Big Bang Day: 5 Particles - 3. The Anti-particle

CERN Multimedia

Franck Close

2008-01-01

Simon Singh looks at the stories behind the discovery of 5 of the universe's most significant subatomic particles: the Electron, the Quark, the Anti-particle, the Neutrino and the "next particle". 3. The Anti-particle. It appears to be the stuff of science fiction. Associated with every elementary particle is an antiparticle which has the same mass and opposite charge. Should the two meet and combine, the result is annihilation - and a flash of light. Thanks to mysterious processes that occurred after the Big Bang there are a vastly greater number of particles than anti-particles. So how could their elusive existence be proved? At CERN particle physicists are crashing together subatomic particles at incredibly high speeds to create antimatter, which they hope will finally reveal what happened at the precise moment of the Big Bang to create the repertoire of elementary particles and antiparticles in existence today.

Big Data - What is it and why it matters.

Science.gov (United States)

Tattersall, Andy; Grant, Maria J

2016-06-01

Big data, like MOOCs, altmetrics and open access, is a term that has been commonplace in the library community for some time yet, despite its prevalence, many in the library and information sector remain unsure of the relationship between big data and their roles. This editorial explores what big data could mean for the day-to-day practice of health library and information workers, presenting examples of big data in action, considering the ethics of accessing big data sets and the potential for new roles for library and information workers. © 2016 Health Libraries Group.

Big Data, Big Responsibility! Building best-practice privacy strategies into a large-scale neuroinformatics platform

Directory of Open Access Journals (Sweden)

Christina Popovich

2017-04-01

OBI’s rigorous approach to data sharing in the field of neuroscience maintains the accessibility of research data for big discoveries without compromising patient privacy and security. We believe that Brain-CODE is a powerful and advantageous tool; moving neuroscience research from independent silos to an integrative system approach for improving patient health. OBI’s vision for improved brain health for patients living with neurological disorders paired with Brain-CODE’s best-practice strategies in privacy protection of patient data offer a novel and innovative approach to “big data” initiatives aimed towards improving public health and society world-wide.

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)

Big data analytics a management perspective

CERN Document Server

Corea, Francesco

2016-01-01

This book is about innovation, big data, and data science seen from a business perspective. Big data is a buzzword nowadays, and there is a growing necessity within practitioners to understand better the phenomenon, starting from a clear stated definition. This book aims to be a starting reading for executives who want (and need) to keep the pace with the technological breakthrough introduced by new analytical techniques and piles of data. Common myths about big data will be explained, and a series of different strategic approaches will be provided. By browsing the book, it will be possible to learn how to implement a big data strategy and how to use a maturity framework to monitor the progress of the data science team, as well as how to move forward from one stage to the next. Crucial challenges related to big data will be discussed, where some of them are more general - such as ethics, privacy, and ownership – while others concern more specific business situations (e.g., initial public offering, growth st...

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

Think big, start small, move fast a blueprint for transformation from the Mayo Clinic Center for Innovation

CERN Document Server

LaRusso, Nicholas; Farrugia, Gianrico

2015-01-01

The Only Innovation Guide You Will Ever Need--from the Award-Winning Minds at Mayo Clinic. A lot of businesspeople talk about innovation, but few companies have achieved the level of truly transformative innovation as brilliantly--or as famously--as the legendary Mayo Clinic. Introducing Think Big, Start Small, Move Fast, the first innovation guide based on the proven, decade-long program that’s made Mayo Clinic one of the most respected and successful organizations in the world. This essential must-have guide shows you how to: Inspire and ignite trailblazing innovation in your workplace Design a new business model that’s creative, collaborative, and sustainable Apply the traditional scientific method to the latest innovations in "design thinking" Build a customized toolkit of the best practices, project portfolios, and strategies Increase your innovation capacity--and watch how quickly you succeed These field-tested techniques grew out of the health care industry but are designed ...

The SIKS/BiGGrid Big Data Tutorial

NARCIS (Netherlands)

Hiemstra, Djoerd; Lammerts, Evert; de Vries, A.P.

2011-01-01

The School for Information and Knowledge Systems SIKS and the Dutch e-science grid BiG Grid organized a new two-day tutorial on Big Data at the University of Twente on 30 November and 1 December 2011, just preceding the Dutch-Belgian Database Day. The tutorial is on top of some exciting new

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, ...

Big Data Caching for Networking: Moving from Cloud to Edge

OpenAIRE

Zeydan, Engin; Baştuğ, Ejder; Bennis, Mehdi; Kader, Manhal Abdel; Karatepe, Alper; Er, Ahmet Salih; Debbah, Mérouane

2016-01-01

In order to cope with the relentless data tsunami in $5G$ wireless networks, current approaches such as acquiring new spectrum, deploying more base stations (BSs) and increasing nodes in mobile packet core networks are becoming ineffective in terms of scalability, cost and flexibility. In this regard, context-aware $5$G networks with edge/cloud computing and exploitation of \\emph{big data} analytics can yield significant gains to mobile operators. In this article, proactive content caching in...

Research on Technology Innovation Management in Big Data Environment

Science.gov (United States)

Ma, Yanhong

2018-02-01

With the continuous development and progress of the information age, the demand for information is getting larger. The processing and analysis of information data is also moving toward the direction of scale. The increasing number of information data makes people have higher demands on processing technology. The explosive growth of information data onto the current society have prompted the advent of the era of big data. At present, people have more value and significance in producing and processing various kinds of information and data in their lives. How to use big data technology to process and analyze information data quickly to improve the level of big data management is an important stage to promote the current development of information and data processing technology in our country. To some extent, innovative research on the management methods of information technology in the era of big data can enhance our overall strength and make China be an invincible position in the development of the big data era.

Infectious Disease Surveillance in the Big Data Era

DEFF Research Database (Denmark)

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

2016-01-01

, 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...... 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...

A proposed framework of big data readiness in public sectors

Science.gov (United States)

Ali, Raja Haslinda Raja Mohd; Mohamad, Rosli; Sudin, Suhizaz

2016-08-01

Growing interest over big data mainly linked to its great potential to unveil unforeseen pattern or profiles that support organisation's key business decisions. Following private sector moves to embrace big data, the government sector has now getting into the bandwagon. Big data has been considered as one of the potential tools to enhance service delivery of the public sector within its financial resources constraints. Malaysian government, particularly, has considered big data as one of the main national agenda. Regardless of government commitment to promote big data amongst government agencies, degrees of readiness of the government agencies as well as their employees are crucial in ensuring successful deployment of big data. This paper, therefore, proposes a conceptual framework to investigate perceived readiness of big data potentials amongst Malaysian government agencies. Perceived readiness of 28 ministries and their respective employees will be assessed using both qualitative (interview) and quantitative (survey) approaches. The outcome of the study is expected to offer meaningful insight on factors affecting change readiness among public agencies on big data potentials and the expected outcome from greater/lower change readiness among the public sectors.

Early processing variations in selective attention to the color and direction of moving stimuli during 30 days head-down bed rest

Science.gov (United States)

Wang, Lin-Jie; He, Si-Yang; Niu, Dong-Bin; Guo, Jian-Ping; Xu, Yun-Long; Wang, De-Sheng; Cao, Yi; Zhao, Qi; Tan, Cheng; Li, Zhi-Li; Tang, Guo-Hua; Li, Yin-Hui; Bai, Yan-Qiang

2013-11-01

Dynamic variations in early selective attention to the color and direction of moving stimuli were explored during a 30 days period of head-down bed rest. Event-related potentials (ERPs) were recorded at F5, F6, P5, P6 scalp locations in seven male subjects who attended to pairs of bicolored light emitting diodes that flashed sequentially to produce a perception of movement. Subjects were required to attend selectively to a critical feature of the moving target, e.g., color or direction. The tasks included: a no response task, a color selective response task, a moving direction selective response task, and a combined color-direction selective response task. Subjects were asked to perform these four tasks on: the 3rd day before bed rest; the 3rd, 15th and 30th day during the bed rest; and the 5th day after bed rest. Subjects responded quickly to the color than moving direction and combined color-direction response. And they had a longer reaction time during bed rest on the 15th and 30th day during bed rest after a relatively quicker response on the 3rd day. Using brain event-related potentials technique, we found that in the color selective response task, the mean amplitudes of P1 and N1 for target ERPs decreased in the 3rd day during bed rest and 5th day after bed rest in comparison with pre-bed rest, 15th day and 30th day during bed rest. In the combined color-direction selective response task, the P1 latencies for target ERPs on the 3rd and 30th day during bed rest were longer than on the 15th day during bed rest. As 3rd day during bed rest was in the acute adaptation period and 30th day during bed rest was in the relatively adaptation stage of head-down bed rest, the results help to clarify the effects of bed rest on different task loads and patterns of attention. It was suggested that subjects expended more time to give correct decision in the head-down tilt bed rest state. A difficulty in the recruitment of brain resources was found in feature selection task

The Big-Wheel TGC-1 being moved against the Barrel Muon Spectrometer. The 216 trigger chambers are supported by a thin structure of 22 m diameter and 0.4 m thickness, weighting 44 tons and supported on two rails.

