21 refs., 11 figs., 1 tab.

  15. Big Bend National Park: Acoustical Monitoring 2010

    Science.gov (United States)

    2013-06-01

    During the summer of 2010 (September October 2010), the Volpe Center collected baseline acoustical data at Big Bend National Park (BIBE) at four sites deployed for approximately 30 days each. The baseline data collected during this period will he...

  16. Big Data for Business Ecosystem Players

    Directory of Open Access Journals (Sweden)

    Perko Igor

    2016-06-01

    Full Text Available In the provided research, some of the Big Data most prospective usage domains connect with distinguished player groups found in the business ecosystem. Literature analysis is used to identify the state of the art of Big Data related research in the major domains of its use-namely, individual marketing, health treatment, work opportunities, financial services, and security enforcement. System theory was used to identify business ecosystem major player types disrupted by Big Data: individuals, small and mid-sized enterprises, large organizations, information providers, and regulators. Relationships between the domains and players were explained through new Big Data opportunities and threats and by players’ responsive strategies. System dynamics was used to visualize relationships in the provided model.

  17. Big Data in Plant Science: Resources and Data Mining Tools for Plant Genomics and Proteomics.

    Science.gov (United States)

    Popescu, George V; Noutsos, Christos; Popescu, Sorina C

    2016-01-01

    In modern plant biology, progress is increasingly defined by the scientists' ability to gather and analyze data sets of high volume and complexity, otherwise known as "big data". Arguably, the largest increase in the volume of plant data sets over the last decade is a consequence of the application of the next-generation sequencing and mass-spectrometry technologies to the study of experimental model and crop plants. The increase in quantity and complexity of biological data brings challenges, mostly associated with data acquisition, processing, and sharing within the scientific community. Nonetheless, big data in plant science create unique opportunities in advancing our understanding of complex biological processes at a level of accuracy without precedence, and establish a base for the plant systems biology. In this chapter, we summarize the major drivers of big data in plant science and big data initiatives in life sciences with a focus on the scope and impact of iPlant, a representative cyberinfrastructure platform for plant science.

  18. 'Big bang' of quantum universe

    International Nuclear Information System (INIS)

    Pawlowski, M.; Pervushin, V.N.

    2000-01-01

    The reparametrization-invariant generating functional for the unitary and causal perturbation theory in general relativity in a finite space-time is obtained. The classical cosmology of a Universe and the Faddeev-Popov-DeWitt functional correspond to different orders of decomposition of this functional over the inverse 'mass' of a Universe. It is shown that the invariant content of general relativity as a constrained system can be covered by two 'equivalent' unconstrained systems: the 'dynamic' (with 'dynamic' time as the cosmic scale factor and conformal field variables) and 'geometric' (given by the Levi-Civita type canonical transformation to the action-angle variables which determine initial cosmological states with the arrow of the proper time measured by the watch of an observer in the comoving frame). 'Big Bang', the Hubble evolution, and creation of 'dynamic' particles by the 'geometric' vacuum are determined by 'relations' between the dynamic and geometric systems as pure relativistic phenomena, like the Lorentz-type 'relation' between the rest and comoving frames in special relativity

  19. BIG SKY CARBON SEQUESTRATION PARTNERSHIP

    Energy Technology Data Exchange (ETDEWEB)

    Susan M. Capalbo

    2004-06-01

    The Big Sky Partnership, led by Montana State University, is comprised of research institutions, public entities and private sectors organizations, and the Confederated Salish and Kootenai Tribes and the Nez Perce Tribe. Efforts during the second performance period fall into four areas: evaluation of sources and carbon sequestration sinks; development of GIS-based reporting framework; designing an integrated suite of monitoring, measuring, and verification technologies; and initiating a comprehensive education and outreach program. At the first two Partnership meetings the groundwork was put in place to provide an assessment of capture and storage capabilities for CO{sub 2} utilizing the resources found in the Partnership region (both geological and terrestrial sinks), that would complement the ongoing DOE research. The region has a diverse array of geological formations that could provide storage options for carbon in one or more of its three states. Likewise, initial estimates of terrestrial sinks indicate a vast potential for increasing and maintaining soil C on forested, agricultural, and reclaimed lands. Both options include the potential for offsetting economic benefits to industry and society. Steps have been taken to assure that the GIS-based framework is consistent among types of sinks within the Big Sky Partnership area and with the efforts of other western DOE partnerships. Efforts are also being made to find funding to include Wyoming in the coverage areas for both geological and terrestrial sinks and sources. The Partnership recognizes the critical importance of measurement, monitoring, and verification technologies to support not only carbon trading but all policies and programs that DOE and other agencies may want to pursue in support of GHG mitigation. The efforts begun in developing and implementing MMV technologies for geological sequestration reflect this concern. Research is also underway to identify and validate best management practices for