CERN Multimedia

Claudia Marcelloni

2006-01-01

The Big-Wheel TGC-1 being moved against the Barrel Muon Spectrometer. The 216 trigger chambers are supported by a thin structure of 22 m diameter and 0.4 m thickness, weighting 44 tons and supported on two rails.

Environmental effects of the Big Rapids dam remnant removal, Big Rapids, Michigan, 2000-02

Science.gov (United States)

Healy, Denis F.; Rheaume, Stephen J.; Simpson, J. Alan

2003-01-01

The U.S. Geological Survey (USGS), in cooperation with the city of Big Rapids, investigated the environmental effects of removal of a dam-foundation remnant and downstream cofferdam from the Muskegon River in Big Rapids, Mich. The USGS applied a multidiscipline approach, which determined the water quality, sediment character, and stream habitat before and after dam removal. Continuous water-quality data and discrete water-quality samples were collected, the movement of suspended and bed sediment were measured, changes in stream habitat were assessed, and streambed elevations were surveyed. Analyses of water upstream and downstream from the dam showed that the dam-foundation remnant did not affect water quality. Dissolved-oxygen concentrations downstream from the dam remnant were depressed for a short period (days) during the beginning of the dam removal, in part because of that removal effort. Sediment transport from July 2000 through March 2002 was 13,800 cubic yards more at the downstream site than the upstream site. This increase in sediment represents the remobilized sediment upstream from the dam, bank erosion when the impoundment was lowered, and contributions from small tributaries between the sites. Five habitat reaches were monitored before and after dam-remnant removal. The reaches consisted of a reference reach (A), upstream from the effects of the impoundment; the impoundment (B); and three sites below the impoundment where habitat changes were expected (C, D, and E, in downstream order). Stream-habitat assessment reaches varied in their responses to the dam-remnant removal. Reference reach A was not affected. In impoundment reach B, Great Lakes and Environmental Assessment Section (GLEAS) Procedure 51 ratings went from fair to excellent. For the three downstream reaches, reach C underwent slight habitat degradation, but ratings remained good; reach D underwent slight habitat degradation with ratings changing from excellent to good; and, in an area

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

Cosmological analogy between the big bang and a supernova

Energy Technology Data Exchange (ETDEWEB)

Sen, S. (Hamburg, Germany, F.R.)

1983-10-01

The author presents an objection to Brown's (1981) analogy between a supernova and the Big Bang. According to Brown an expanding spherical shell is quite similar to an ejected supernova shell. However, the fragmented shell of a supernova moves outward in pre-existing space. The force of repulsion which makes the fragments of the shell drift apart can be regarded as equivalent to the force of attraction of the rest of the universe on the supernova. By definition, such a force of attraction is absent in the case of the Big Bang. Energy is supposed suddenly to appear simultaneously at all points throughout the universe at the time of the Big Bang. As the universe expands, space expands too. In the relativistic cosmology, the universe cannot expand in pre-existing space.

Privacy as virtue : Moving beyond the individual in the age of big data

NARCIS (Netherlands)

van der Sloot, B.

2017-01-01

This book discusses whether a rights-based approach to privacy regulation still suffices to address the challenges triggered by new data processing techniques such as Big Data and mass surveillance. A rights-based approach generally grants subjective rights to individuals to protect their personal

Dog days of summer: Influences on decision of wolves to move pups

Science.gov (United States)

Ausband, David E.; Mitchell, Michael S.; Bassing, Sarah B.; Nordhagen, Matthew; Smith, Douglas W.; Stahler, Daniel R.

2016-01-01

For animals that forage widely, protecting young from predation can span relatively long time periods due to the inability of young to travel with and be protected by their parents. Moving relatively immobile young to improve access to important resources, limit detection of concentrated scent by predators, and decrease infestations by ectoparasites can be advantageous. Moving young, however, can also expose them to increased mortality risks (e.g., accidents, getting lost, predation). For group-living animals that live in variable environments and care for young over extended time periods, the influence of biotic factors (e.g., group size, predation risk) and abiotic factors (e.g., temperature and precipitation) on the decision to move young is unknown. We used data from 25 satellite-collared wolves ( Canis lupus ) in Idaho, Montana, and Yellowstone National Park to evaluate how these factors could influence the decision to move pups during the pup-rearing season. We hypothesized that litter size, the number of adults in a group, and perceived predation risk would positively affect the number of times gray wolves moved pups. We further hypothesized that wolves would move their pups more often when it was hot and dry to ensure sufficient access to water. Contrary to our hypothesis, monthly temperature above the 30-year average was negatively related to the number of times wolves moved their pups. Monthly precipitation above the 30-year average, however, was positively related to the amount of time wolves spent at pup-rearing sites after leaving the natal den. We found little relationship between risk of predation (by grizzly bears, humans, or conspecifics) or group and litter sizes and number of times wolves moved their pups. Our findings suggest that abiotic factors most strongly influence the decision of wolves to move pups, although responses to unpredictable biotic events (e.g., a predator encountering pups) cannot be ruled out.

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…

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

Collaborative Approaches Needed to Close the Big Data Skills Gap

Directory of Open Access Journals (Sweden)

Steven Miller

2014-04-01

Full Text Available The big data and analytics talent discussion has largely focused on a single role – the data scientist. However, the need is much broader than data scientists. Data has become a strategic business asset. Every professional occupation must adapt to this new mindset. Universities in partnership with industry must move quickly to ensure that the graduates they produce have the required skills for the age of big data. Existing curricula should be reviewed and adapted to ensure relevance. New curricula and degree programs are needed to meet the needs of industry.

The Moving image

DEFF Research Database (Denmark)

Hansen, Lennard Højbjerg

2014-01-01

Every day we are presented with bodily expressions in audiovisual media – by anchors, journalists and characters in films for instance. This article explores how body language in the moving image has been and can be approached in a scholarly manner.......Every day we are presented with bodily expressions in audiovisual media – by anchors, journalists and characters in films for instance. This article explores how body language in the moving image has been and can be approached in a scholarly manner....

Student-Centered Coaching: The Moves

Science.gov (United States)

Sweeney, Diane; Harris, Leanna S.

2017-01-01

Student-centered coaching is a highly-effective, evidence-based coaching model that shifts the focus from "fixing" teachers to collaborating with them to design instruction that targets student outcomes. But what does this look like in practice? "Student-Centered Coaching: The Moves" shows you the day-to-day coaching moves that…

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

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

A cosmological analogy between the big bang and a supernova

International Nuclear Information System (INIS)

Sen, S.

1983-01-01

The author presents an objection to Brown's (1981) analogy between a supernova and the Big Bang. According to Brown an expanding spherical shell is quite similar to an ejected supernova shell. However, the fragmented shell of a supernova moves outward in pre-existing space. The force of repulsion which makes the fragments of the shell drift apart can be regarded as equivalent to the force of attraction of the rest of the universe on the supernova. By definition, such a force of attraction is absent in the case of the Big Bang. Energy is supposed suddenly to appear simultaneously at all points throughout the universe at the time of the Big Bang. As the universe expands, space expands too. In the relativistic cosmology, the universe cannot expand in pre-existing space. (Auth.)

Using exponentially weighted moving average algorithm to defend against DDoS attacks

CSIR Research Space (South Africa)

Machaka, P

2016-11-01

Full Text Available This paper seeks to investigate the performance of the Exponentially Weighted Moving Average (EWMA) for mining big data and detection of DDoS attacks in Internet of Things (IoT) infrastructure. The paper will investigate the tradeoff between...

Big Bang Day : Afternoon Play - Torchwood: Lost Souls

CERN Multimedia

2008-01-01

Martha Jones, ex-time traveller and now working as a doctor for a UN task force, has been called to CERN where they're about to activate the Large Hadron Collider. Once activated, the Collider will fire beams of protons together recreating conditions a billionth of a second after the Big Bang - and potentially allowing the human race a greater insight into what the Universe is made of. But so much could go wrong - it could open a gateway to a parallel dimension, or create a black hole - and now voices from the past are calling out to people and scientists have started to disappear... Where have the missing scientists gone? What is the secret of the glowing man? What is lurking in the underground tunnel? And do the dead ever really stay dead? Lost Souls is a spin-off from the award-winning BBC Wales TV production Torchwood. It stars John Barrowman, Freema Agyeman, Eve Myles, Gareth David-Lloyd, Lucy Montgomery (of Titty Bang Bang) and Stephen Critchlow.