  20. The NOAA Big Data Project

    Science.gov (United States)

    de la Beaujardiere, J.

    2015-12-01

    The US National Oceanic and Atmospheric Administration (NOAA) is a Big Data producer, generating tens of terabytes per day from hundreds of sensors on satellites, radars, aircraft, ships, and buoys, and from numerical models. These data are of critical importance and value for NOAA's mission to understand and predict changes in climate, weather, oceans, and coasts. In order to facilitate extracting additional value from this information, NOAA has established Cooperative Research and Development Agreements (CRADAs) with five Infrastructure-as-a-Service (IaaS) providers — Amazon, Google, IBM, Microsoft, Open Cloud Consortium — to determine whether hosting NOAA data in publicly-accessible Clouds alongside on-demand computational capability stimulates the creation of new value-added products and services and lines of business based on the data, and if the revenue generated by these new applications can support the costs of data transmission and hosting. Each IaaS provider is the anchor of a "Data Alliance" which organizations or entrepreneurs can join to develop and test new business or research avenues. This presentation will report on progress and lessons learned during the first 6 months of the 3-year CRADAs.

  1. Big-bang nucleosynthesis revisited

    Science.gov (United States)

    Olive, Keith A.; Schramm, David N.; Steigman, Gary; Walker, Terry P.

    1989-01-01

    The homogeneous big-bang nucleosynthesis yields of D, He-3, He-4, and Li-7 are computed taking into account recent measurements of the neutron mean-life as well as updates of several nuclear reaction rates which primarily affect the production of Li-7. The extraction of primordial abundances from observation and the likelihood that the primordial mass fraction of He-4, Y(sub p) is less than or equal to 0.24 are discussed. Using the primordial abundances of D + He-3 and Li-7 we limit the baryon-to-photon ratio (eta in units of 10 exp -10) 2.6 less than or equal to eta(sub 10) less than or equal to 4.3; which we use to argue that baryons contribute between 0.02 and 0.11 to the critical energy density of the universe. An upper limit to Y(sub p) of 0.24 constrains the number of light neutrinos to N(sub nu) less than or equal to 3.4, in excellent agreement with the LEP and SLC collider results. We turn this argument around to show that the collider limit of 3 neutrino species can be used to bound the primordial abundance of He-4: 0.235 less than or equal to Y(sub p) less than or equal to 0.245.

  2. Neutrinos and Big Bang Nucleosynthesis

    Directory of Open Access Journals (Sweden)

    Gary Steigman

    2012-01-01

    Full Text Available According to the standard models of particle physics and cosmology, there should be a background of cosmic neutrinos in the present Universe, similar to the cosmic microwave photon background. The weakness of the weak interactions renders this neutrino background undetectable with current technology. The cosmic neutrino background can, however, be probed indirectly through its cosmological effects on big bang nucleosynthesis (BBN and the cosmic microwave background (CMB radiation. In this BBN review, focused on neutrinos and more generally on dark radiation, the BBN constraints on the number of “equivalent neutrinos” (dark radiation, on the baryon asymmetry (baryon density, and on a possible lepton asymmetry (neutrino degeneracy are reviewed and updated. The BBN constraints on dark radiation and on the baryon density following from considerations of the primordial abundances of deuterium and helium-4 are in excellent agreement with the complementary results from the CMB, providing a suggestive, but currently inconclusive, hint of the presence of dark radiation, and they constrain any lepton asymmetry. For all the cases considered here there is a “lithium problem”: the BBN-predicted lithium abundance exceeds the observationally inferred primordial value by a factor of ~3.