Relational Databases and Biomedical Big Data.

Science.gov (United States)

de Silva, N H Nisansa D

2017-01-01

In various biomedical applications that collect, handle, and manipulate data, the amounts of data tend to build up and venture into the range identified as bigdata. In such occurrences, a design decision has to be taken as to what type of database would be used to handle this data. More often than not, the default and classical solution to this in the biomedical domain according to past research is relational databases. While this used to be the norm for a long while, it is evident that there is a trend to move away from relational databases in favor of other types and paradigms of databases. However, it still has paramount importance to understand the interrelation that exists between biomedical big data and relational databases. This chapter will review the pros and cons of using relational databases to store biomedical big data that previous researches have discussed and used.

Opportunity and Challenges for Migrating Big Data Analytics in Cloud

Science.gov (United States)

Amitkumar Manekar, S.; Pradeepini, G., Dr.

2017-08-01

Big Data Analytics is a big word now days. As per demanding and more scalable process data generation capabilities, data acquisition and storage become a crucial issue. Cloud storage is a majorly usable platform; the technology will become crucial to executives handling data powered by analytics. Now a day’s trend towards “big data-as-a-service” is talked everywhere. On one hand, cloud-based big data analytics exactly tackle in progress issues of scale, speed, and cost. But researchers working to solve security and other real-time problem of big data migration on cloud based platform. This article specially focused on finding possible ways to migrate big data to cloud. Technology which support coherent data migration and possibility of doing big data analytics on cloud platform is demanding in natute for new era of growth. This article also gives information about available technology and techniques for migration of big data in cloud.

Questioning Big Data: Crowdsourcing crisis data towards an inclusive humanitarian response

Directory of Open Access Journals (Sweden)

Femke Mulder

2016-08-01

Full Text Available The aim of this paper is to critically explore whether crowdsourced Big Data enables an inclusive humanitarian response at times of crisis. We argue that all data, including Big Data, are socially constructed artefacts that reflect the contexts and processes of their creation. To support our argument, we qualitatively analysed the process of ‘Big Data making’ that occurred by way of crowdsourcing through open data platforms, in the context of two specific humanitarian crises, namely the 2010 earthquake in Haiti and the 2015 earthquake in Nepal. We show that the process of creating Big Data from local and global sources of knowledge entails the transformation of information as it moves from one distinct group of contributors to the next. The implication of this transformation is that locally based, affected people and often the original ‘crowd’ are excluded from the information flow, and from the interpretation process of crowdsourced crisis knowledge, as used by formal responding organizations, and are marginalized in their ability to benefit from Big Data in support of their own means. Our paper contributes a critical perspective to the debate on participatory Big Data, by explaining the process of in and exclusion during data making, towards more responsive humanitarian relief.

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…

ATLAS BigPanDA Monitoring

CERN Document Server

Padolski, Siarhei; The ATLAS collaboration; Klimentov, Alexei; Korchuganova, Tatiana

2017-01-01

BigPanDA monitoring is a web based application which provides various processing and representation of the Production and Distributed Analysis (PanDA) system objects states. Analyzing hundreds of millions of computation entities such as an event or a job BigPanDA monitoring builds different scale and levels of abstraction reports in real time mode. Provided information allows users to drill down into the reason of a concrete event failure or observe system bigger picture such as tracking the computation nucleus and satellites performance or the progress of whole production campaign. PanDA system was originally developed for the Atlas experiment and today effectively managing more than 2 million jobs per day distributed over 170 computing centers worldwide. BigPanDA is its core component commissioned in the middle of 2014 and now is the primary source of information for ATLAS users about state of their computations and the source of decision support information for shifters, operators and managers. In this wor...

ATLAS BigPanDA Monitoring

CERN Document Server

Padolski, Siarhei; The ATLAS collaboration

2017-01-01

BigPanDA monitoring is a web-based application that provides various processing and representation of the Production and Distributed Analysis (PanDA) system objects states. Analysing hundreds of millions of computation entities such as an event or a job BigPanDA monitoring builds different scale and levels of abstraction reports in real time mode. Provided information allows users to drill down into the reason of a concrete event failure or observe system bigger picture such as tracking the computation nucleus and satellites performance or the progress of whole production campaign. PanDA system was originally developed for the Atlas experiment and today effectively managing more than 2 million jobs per day distributed over 170 computing centers worldwide. BigPanDA is its core component commissioned in the middle of 2014 and now is the primary source of information for ATLAS users about state of their computations and the source of decision support information for shifters, operators and managers. In this work...

Improving Healthcare Using Big Data Analytics

Directory of Open Access Journals (Sweden)

Revanth Sonnati

2017-03-01

Full Text Available In daily terms we call the current era as Modern Era which can also be named as the era of Big Data in the field of Information Technology. Our daily lives in todays world are rapidly advancing never quenching ones thirst. The fields of science engineering and technology are producing data at an exponential rate leading to Exabytes of data every day. Big data helps us to explore and re-invent many areas not limited to education health and law. The primary purpose of this paper is to provide an in-depth analysis in the area of Healthcare using the big data and analytics. The main purpose is to emphasize on the usage of the big data which is being stored all the time helping to look back in the history but this is the time to emphasize on the analyzation to improve the medication and services. Although many big data implementations happen to be in-house development this proposed implementation aims to propose a broader extent using Hadoop which just happen to be the tip of the iceberg. The focus of this paper is not limited to the improvement and analysis of the data it also focusses on the strengths and drawbacks compared to the conventional techniques available.

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."

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.

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

SDN Low Latency for Medical Big Data Using Wavelets

Directory of Open Access Journals (Sweden)

Fadia Shah

2017-06-01

Full Text Available New era is the age of 5G. The network has moved from the simple internet connection towards advanced LTE connections and transmission. The information and communication technology has reshaped telecommunication. For this, among many types of big data, Medical Big Data is one of the most sensitive forms of data. Wavelet is a technical tool to reduce the size of this data to make it available for the user for more time. It is also responsible for low latency and high speed data transmission over the network. The key concern is the Medical Big Data should be accurate and reliable enough so that the recommended treatment should be the concerned one. This paper proposed the scheme to support the concept of data availability without losing crucial information, via Wavelet the Medical Data compression and through SDN supportive architecture by making data availability over the wireless network. Such scheme is in favor of the efficient use of technology for the benefit of human beings in the support of medical treatments.

ATLAS BigPanDA Monitoring and Its Evolution

CERN Document Server

Wenaus, Torre; The ATLAS collaboration; Korchuganova, Tatiana

2016-01-01

BigPanDA is the latest generation of the monitoring system for the Production and Distributed Analysis (PanDA) system. The BigPanDA monitor is a core component of PanDA and also serves the monitoring needs of the new ATLAS Production System Prodsys-2. BigPanDA has been developed to serve the growing computation needs of the ATLAS Experiment and the wider applications of PanDA beyond ATLAS. Through a system-wide job database, the BigPanDA monitor provides a comprehensive and coherent view of the tasks and jobs executed by the system, from high level summaries to detailed drill-down job diagnostics. The system has been in production and has remained in continuous development since mid 2014, today effectively managing more than 2 million jobs per day distributed over 150 computing centers worldwide. BigPanDA also delivers web-based analytics and system state views to groups of users including distributed computing systems operators, shifters, physicist end-users, computing managers and accounting services. Provi...

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.

One Second After the Big Bang

CERN Multimedia

CERN. Geneva

2014-01-01

A new experiment called PTOLEMY (Princeton Tritium Observatory for Light, Early-Universe, Massive-Neutrino Yield) is under development at the Princeton Plasma Physics Laboratory with the goal of challenging one of the most fundamental predictions of the Big Bang – the present-day existence of relic neutrinos produced less than one second after the Big Bang. Using a gigantic graphene surface to hold 100 grams of a single-atomic layer of tritium, low noise antennas that sense the radio waves of individual electrons undergoing cyclotron motion, and a massive array of cryogenic sensors that sit at the transition between normal and superconducting states, the PTOLEMY project has the potential to challenge one of the most fundamental predictions of the Big Bang, to potentially uncover new interactions and properties of the neutrinos, and to search for the existence of a species of light dark matter known as sterile neutrinos.

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

Revisiting the Battle of the Little Big Horn

National Research Council Canada - National Science Library

Burns, Matthew

2000-01-01

The Battle of the Little Big Horn has captured the interest of historians, scholars, and military enthusiasts since the day that over 200 United States soldiers under General George Armstrong Custer's...

Big endothelin changes the cellular miRNA environment in TMOb osteoblasts and increases mineralization.

Science.gov (United States)

Johnson, Michael G; Kristianto, Jasmin; Yuan, Baozhi; Konicke, Kathryn; Blank, Robert

2014-08-01

Endothelin (ET1) promotes the growth of osteoblastic breast and prostate cancer metastases. Conversion of big ET1 to mature ET1, catalyzed primarily by endothelin converting enzyme 1 (ECE1), is necessary for ET1's biological activity. We previously identified the Ece1, locus as a positional candidate gene for a pleiotropic quantitative trait locus affecting femoral size, shape, mineralization, and biomechanical performance. We exposed TMOb osteoblasts continuously to 25 ng/ml big ET1. Cells were grown for 6 days in growth medium and then switched to mineralization medium for an additional 15 days with or without big ET1, by which time the TMOb cells form mineralized nodules. We quantified mineralization by alizarin red staining and analyzed levels of miRNAs known to affect osteogenesis. Micro RNA 126-3p was identified by search as a potential regulator of sclerostin (SOST) translation. TMOb cells exposed to big ET1 showed greater mineralization than control cells. Big ET1 repressed miRNAs targeting transcripts of osteogenic proteins. Big ET1 increased expression of miRNAs that target transcripts of proteins that inhibit osteogenesis. Big ET1 increased expression of 126-3p 121-fold versus control. To begin to assess the effect of big ET1 on SOST production we analyzed both SOST transcription and protein production with and without the presence of big ET1 demonstrating that transcription and translation were uncoupled. Our data show that big ET1 signaling promotes mineralization. Moreover, the results suggest that big ET1's osteogenic effects are potentially mediated through changes in miRNA expression, a previously unrecognized big ET1 osteogenic mechanism.

People on the Move

Science.gov (United States)

Mohan, Audrey

2018-01-01

The purpose of this 2-3 day lesson is to introduce students in Grades 2-4 to the idea that people move around the world for a variety of reasons. In this activity, students explore why people move through class discussion, a guided reading, and interviews. The teacher elicits student ideas using the compelling question (Dimension 1 of the C3…

Surface urban heat island across 419 global big cities.

Science.gov (United States)

Peng, Shushi; Piao, Shilong; Ciais, Philippe; Friedlingstein, Pierre; Ottle, Catherine; Bréon, François-Marie; Nan, Huijuan; Zhou, Liming; Myneni, Ranga B

2012-01-17

Urban heat island is among the most evident aspects of human impacts on the earth system. Here we assess the diurnal and seasonal variation of surface urban heat island intensity (SUHII) defined as the surface temperature difference between urban area and suburban area measured from the MODIS. Differences in SUHII are analyzed across 419 global big cities, and we assess several potential biophysical and socio-economic driving factors. Across the big cities, we show that the average annual daytime SUHII (1.5 ± 1.2 °C) is higher than the annual nighttime SUHII (1.1 ± 0.5 °C) (P < 0.001). But no correlation is found between daytime and nighttime SUHII across big cities (P = 0.84), suggesting different driving mechanisms between day and night. The distribution of nighttime SUHII correlates positively with the difference in albedo and nighttime light between urban area and suburban area, while the distribution of daytime SUHII correlates negatively across cities with the difference of vegetation cover and activity between urban and suburban areas. Our results emphasize the key role of vegetation feedbacks in attenuating SUHII of big cities during the day, in particular during the growing season, further highlighting that increasing urban vegetation cover could be one effective way to mitigate the urban heat island effect.

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...

Functional connectomics from a "big data" perspective.

Science.gov (United States)

Xia, Mingrui; He, Yong

2017-10-15

In the last decade, explosive growth regarding functional connectome studies has been observed. Accumulating knowledge has significantly contributed to our understanding of the brain's functional network architectures in health and disease. With the development of innovative neuroimaging techniques, the establishment of large brain datasets and the increasing accumulation of published findings, functional connectomic research has begun to move into the era of "big data", which generates unprecedented opportunities for discovery in brain science and simultaneously encounters various challenging issues, such as data acquisition, management and analyses. Big data on the functional connectome exhibits several critical features: high spatial and/or temporal precision, large sample sizes, long-term recording of brain activity, multidimensional biological variables (e.g., imaging, genetic, demographic, cognitive and clinic) and/or vast quantities of existing findings. We review studies regarding functional connectomics from a big data perspective, with a focus on recent methodological advances in state-of-the-art image acquisition (e.g., multiband imaging), analysis approaches and statistical strategies (e.g., graph theoretical analysis, dynamic network analysis, independent component analysis, multivariate pattern analysis and machine learning), as well as reliability and reproducibility validations. We highlight the novel findings in the application of functional connectomic big data to the exploration of the biological mechanisms of cognitive functions, normal development and aging and of neurological and psychiatric disorders. We advocate the urgent need to expand efforts directed at the methodological challenges and discuss the direction of applications in this field. Copyright © 2017 Elsevier Inc. All rights reserved.

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

77 FR 74829 - Notice of Public Meeting-Cloud Computing and Big Data Forum and Workshop

Science.gov (United States)

2012-12-18

...--Cloud Computing and Big Data Forum and Workshop AGENCY: National Institute of Standards and Technology... Standards and Technology (NIST) announces a Cloud Computing and Big Data Forum and Workshop to be held on... followed by a one-day hands-on workshop. The NIST Cloud Computing and Big Data Forum and Workshop will...

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...

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…

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.

A Cognitive Adopted Framework for IoT Big-Data Management and Knowledge Discovery Prospective

OpenAIRE

Mishra, Nilamadhab; Lin, Chung-Chih; Chang, Hsien-Tsung

2015-01-01

In future IoT big-data management and knowledge discovery for large scale industrial automation application, the importance of industrial internet is increasing day by day. Several diversified technologies such as IoT (Internet of Things), computational intelligence, machine type communication, big-data, and sensor technology can be incorporated together to improve the data management and knowledge discovery efficiency of large scale automation applications. So in this work, we need to propos...

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

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...

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

Global fluctuation spectra in big-crunch-big-bang string vacua

International Nuclear Information System (INIS)

Craps, Ben; Ovrut, Burt A.

2004-01-01

We study big-crunch-big-bang cosmologies that correspond to exact world-sheet superconformal field theories of type II strings. The string theory spacetime contains a big crunch and a big bang cosmology, as well as additional 'whisker' asymptotic and intermediate regions. Within the context of free string theory, we compute, unambiguously, the scalar fluctuation spectrum in all regions of spacetime. Generically, the big crunch fluctuation spectrum is altered while passing through the bounce singularity. The change in the spectrum is characterized by a function Δ, which is momentum and time dependent. We compute Δ explicitly and demonstrate that it arises from the whisker regions. The whiskers are also shown to lead to 'entanglement' entropy in the big bang region. Finally, in the Milne orbifold limit of our superconformal vacua, we show that Δ→1 and, hence, the fluctuation spectrum is unaltered by the big-crunch-big-bang singularity. We comment on, but do not attempt to resolve, subtleties related to gravitational back reaction and light winding modes when interactions are taken into account

The (Big Data-security assemblage: Knowledge and critique

Directory of Open Access Journals (Sweden)

Claudia Aradau

2015-10-01

Full Text Available The Snowden revelations and the emergence of ‘Big Data’ have rekindled questions about how security practices are deployed in a digital age and with what political effects. While critical scholars have drawn attention to the social, political and legal challenges to these practices, the debates in computer and information science have received less analytical attention. This paper proposes to take seriously the critical knowledge developed in information and computer science and reinterpret their debates to develop a critical intervention into the public controversies concerning data-driven security and digital surveillance. The paper offers a two-pronged contribution: on the one hand, we challenge the credibility of security professionals’ discourses in light of the knowledge that they supposedly mobilize; on the other, we argue for a series of conceptual moves around data, human–computer relations, and algorithms to address some of the limitations of existing engagements with the Big Data-security assemblage.

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.

Big Data: an exploration of research, technologies and application cases

Directory of Open Access Journals (Sweden)

Emilcy J. Hernández-Leal

2017-05-01

Full Text Available Big Data has become a worldwide trend and although still lacks a scientific or academic consensual concept, every day it portends greater market growth that surrounds and the associated research areas. This paper reports a systematic review of the literature on Big Data considering a state of the art about techniques and technologies associated with Big Data, which include capture, processing, analysis and data visualization. The characteristics, strengths, weaknesses and opportunities for some applications and Big Data models that include support mainly for modeling, analysis, and data mining are explored. Likewise, some of the future trends for the development of Big Data are introduced by basic aspects, scope, and importance of each one. The methodology used for exploration involves the application of two strategies, the first corresponds to a scientometric analysis and the second corresponds to a categorization of documents through a web tool to support the process of literature review. As results, a summary and conclusions about the subject are generated and possible scenarios arise for research work in the field.

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...