  3. "Big Science" exhibition at Balexert

    CERN Multimedia

    2008-01-01

    CERN is going out to meet those members of the general public who were unable to attend the recent Open Day. The Laboratory will be taking its "Big Science" exhibition from the Globe of Science and Innovation to the Balexert shopping centre from 19 to 31 May 2008. The exhibition, which shows the LHC and its experiments through the eyes of a photographer, features around thirty spectacular photographs measuring 4.5 metres high and 2.5 metres wide. Welcomed and guided around the exhibition by CERN volunteers, shoppers at Balexert will also have the opportunity to discover LHC components on display and watch films. "Fun with Physics" workshops will be held at certain times of the day. Main hall of the Balexert shopping centre, ground floor, from 9.00 a.m. to 7.00 p.m. Monday to Friday and from 10 a.m. to 6 p.m. on the two Saturdays. Call for volunteers All members of the CERN personnel are invited to enrol as volunteers to help welcom...

  4. Deuterium and big bang nucleosynthesis

    International Nuclear Information System (INIS)

    Burles, S.

    2000-01-01

    Measurements of deuterium absorption in high redshift quasar absorption systems provide a direct inference of the deuterium abundance produced by big bang nucleosynthesis (BBN). With measurements and limits from five independent absorption systems, we place strong constraints on the primordial ratio of deuterium to hydrogen, (D/H) p = 3.4 ± 0.3 x 10 -5 [1,2]. We employ a direct numerical treatment to improve the estimates of critical reaction rates and reduce the uncertainties in BBN predictions of D/H and 7 Li/H by a factor of three[3] over previous efforts[4]. Using our measurements of (D/H) p and new BBN predictions, we find at 95% confidence the baryon density ρ b = (3.6 ± 0.4) x 10 -31 g cm -3 (Ω b h 2 65 = 0.045 ± 0.006 in units of the critical density), and cosmological baryon-photon ratio η = (5.1 ± 0.6) x 10 -10

  5. A little big history of Tiananmen

    OpenAIRE

    Quaedackers, E.; Grinin, L.E.; Korotayev, A.V.; Rodrigue, B.H.

    2011-01-01

    This contribution aims at demonstrating the usefulness of studying small-scale subjects such as Tiananmen, or the Gate of Heavenly Peace, in Beijing - from a Big History perspective. By studying such a ‘little big history’ of Tiananmen, previously overlooked yet fundamental explanations for why people built the gate the way they did can be found. These explanations are useful in their own right and may also be used to deepen our understanding of more traditional explanations of why Tiananmen ...

  6. Big Data: Challenges, Opportunities and Realities

    OpenAIRE

    Bhadani, Abhay; Jothimani, Dhanya

    2017-01-01

    With the advent of Internet of Things (IoT) and Web 2.0 technologies, there has been a tremendous growth in the amount of data generated. This chapter emphasizes on the need for big data, technological advancements, tools and techniques being used to process big data are discussed. Technological improvements and limitations of existing storage techniques are also presented. Since, the traditional technologies like Relational Database Management System (RDBMS) have their own limitations to han...

  7. Big Data, Biostatistics and Complexity Reduction

    Czech Academy of Sciences Publication Activity Database

    Kalina, Jan

    2018-01-01

    Roč. 14, č. 2 (2018), s. 24-32 ISSN 1801-5603 R&D Projects: GA MZd(CZ) NV15-29835A Institutional support: RVO:67985807 Keywords : Biostatistics * Big data * Multivariate statistics * Dimensionality * Variable selection Subject RIV: IN - Informatics, Computer Science OBOR OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) https://www.ejbi.org/scholarly-articles/big-data-biostatistics-and-complexity-reduction.pdf

  8. Storage and Database Management for Big Data

    Science.gov (United States)

    2015-07-27

    cloud models that satisfy different problem 1.2. THE BIG DATA CHALLENGE 3 Enterprise Big Data - Interactive - On-demand - Virtualization - Java ...replication. Data loss can only occur if three drives fail prior to any one of the failures being corrected. Hadoop is written in Java and is installed in a...visible view into a dataset. There are many popular database management systems such as MySQL [4], PostgreSQL [63], and Oracle [5]. Most commonly