Artificial intelligence and big data management: the dynamic duo for moving forward data centric sciences

OpenAIRE

Vargas Solar, Genoveva

2017-01-01

After vivid discussions led by the emergence of the buzzword “Big Data”, it seems that industry and academia have reached an objective understanding about data properties (volume, velocity, variety, veracity and value), the resources and “know how” it requires, and the opportunities it opens. Indeed, new applications promising fundamental changes in society, industry and science, include face recognition, machine translation, digital assistants, self-driving cars, ad-serving, chat-bots, perso...

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

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)

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...

Eating on nightshift: A big vs small snack impairs glucose response to breakfast

Directory of Open Access Journals (Sweden)

Stephanie Centofanti

2018-01-01

Full Text Available Shift work is a risk factor for chronic diseases such as Type 2 diabetes. Food choice may play a role, however simply eating at night when the body is primed for sleep may have implications for health. This study examined the impact of consuming a big versus small snack at night on glucose metabolism. N = 31 healthy subjects (21–35 y; 18 F participated in a simulated nightshift laboratory study that included one baseline night of sleep (22:00 h-07:00 h and one night awake with allocation to either a big snack (2100 kJ or small snack (840 kJ group. The snack was consumed between 00:00–00:30 h and consisted of low fat milk, a sandwich, chips and fruit (big snack or half sandwich and fruit (small snack. Subjects ate an identical mixed meal breakfast (2100 kJ at 08:30 h after one full night of sleep and a simulated nightshift. Interstitial glucose was measured continuously during the entire study using Medtronic Continual Glucose Monitors. Only subjects with identical breakfast consumption and complete datasets were analysed (N = 20. Glucose data were averaged into 5-minute bins and area under the curve (AUC was calculated for 90 min post-breakfast. Pre-breakfast, glucose levels were not significantly different between Day1 and Day2, nor were they different between snack groups (p > 0.05. A snack group by day interaction effect was found (F1,16 = 5.36, p = 0.034 and post-hocs revealed that in the big snack group, AUC response to breakfast was significantly higher following nightshift (Day2 compared to Day1 (p = 0.001. This translated to a 20.8% (SEM 5.6 increase. AUC was not significantly different between days in the small snack group. Consuming a big snack at 00:00 h impaired the glucose response to breakfast at 08:30 h, compared to a smaller snack. Further research in this area will inform dietary advice for shift workers, which could include recommendations on how much to eat as well as content.

Pre-Big-Bang bubbles from the gravitational instability of generic string vacua

International Nuclear Information System (INIS)

Buonanno, A.; Damour, T.; Veneziano, G.

1999-01-01

We formulate the basic postulate of pre-Big-Bang cosmology as one of 'asymptotic past triviality', by which we mean that the initial state is a generic perturbative solution of the tree-level low-energy effective action. Such a past-trivial 'string vacuum' is made of an arbitrary ensemble of incoming gravitational and dilatonic waves, and is generically prone to gravitational instability, leading to the possible formation of many black holes hiding singular space-like hypersurfaces. Each such singular space-like hypersurface of gravitational collapse becomes, in the string-frame metric, the usual Big-Bang t = 0 hypersurface, i.e. the place of birth of a baby Friedmann universe after a period of dilaton-driven inflation. Specializing to the spherically symmetric case, we review and reinterpret previous work on the subject, and propose a simple, scale-invariant criterion for collapse/inflation in terms of asymptotic data at past null infinity. Those data should determine whether, when, and where collapse/inflation occurs, and, when it does, fix its characteristics, including anisotropies on the Big-Bang hypersurface whose imprint could have survived till now. Using Bayesian probability concepts, we finally attempt to answer some fine-tuning objections recently moved to the pre-Big-Bang scenario

Pre-Big-Bang bubbles from the gravitational instability of generic string vacua

Energy Technology Data Exchange (ETDEWEB)

Buonanno, A.; Damour, T.; Veneziano, G

1999-03-22

We formulate the basic postulate of pre-Big-Bang cosmology as one of 'asymptotic past triviality', by which we mean that the initial state is a generic perturbative solution of the tree-level low-energy effective action. Such a past-trivial 'string vacuum' is made of an arbitrary ensemble of incoming gravitational and dilatonic waves, and is generically prone to gravitational instability, leading to the possible formation of many black holes hiding singular space-like hypersurfaces. Each such singular space-like hypersurface of gravitational collapse becomes, in the string-frame metric, the usual Big-Bang t = 0 hypersurface, i.e. the place of birth of a baby Friedmann universe after a period of dilaton-driven inflation. Specializing to the spherically symmetric case, we review and reinterpret previous work on the subject, and propose a simple, scale-invariant criterion for collapse/inflation in terms of asymptotic data at past null infinity. Those data should determine whether, when, and where collapse/inflation occurs, and, when it does, fix its characteristics, including anisotropies on the Big-Bang hypersurface whose imprint could have survived till now. Using Bayesian probability concepts, we finally attempt to answer some fine-tuning objections recently moved to the pre-Big-Bang scenario.

Small decisions with big impact on data analytics

Directory of Open Access Journals (Sweden)

Jana Diesner

2015-11-01

Full Text Available Big social data have enabled new opportunities for evaluating the applicability of social science theories that were formulated decades ago and were often based on small- to medium-sized samples. Big Data coupled with powerful computing has the potential to replace the statistical practice of sampling and estimating effects by measuring phenomena based on full populations. Preparing these data for analysis and conducting analytics involves a plethora of decisions, some of which are already embedded in previously collected data and built tools. These decisions refer to the recording, indexing and representation of data and the settings for analysis methods. While these choices can have tremendous impact on research outcomes, they are not often obvious, not considered or not being made explicit. Consequently, our awareness and understanding of the impact of these decisions on analysis results and derived implications are highly underdeveloped. This might be attributable to occasional high levels of over-confidence in computational solutions as well as the possible yet questionable assumption that Big Data can wash out minor data quality issues, among other reasons. This article provides examples for how to address this issue. It argues that checking, ensuring and validating the quality of big social data and related auxiliary material is a key ingredient for empowering users to gain reliable insights from their work. Scrutinizing data for accuracy issues, systematically fixing them and diligently documenting these processes can have another positive side effect: Closely interacting with the data, thereby forcing ourselves to understand their idiosyncrasies and patterns, can help us to move from being able to precisely model and formally describe effects in society to also understand and explain them.

Ocean Networks Canada's "Big Data" Initiative

Science.gov (United States)

Dewey, R. K.; Hoeberechts, M.; Moran, K.; Pirenne, B.; Owens, D.

2013-12-01

Ocean Networks Canada operates two large undersea observatories that collect, archive, and deliver data in real time over the Internet. These data contribute to our understanding of the complex changes taking place on our ocean planet. Ocean Networks Canada's VENUS was the world's first cabled seafloor observatory to enable researchers anywhere to connect in real time to undersea experiments and observations. Its NEPTUNE observatory is the largest cabled ocean observatory, spanning a wide range of ocean environments. Most recently, we installed a new small observatory in the Arctic. Together, these observatories deliver "Big Data" across many disciplines in a cohesive manner using the Oceans 2.0 data management and archiving system that provides national and international users with open access to real-time and archived data while also supporting a collaborative work environment. Ocean Networks Canada operates these observatories to support science, innovation, and learning in four priority areas: study of the impact of climate change on the ocean; the exploration and understanding the unique life forms in the extreme environments of the deep ocean and below the seafloor; the exchange of heat, fluids, and gases that move throughout the ocean and atmosphere; and the dynamics of earthquakes, tsunamis, and undersea landslides. To date, the Ocean Networks Canada archive contains over 130 TB (collected over 7 years) and the current rate of data acquisition is ~50 TB per year. This data set is complex and diverse. Making these "Big Data" accessible and attractive to users is our priority. In this presentation, we share our experience as a "Big Data" institution where we deliver simple and multi-dimensional calibrated data cubes to a diverse pool of users. Ocean Networks Canada also conducts extensive user testing. Test results guide future tool design and development of "Big Data" products. We strive to bridge the gap between the raw, archived data and the needs and

Some experiences and opportunities for big data in translational research.

Science.gov (United States)

Chute, Christopher G; Ullman-Cullere, Mollie; Wood, Grant M; Lin, Simon M; He, Min; Pathak, Jyotishman

2013-10-01

Health care has become increasingly information intensive. The advent of genomic data, integrated into patient care, significantly accelerates the complexity and amount of clinical data. Translational research in the present day increasingly embraces new biomedical discovery in this data-intensive world, thus entering the domain of "big data." The Electronic Medical Records and Genomics consortium has taught us many lessons, while simultaneously advances in commodity computing methods enable the academic community to affordably manage and process big data. Although great promise can emerge from the adoption of big data methods and philosophy, the heterogeneity and complexity of clinical data, in particular, pose additional challenges for big data inferencing and clinical application. However, the ultimate comparability and consistency of heterogeneous clinical information sources can be enhanced by existing and emerging data standards, which promise to bring order to clinical data chaos. Meaningful Use data standards in particular have already simplified the task of identifying clinical phenotyping patterns in electronic health records.

Big(ger Data as Better Data in Open Distance Learning

Directory of Open Access Journals (Sweden)

Paul Prinsloo

2015-02-01

Full Text Available In the context of the hype, promise and perils of Big Data and the currently dominant paradigm of data-driven decision-making, it is important to critically engage with the potential of Big Data for higher education. We do not question the potential of Big Data, but we do raise a number of issues, and present a number of theses to be seriously considered in realising this potential. The University of South Africa (Unisa is one of the mega ODL institutions in the world with more than 360,000 students and a range of courses and programmes. Unisa already has access to a staggering amount of student data, hosted in disparate sources, and governed by different processes. As the university moves to mainstreaming online learning, the amount of and need for analyses of data are increasing, raising important questions regarding our assumptions, understanding, data sources, systems and processes. This article presents a descriptive case study of the current state of student data at Unisa, as well as explores the impact of existing data sources and analytic approaches. From the analysis it is clear that in order for big(ger data to be better data, a number of issues need to be addressed. The article concludes by presenting a number of theses that should form the basis for the imperative to optimise the harvesting, analysis and use of student data.

Opening the Big Black Box: European study reveals visitors' impressions of science laboratories

CERN Multimedia

2004-01-01

"On 29 - 30 March the findings of 'Inside the Big Black Box'- a Europe-wide science and society project - will be revealed during a two-day seminar hosted by CERN*. The principle aim of Inside the Big Black Box (IN3B) is to determine whether a working scientific laboratory can capture the curiosity of the general public through visits" (1 page)

A study and analysis of recommendation systems for location-based social network (LBSN with big data

Directory of Open Access Journals (Sweden)

Murale Narayanan

2016-03-01

Full Text Available Recommender systems play an important role in our day-to-day life. A recommender system automatically suggests an item to a user that he/she might be interested in. Small-scale datasets are used to provide recommendations based on location, but in real time, the volume of data is large. We have selected Foursquare dataset to study the need for big data in recommendation systems for location-based social network (LBSN. A few quality parameters like parallel processing and multimodal interface have been selected to study the need for big data in recommender systems. This paper provides a study and analysis of quality parameters of recommendation systems for LBSN with big data.

Benchmarking Big Data Systems and the BigData Top100 List.

Science.gov (United States)

Baru, Chaitanya; Bhandarkar, Milind; Nambiar, Raghunath; Poess, Meikel; Rabl, Tilmann

2013-03-01

"Big data" has become a major force of innovation across enterprises of all sizes. New platforms with increasingly more features for managing big datasets are being announced almost on a weekly basis. Yet, there is currently a lack of any means of comparability among such platforms. While the performance of traditional database systems is well understood and measured by long-established institutions such as the Transaction Processing Performance Council (TCP), there is neither a clear definition of the performance of big data systems nor a generally agreed upon metric for comparing these systems. In this article, we describe a community-based effort for defining a big data benchmark. Over the past year, a Big Data Benchmarking Community has become established in order to fill this void. The effort focuses on defining an end-to-end application-layer benchmark for measuring the performance of big data applications, with the ability to easily adapt the benchmark specification to evolving challenges in the big data space. This article describes the efforts that have been undertaken thus far toward the definition of a BigData Top100 List. While highlighting the major technical as well as organizational challenges, through this article, we also solicit community input into this process.

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.

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...

ICRH coupling experiment in Big D

International Nuclear Information System (INIS)

Hoffman, D.J.; Baity, F.W.; Owens, T.L.; Jaeger, E.F.; Bryan, W.E.; Hammonds, C.J.

1985-01-01

A 10 MW, 40 to 80 MHz ICRH experiment has been proposed for Big D (at General Atomic). Compact loop antennas have been chosen to convey this power. In order to verify that the antenna will have sufficient loading, a prototype low-power antenna has been designed and will be installed in January 1986. The antenna is a cavity antenna that will operate from 30 to 80 MHz with a 50 Ohm match at R = 1 Ohm. The antenna can be moved from a position flush with the wall to flush with the limiter. By these means, we will establish the maximum acceptable gap from the coupler to the plasma. The electrical, mechanical, and thermal characteristics of this antenna system will be discussed. In addition to experimental exploration of coupling, we have investigated wave propagation and absorption in Big D by using a cold collisional plasma model in straight tokamak geometry with rotational transform. Although loading is dependent on the plasma position, both the reactive and real loads (10 to 20 and 1 to 2 Ohms) are comparable to other experiments. Loading and power deposition profiles as a function of frequency, density, and species mix will be presented. The report consists of viewgraphs of the presentation

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.

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.

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.

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.

Moving from reclusion to partial freedom: the experience of family caregivers for disabled elderly persons assisted in a day care center.

Science.gov (United States)

Bocchi, Silvia Cristina Mangini; Cano, Karen Cristina Urtado; Baltieri, Lilian; Godoy, Daniele Cristina; Spiri, Wilza Carla; Juliani, Carmen Maria Casquel Monti

2010-09-01

This study aimed at understanding the interactional experience between family caregivers and disabled elderly persons supported in a Day Care Center according to the caregiver's perspective. It also aimed at developing a representative theoretical model for the events experienced by such caregiver. The Grounded Theory was used as methodological framework whereas Interactional Symbolism served as the theoretical framework. Observation and interviews were used for data collection. The following phenomenon arose from the results: feeling of support by the Day Care Center, by the strength of the bond with the elderly and by spirituality in order to continue playing the challenging role of a family caregiver for a disabled elderly person. The study made possible to understand that, among these three supporting cornerstones for coping with the burden generated by the family caregiver role, the care model promoted by the Day Care Center was the intervenient variable in the process of improving the quality of life of the family caregiver-disabled elderly person binomial. This allowed the identification of the main category--moving from reclusion to partial freedom: the experience of family caregivers for disabled elderly persons assisted in a Day Care Center.

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...

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....

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.

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.

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

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.

When Big Ice Turns Into Water It Matters For Houses, Stores And Schools All Over

Science.gov (United States)

Bell, R. E.

2017-12-01

When ice in my glass turns to water it is not bad but when the big ice at the top and bottom of the world turns into water it is not good. This new water makes many houses, stores and schools wet. It is really bad during when the wind is strong and the rain is hard. New old ice water gets all over the place. We can not get to work or school or home. We go to the big ice at the top and bottom of the world to see if it will turn to water soon and make more houses wet. We fly over the big ice to see how it is doing. Most of the big ice sits on rock. Around the edge of the big sitting on rock ice, is really low ice that rides on top of the water. This really low ice slows down the big rock ice turning into water. If the really low ice cracks up and turns into little pieces of ice, the big rock ice will make more houses wet. We look to see if there is new water in the cracks. Water in the cracks is bad as it hurts the big rock ice. Water in the cracks on the really low ice will turn the low ice into many little pieces of ice. Then the big rock ice will turn to water. That is water in cracks is bad for the houses, schools and businesses. If water moves off the really low ice, it does not stay in the cracks. This is better for the really low ice. This is better for the big rock ice. We took pictures of the really low ice and saw water leaving. The water was not staying in the cracks. Water leaving the really low ice might be good for houses, schools and stores.

So, You Want to Move out?!--An Awareness Program of the Real Costs of Moving Away from Home

Science.gov (United States)

Hines, Steven L.; Hansen, Lyle; Falen, Christi

2011-01-01

The So, You Want To Move Out?! program was developed to help teens explore the financial realities of moving away from home. This 3-day camp program allows youth the opportunity to interview for a job, work, earn a paycheck, and pay financial obligations. After paying expenses and trying to put some money away in savings, the participants begin to…

Hepatic Lipidosis in a Research Colony of Big Brown Bats (Eptesicus fuscus)

OpenAIRE

Snyder, Jessica M; Treuting, Piper M; Brabb, Thea; Miller, Kimberly E; Covey, Ellen; Lencioni, Karen L

2015-01-01

During a nearby construction project, a sudden decrease in food intake and guano production occurred in an outdoor colony of big brown bats (Eptesicus fuscus), and one animal was found dead. Investigation revealed that the project was generating a large amount of noise and vibration, which disturbed the bats’ feeding. Consequently the bats were moved into an indoor enclosure away from the construction noises, and the colony resumed eating. Over the next 3 wk, additional animals presented with...

Taking a lot of Pictures of Real Things and Making them into a Single Picture you can Move on a Computer

Science.gov (United States)

Linneman, C.; Hults, C.