  9. From Data Quality to Big Data Quality

    OpenAIRE

    Batini, Carlo; Rula, Anisa; Scannapieco, Monica; Viscusi, Gianluigi

    2015-01-01

    This article investigates the evolution of data quality issues from traditional structured data managed in relational databases to Big Data. In particular, the paper examines the nature of the relationship between Data Quality and several research coordinates that are relevant in Big Data, such as the variety of data types, data sources and application domains, focusing on maps, semi-structured texts, linked open data, sensor & sensor networks and official statistics. Consequently a set of st...

  10. Dark energy, wormholes, and the big rip

    International Nuclear Information System (INIS)

    Faraoni, V.; Israel, W.

    2005-01-01

    The time evolution of a wormhole in a Friedmann universe approaching the big rip is studied. The wormhole is modeled by a thin spherical shell accreting the superquintessence fluid--two different models are presented. Contrary to recent claims that the wormhole overtakes the expansion of the universe and engulfs it before the big rip is reached, it is found that the wormhole becomes asymptotically comoving with the cosmic fluid and the future evolution of the universe is fully causal

  11. COBE looks back to the Big Bang

    Science.gov (United States)

    Mather, John C.

    1993-01-01

    An overview is presented of NASA-Goddard's Cosmic Background Explorer (COBE), the first NASA satellite designed to observe the primeval explosion of the universe. The spacecraft carries three extremely sensitive IR and microwave instruments designed to measure the faint residual radiation from the Big Bang and to search for the formation of the first galaxies. COBE's far IR absolute spectrophotometer has shown that the Big Bang radiation has a blackbody spectrum, proving that there was no large energy release after the explosion.

  12. CERN: A big year for LEP

    International Nuclear Information System (INIS)

    Anon.

    1991-01-01

    In April this year's data-taking period for CERN's big LEP electron-positron collider got underway, and is scheduled to continue until November. The immediate objective of the four big experiments - Aleph, Delphi, L3 and Opal - will be to increase considerably their stock of carefully recorded Z decays, currently totalling about three-quarters of a million

  13. Evidence for Evolution as Support for Big Bang

    Science.gov (United States)

    Gopal-Krishna

    1997-12-01

    With the exception of ZERO, the concept of BIG BANG is by far the most bizarre creation of the human mind. Three classical pillars of the Big Bang model of the origin of the universe are generally thought to be: (i) The abundances of the light elements; (ii) the microwave back-ground radiation; and (iii) the change with cosmic epoch in the average properties of galaxies (both active and non-active types). Evidence is also mounting for redshift dependence of the intergalactic medium, as discussed elsewhere in this volume in detail. In this contribution, I endeavour to highlight a selection of recent advances pertaining to the third category. The widely different levels of confidence in the claimed observational constraints in the field of cosmology can be guaged from the following excerpts from two leading astrophysicists: "I would bet odds of 10 to 1 on the validity of the general 'hot Big Bang' concept as a description of how our universe has evolved since it was around 1 sec. old" -M. Rees (1995), in 'Perspectives in Astrophysical Cosmology' CUP. "With the much more sensitive observations available today, no astrophysical property shows evidence of evolution, such as was claimed in the 1950s to disprove the Steady State theory" -F. Hoyle (1987), in 'Fifty years in cosmology', B. M. Birla Memorial Lecture, Hyderabad, India. The burgeoning multi-wavelength culture in astronomy has provided a tremendous boost to observational cosmology in recent years. We now proceed to illustrate this with a sequence of examples which reinforce the picture of an evolving universe. Also provided are some relevant details of the data used in these studies so that their scope can be independently judged by the readers.