2017-12-01

This summer I spent my time in the largest state of all the states, with the people who take care of the most important parks, owned by all of us. My job was to take a lot of pictures of real things, small and large, and to make them into one piece on a computer, into pictures that can be moved and turned and can be easily shared across the world at any time. My job had three different classes: very small, pretty big, and very big. For the small things: Using a table that turns, I took many still pictures of old animals turned into rocks as well as things thrown away by people who are now dead. The pieces of rock and old things are important and exciting, but they can break quite easily, so only a few people are allowed to touch them. With the pictures you can move, many more people can learn about, "touch", and see them, but they use a computer instead of their hands. For a pretty big block of ice moving down a long area of land, I took many pictures of the end of it, while at the same time knowing just where I was on the face of the world. Using a computer, I again put all the pictures together into one picture that could be turned and moved. One person with a computer could look at any part of the piece of ice without having to actually visit it. Finally, for the very big things, I was part of a team that would fly slowly over the areas we were interested in, taking pictures about every half of a second. After taking tens of hundreds of pictures, the computer join all the pictures together into a single picture that showed each and every little up and down of the land that we had flown over, getting very few wrong. This way of making pictures you can move doesn't take as much money as other means, and it can be used on things of very different areas, from something as small as a finger to something as large as a huge field of ice moving slowly over time. The people who care for the parks that we all own don't have as much money as some, and in the biggest state

Significance of Supply Logistics in Big Cities

Directory of Open Access Journals (Sweden)

Mario Šafran

2012-10-01

Full Text Available The paper considers the concept and importance of supplylogistics as element in improving storage, supply and transportof goods in big cities. There is always room for improvements inthis segmenl of economic activities, and therefore continuousoptimisation of the cargo flows from the manufacturer to theend user is impor1a11t. Due to complex requirements in thecargo supply a11d the "spoiled" end users, modem cities represe/ll great difficulties and a big challenge for the supply organisers.The consumers' needs in big cities have developed over therecent years i11 such a way that they require supply of goods severaltimes a day at precisely determined times (orders are receivedby e-mail, and the information transfer is therefore instantaneous.In order to successfully meet the consumers'needs in advanced economic systems, advanced methods ofgoods supply have been developed and improved, such as 'justin time'; ''door-to-door", and "desk-to-desk". Regular operationof these systems requires supply logistics 1vhiclz includes thetotalthroughpw of materials, from receiving the raw materialsor reproduction material to the delive1y of final products to theend users.

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,

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

SOA enabled ELTA: approach in designing business intelligence solutions in Era of Big Data

Directory of Open Access Journals (Sweden)

Viktor Dmitriyev

2015-01-01

Full Text Available The current work presents a new approach for designing business intelligence solutions. In the Era of Big Data, former and robust analytical concepts and utilities need to adapt themselves to the changed market circumstances. The main focus of this work is to address the acceleration of building process of a “data-centric” Business Intelligence (BI solution besides preparing BI solutions for Big Data utilization. This research addresses the following goals: reducing the time spent during business intelligence solution’s design phase; achieving flexibility of BI solution by adding new data sources; and preparing BI solution for utilizing Big Data concepts. This research proposes an extension of the existing Extract, Load and Transform (ELT approach to the new one Extract, Load, Transform and Analyze (ELTA supported by service-orientation concept. Additionally, the proposed model incorporates Service-Oriented Architecture concept as a mediator for the transformation phase. On one side, such incorporation brings flexibility to the BI solution and on the other side; it reduces the complexity of the whole system by moving some responsibilities to external authorities.

Sustainable Development Plan for Korea through Expansion of Green IT: Policy Issues for the Effective Utilization of Big Data

Directory of Open Access Journals (Sweden)

Hyun Baek

2015-01-01

Full Text Available The South Korean government is providing full support for green IT as one of the growth engines of Korea. The purpose of this study is to derive policy issues needed for the sustainable development of Korea through utilizing Big Data by applying green IT. The analysis is done using a Delphi technique. Results show that the establishment of computing platforms that can easily share data and generate value is prioritized for the effective use of Big Data from the environment. In addition, the government-led publication of genetic information and electronic medical records for research purposes has been derived as an important policy issue for the use of bio-Big Data. Besides, a guideline concerning the standardization of machine to machine and Internet of Things communication and data security is needed to effectively use Big Data from machines/things. Moreover, a review of legislation related to the utilization of Big Data from digital media has been derived as an important policy issue. The results of this study propose the direction in which the Korean government should move for green growth through effective utilization of Big Data. The results can be also useful resources for establishing relevant policies for various countries that are accelerating sustainable development.

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

Complexities’ day-to-day dynamic evolution analysis and prediction for a Didi taxi trip network based on complex network theory

Science.gov (United States)

Zhang, Lin; Lu, Jian; Zhou, Jialin; Zhu, Jinqing; Li, Yunxuan; Wan, Qian

2018-03-01

Didi Dache is the most popular taxi order mobile app in China, which provides online taxi-hailing service. The obtained big database from this app could be used to analyze the complexities’ day-to-day dynamic evolution of Didi taxi trip network (DTTN) from the level of complex network dynamics. First, this paper proposes the data cleaning and modeling methods for expressing Nanjing’s DTTN as a complex network. Second, the three consecutive weeks’ data are cleaned to establish 21 DTTNs based on the proposed big data processing technology. Then, multiple topology measures that characterize the complexities’ day-to-day dynamic evolution of these networks are provided. Third, these measures of 21 DTTNs are calculated and subsequently explained with actual implications. They are used as a training set for modeling the BP neural network which is designed for predicting DTTN complexities evolution. Finally, the reliability of the designed BP neural network is verified by comparing with the actual data and the results obtained from ARIMA method simultaneously. Because network complexities are the basis for modeling cascading failures and conducting link prediction in complex system, this proposed research framework not only provides a novel perspective for analyzing DTTN from the level of system aggregated behavior, but can also be used to improve the DTTN management level.

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...

Supporting diagnosis and treatment in medical care based on Big Data processing.

Science.gov (United States)

Lupşe, Oana-Sorina; Crişan-Vida, Mihaela; Stoicu-Tivadar, Lăcrămioara; Bernard, Elena

2014-01-01

With information and data in all domains growing every day, it is difficult to manage and extract useful knowledge for specific situations. This paper presents an integrated system architecture to support the activity in the Ob-Gin departments with further developments in using new technology to manage Big Data processing - using Google BigQuery - in the medical domain. The data collected and processed with Google BigQuery results from different sources: two Obstetrics & Gynaecology Departments, the TreatSuggest application - an application for suggesting treatments, and a home foetal surveillance system. Data is uploaded in Google BigQuery from Bega Hospital Timişoara, Romania. The analysed data is useful for the medical staff, researchers and statisticians from public health domain. The current work describes the technological architecture and its processing possibilities that in the future will be proved based on quality criteria to lead to a better decision process in diagnosis and public health.

Day-to-day variability of the latitudes of the Sq foci

International Nuclear Information System (INIS)

Schlapp, D.M.

1976-01-01

Day-to-day changes in the latitudes of the Sq foci have been studied for several longitudes and at both sunspot maximum and minimum. A small but significant correlation has been found indicating a tendency for the foci to move poleward or equatorward together. There is little correlation between the strength of the electrojet and the separation of the foci; if anything, the electrojet is weaker when the foci are closer together. (author)

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)...

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"

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.)

The Ethical Imperative to Move to a Seven-Day Care Model.

Science.gov (United States)

Bell, Anthony; McDonald, Fiona; Hobson, Tania

2016-06-01

Whilst the nature of human illness is not determined by time of day or day of week, we currently structure health service delivery around a five-day delivery model. At least one country is endeavouring to develop a systems-based approach to planning a transition from five- to seven-day healthcare delivery models, and some services are independently instituting program reorganization to achieve these ends as research, amongst other things, highlights increased mortality and morbidity for weekend and after-hours admissions to hospitals. In this article, we argue that this issue does not merely raise instrumental concerns but also opens up a normative ethical dimension, recognizing that clinical ethical dilemmas are impacted on and created by systems of care. Using health policy ethics, we critically examine whether our health services, as currently structured, are at odds with ethical obligations for patient care and broader collective goals associated with the provision of publicly funded health services. We conclude by arguing that a critical health policy ethics perspective applying relevant ethical values and principles needs to be included when considering whether and how to transition from five-day to seven-day models for health delivery.

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

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.

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.

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...