  14. Kazakhstan's Environment-Health system, a Big Data challenge

    Science.gov (United States)

    Vitolo, Claudia; Bella Gazdiyeva, Bella; Tucker, Allan; Russell, Andrew; Ali, Maged; Althonayan, Abraham

    2016-04-01

    Kazakhstan has witnessed a remarkable economic development in the past 15 years, becoming an upper-middle-income country. However it is still widely regarded as a developing nation, partially because of its population's low life expectancy which is 5 years below the average in similar economies. The environment is in a rather fragile state, affected by soil, water, air pollution, radioactive contamination and climate change. However, Kazakhstan's government is moving towards clean energy and environmental protection and calling on scientists to help prioritise investments. The British Council-funded "Kazakhstan's Environment-Health Risk Analysis (KEHRA)" project is one of the recently launched initiatives to support Kazakhstan healthier future. The underlying hypothesis of this research is that the above mentioned factors (air/water/soil pollution, etc.) affecting public health almost certainly do not act independently but rather trigger and exacerbate each other. Exploring the environment-health links in a multi-dimensional framework is a typical Big Data problem, in which the volume and variety of the data needed poses technical as well as scientific challenges. In Kazakhstan, the complexities related to managing and analysing Big Data are worsened by a number of obstacles at the data acquisition step: most of the data is not in digital form, spatial and temporal attributes are often ambiguous and the re-use and re-purpose of the information is subject to restrictive licenses and other mechanisms of control. In this work, we document the first steps taken towards building an understanding of the complex environment-health system in Kazakhstan, using interactive visualisation tools to identify and compare hot-spots of pollution and poor health outcomes, Big Data and web technologies to collect, manage and explore available information. In the future, the knowledge acquired will be modelled to develop evidence-based recommendation systems for decision makers in

  15. Slaves to Big Data. Or Are We?

    Directory of Open Access Journals (Sweden)

    Mireille Hildebrandt

    2013-10-01

    Full Text Available

    In this contribution, the notion of Big Data is discussed in relation to the monetisation of personal data. The claim of some proponents, as well as adversaries, that Big Data implies that ‘n = all’, meaning that we no longer need to rely on samples because we have all the data, is scrutinised and found to be both overly optimistic and unnecessarily pessimistic. A set of epistemological and ethical issues is presented, focusing on the implications of Big Data for our perception, cognition, fairness, privacy and due process. The article then looks into the idea of user-centric personal data management to investigate to what extent it provides solutions for some of the problems triggered by the Big Data conundrum. Special attention is paid to the core principle of data protection legislation, namely purpose binding. Finally, this contribution seeks to inquire into the influence of Big Data politics on self, mind and society, and asks how we can prevent ourselves from becoming slaves to Big Data.

  16. The New Possibilities from "Big Data" to Overlooked Associations Between Diabetes, Biochemical Parameters, Glucose Control, and Osteoporosis.

    Science.gov (United States)

    Kruse, Christian

    2018-06-01

    To review current practices and technologies within the scope of "Big Data" that can further our understanding of diabetes mellitus and osteoporosis from large volumes of data. "Big Data" techniques involving supervised machine learning, unsupervised machine learning, and deep learning image analysis are presented with examples of current literature. Supervised machine learning can allow us to better predict diabetes-induced osteoporosis and understand relative predictor importance of diabetes-affected bone tissue. Unsupervised machine learning can allow us to understand patterns in data between diabetic pathophysiology and altered bone metabolism. Image analysis using deep learning can allow us to be less dependent on surrogate predictors and use large volumes of images to classify diabetes-induced osteoporosis and predict future outcomes directly from images. "Big Data" techniques herald new possibilities to understand diabetes-induced osteoporosis and ascertain our current ability to classify, understand, and predict this condition.

  17. A conceptual review of XBRL in relation to Big Data

    DEFF Research Database (Denmark)

    Krisko, Adam

    The technological developments of the last couple of decades have led to remarkable changes in the role of data within society. Data continues to grow, and the advances in the field of information technology (IT) further accelerate the process. This unfolding phenomenon has resulted in the emerge......The technological developments of the last couple of decades have led to remarkable changes in the role of data within society. Data continues to grow, and the advances in the field of information technology (IT) further accelerate the process. This unfolding phenomenon has resulted...... the increasingly important role of Big Data within the accounting domain, by discussing the potential benefits and challenges the expansion in the volume, velocity, and variety of accounting data carries. One of the possible responses to the potential changes Big Data might foster within accounting...... is the utilisation of eXtensible Business Reporting Language (XBRL). XBRL is an open standard, free of license fees electronic language for communicating financial and business information. Storing data in XBRL format enables it to be machine-readable, and standardises the financial terms through XBRL taxonomy...

  18. Big Data, Data Analyst, and Improving the Competence of Librarian

    Directory of Open Access Journals (Sweden)

    Albertus Pramukti Narendra

    2016-01-01

    Full Text Available Issue of Big Data was already raised by Fremont Rider, an American Librarian from Westleyan University, in 1944. He predicted that the volume of American universities collection would reach 200 million copies in 2040. As a result, it brings to fore multiple issues such as big data users, storage capacity, and the need to have data analysts. In Indonesia, data analysts is still a rare profession, and therefore urgently needed. One of its distinctive tasks is to conduct visual analyses from various data resources and also to present the result visually as interesting knowledge. It becomes science enliven by interactive visualization. In response to the issue, librarians have already been equipped with basic information management. Yet, they can see the opportunity and improve themselves as data analysts. In developed countries, it is common that librarian are also regarded as data analysts. They enhance themselves with various skills required, such as cloud computing and smart computing. In the end librarian with data analysts competency are eloquent to extract and present complex data resources as interesting and discernible knowledge.

  19. Eksplorasi Teknologi Big Data Hadoop Untuk Sistem Aplikasi Berbasis Komunitas

    Directory of Open Access Journals (Sweden)

    Gede Karya

    2017-11-01

    Full Text Available Pada tahun 2014, telah dikembangkan aplikasi pembukuan untuk usaha mikro dan kecil (UMK berbasis mobile cloud. Aplikasi tersebut dikembangkan dengan teknologi mobile berbasis Android, teknologi web dan web service serta menggunakan basis data MySQL sebagai back-end. Dengan populasi usaha mikro sebanyak 55,1 juta unit usaha di Indonesia dan terus berkembang, maka aplikasi pembukuan UMK berpotensi digunakan oleh banyak user. Hal ini menimbulkan kebutuhan akan layanan pengelolaan data yang sangat besar baik volume maupun pertumbuhannya. Oleh karena itu, perlu dipersiapkan sisi back-end dengan teknologi big-data processing untuk menjamin ketersediaan dan kehandalan layanan kepada pengguna UMK. Makalah ini fokus pada eksplorasi teknologi big-data Hadoop yang saat ini banyak diterapkan untuk aplikasi komunitas seperti: Google, Facebook, Twitter, dan Amazon. Pembahasan diawali dengan studi tentang Hadoop dan ekosistemnya, kemudian merumuskan pola adopsi untuk aplikasi berbasis komunitas. Setelah itu pola dan teknologi tersebut diterapkan untuk mengembangkan back-end aplikasi pembukuan UMK berbasis mobile cloud. Hasil studi dan penerapannya menunjukkan bahwa Hadoop dapat diadopsi pada aplikasi pembukuan UMK khususnya HBase. Untuk memudahkan akses dan meminimalkan usaha modifikasi, maka akses HBase dari aplikasi dapat menggunakan Apache Phoenix Java Data Base Connectivity (JDBC dari beberapa opsi yang tersedia.

  20. Real-time analysis of healthcare using big data analytics

    Science.gov (United States)

    Basco, J. Antony; Senthilkumar, N. C.

    2017-11-01

    Big Data Analytics (BDA) provides a tremendous advantage where there is a need of revolutionary performance in handling large amount of data that covers 4 characteristics such as Volume Velocity Variety Veracity. BDA has the ability to handle such dynamic data providing functioning effectiveness and exceptionally beneficial output in several day to day applications for various organizations. Healthcare is one of the sectors which generate data constantly covering all four characteristics with outstanding growth. There are several challenges in processing patient records which deals with variety of structured and unstructured format. Inducing BDA in to Healthcare (HBDA) will deal with sensitive patient driven information mostly in unstructured format comprising of prescriptions, reports, data from imaging system, etc., the challenges will be overcome by big data with enhanced efficiency in fetching and storing of data. In this project, dataset alike Electronic Medical Records (EMR) produced from numerous medical devices and mobile applications will be induced into MongoDB using Hadoop framework with Improvised processing technique to improve outcome of processing patient records.