The Influence Of Switching-Off The Big Lamps On The Humidity Operation Hall

International Nuclear Information System (INIS)

Wiranto, Slamet; Sriawan

2001-01-01

When there is no activity in the Operation Hall, the big lamps in this are switched off. Due to the water trap of ventilation system is not in good function, the humidity of the Operation Hall increases. In any point of time the humidity rise over the permitted limit value. To avoid this problem it is needed to investigate the characteristic by measuring the humidity of the Operation Hall at various condition and situation. From the characteristic, it can be determined that for normal condition, the Operation Hall big lamps should be switched off, and 2 days before start-up reactor, the all operation building lamps should be switched on for about 5 days as the operation building humidity back to normal value

Distilling Big Data: Refining Quality Information in the Era of Yottabytes

Directory of Open Access Journals (Sweden)

Sivaraman Ramachandramurthy

2015-01-01

Full Text Available Big Data is the buzzword of the modern century. With the invasion of pervasive computing, we live in a data centric environment, where we always leave a track of data related to our day to day activities. Be it a visit to a shopping mall or hospital or surfing Internet, we create voluminous data related to credit card transactions, user details, location information, and so on. These trails of data simply define an individual and form the backbone for user-profiling. With the mobile phones and their easy access to online social networks on the go, sensor data such as geo-taggings and events and sentiments around them contribute to the already overwhelming data containers. With reductions in the cost of storage and computational devices and with increasing proliferation of Cloud, we never felt any constraints in storing or processing such data. Eventually we end up having several exabytes of data and analysing them for their usefulness has introduced new frontiers of research. Effective distillation of these data is the need of the hour to improve the veracity of the Big Data. This research targets the utilization of the Fuzzy Bayesian process model to improve the quality of information in Big Data.

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.

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.

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

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.

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....

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.

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....

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

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...

From big data analysis to personalized medicine for all: challenges and opportunities.

Science.gov (United States)

Alyass, Akram; Turcotte, Michelle; Meyre, David

2015-06-27

Recent advances in high-throughput technologies have led to the emergence of systems biology as a holistic science to achieve more precise modeling of complex diseases. Many predict the emergence of personalized medicine in the near future. We are, however, moving from two-tiered health systems to a two-tiered personalized medicine. Omics facilities are restricted to affluent regions, and personalized medicine is likely to widen the growing gap in health systems between high and low-income countries. This is mirrored by an increasing lag between our ability to generate and analyze big data. Several bottlenecks slow-down the transition from conventional to personalized medicine: generation of cost-effective high-throughput data; hybrid education and multidisciplinary teams; data storage and processing; data integration and interpretation; and individual and global economic relevance. This review provides an update of important developments in the analysis of big data and forward strategies to accelerate the global transition to personalized medicine.

The Four Day School Week. Research Brief

Science.gov (United States)

Muir, Mike

2013-01-01

Can four-day school weeks help districts save money? How do districts overcome the barriers of moving to a four-day week? What is the effect of a four-day week on students, staff and the community? This paper enumerates the benefits for students and teachers of four-day school weeks. Recommendations for implementation of a four-day week are also…

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...

Resources available for autism research in the big data era: a systematic review

Directory of Open Access Journals (Sweden)

Reem Al-jawahiri

2017-01-01

Full Text Available Recently, there has been a move encouraged by many stakeholders towards generating big, open data in many areas of research. One area where big, open data is particularly valuable is in research relating to complex heterogeneous disorders such as Autism Spectrum Disorder (ASD. The inconsistencies of findings and the great heterogeneity of ASD necessitate the use of big and open data to tackle important challenges such as understanding and defining the heterogeneity and potential subtypes of ASD. To this end, a number of initiatives have been established that aim to develop big and/or open data resources for autism research. In order to provide a useful data reference for autism researchers, a systematic search for ASD data resources was conducted using the Scopus database, the Google search engine, and the pages on ‘recommended repositories’ by key journals, and the findings were translated into a comprehensive list focused on ASD data. The aim of this review is to systematically search for all available ASD data resources providing the following data types: phenotypic, neuroimaging, human brain connectivity matrices, human brain statistical maps, biospecimens, and ASD participant recruitment. A total of 33 resources were found containing different types of data from varying numbers of participants. Description of the data available from each data resource, and links to each resource is provided. Moreover, key implications are addressed and underrepresented areas of data are identified.

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...

Installation of the first of the big wheels of the ATLAS muon spectrometer, a thin gap chamber (TGC) wheel

CERN Multimedia

Claudia Marcelloni

2006-01-01

The muon spectrometer will include four big moving wheels at each end, each measuring 25 metres in diameter. Of the eight wheels in total, six will be composed of thin gap chambers for the muon trigger system and the other two will consist of monitored drift tubes (MDTs) to measure the position of the muons

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

Big Data access and infrastructure for modern biology: case studies in data repository utility.

Science.gov (United States)

Boles, Nathan C; Stone, Tyler; Bergeron, Charles; Kiehl, Thomas R

2017-01-01

Big Data is no longer solely the purview of big organizations with big resources. Today's routine tools and experimental methods can generate large slices of data. For example, high-throughput sequencing can quickly interrogate biological systems for the expression levels of thousands of different RNAs, examine epigenetic marks throughout the genome, and detect differences in the genomes of individuals. Multichannel electrophysiology platforms produce gigabytes of data in just a few minutes of recording. Imaging systems generate videos capturing biological behaviors over the course of days. Thus, any researcher now has access to a veritable wealth of data. However, the ability of any given researcher to utilize that data is limited by her/his own resources and skills for downloading, storing, and analyzing the data. In this paper, we examine the necessary resources required to engage Big Data, survey the state of modern data analysis pipelines, present a few data repository case studies, and touch on current institutions and programs supporting the work that relies on Big Data. © 2016 New York Academy of Sciences.

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

On Study of Application of Big Data and Cloud Computing Technology in Smart Campus

Science.gov (United States)

Tang, Zijiao

2017-12-01

We live in an era of network and information, which means we produce and face a lot of data every day, however it is not easy for database in the traditional meaning to better store, process and analyze the mass data, therefore the big data was born at the right moment. Meanwhile, the development and operation of big data rest with cloud computing which provides sufficient space and resources available to process and analyze data of big data technology. Nowadays, the proposal of smart campus construction aims at improving the process of building information in colleges and universities, therefore it is necessary to consider combining big data technology and cloud computing technology into construction of smart campus to make campus database system and campus management system mutually combined rather than isolated, and to serve smart campus construction through integrating, storing, processing and analyzing mass data.

The Big Bang: UK Young Scientists' and Engineers' Fair 2010

Science.gov (United States)

Allison, Simon

2010-01-01

The Big Bang: UK Young Scientists' and Engineers' Fair is an annual three-day event designed to promote science, technology, engineering and maths (STEM) careers to young people aged 7-19 through experiential learning. It is supported by stakeholders from business and industry, government and the community, and brings together people from various…

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.

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

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.

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....

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.

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

2011-12-01

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 faced with a variety of options as to inventory length. Furthermore, a 6-factor model has been proposed to extend and update the Big Five model, in part by adding a dimension of Honesty/Humility or Honesty/Propriety. In this study, 3 popular brief to medium-length Big Five measures (NEO Five Factor Inventory, Big Five Inventory [BFI], and International Personality Item Pool), and 3 six-factor measures (HEXACO Personality Inventory, Questionnaire Big Six Scales, and a 6-factor version of the BFI) were placed in competition to best predict important student life outcomes. The effect of test length was investigated by comparing brief versions of most measures (subsets of items) with original versions. Personality questionnaires were administered to undergraduate students (N = 227). Participants' college transcripts and student conduct records were obtained 6-9 months after data was collected. Six-factor inventories demonstrated better predictive ability for life outcomes than did some Big Five inventories. Additional behavioral observations made on participants, including their Facebook profiles and cell-phone text usage, were predicted similarly by Big Five and 6-factor measures. A brief version of the BFI performed surprisingly well; across inventory platforms, increasing test length had little effect on predictive validity. Comparative validity of the models and measures in terms of outcome prediction and parsimony is discussed.

Big Machines and Big Science: 80 Years of Accelerators at Stanford

Energy Technology Data Exchange (ETDEWEB)

Loew, Gregory

2008-12-16

Longtime SLAC physicist Greg Loew will present a trip through SLAC's origins, highlighting its scientific achievements, and provide a glimpse of the lab's future in 'Big Machines and Big Science: 80 Years of Accelerators at Stanford.'

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

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…

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.

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.

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 ...

BigWig and BigBed: enabling browsing of large distributed datasets.

Science.gov (United States)

Kent, W J; Zweig, A S; Barber, G; Hinrichs, A S; Karolchik, D

2010-09-01

BigWig and BigBed files are compressed binary indexed files containing data at several resolutions that allow the high-performance display of next-generation sequencing experiment results in the UCSC Genome Browser. The visualization is implemented using a multi-layered software approach that takes advantage of specific capabilities of web-based protocols and Linux and UNIX operating systems files, R trees and various indexing and compression tricks. As a result, only the data needed to support the current browser view is transmitted rather than the entire file, enabling fast remote access to large distributed data sets. Binaries for the BigWig and BigBed creation and parsing utilities may be downloaded at http://hgdownload.cse.ucsc.edu/admin/exe/linux.x86_64/. Source code for the creation and visualization software is freely available for non-commercial use at http://hgdownload.cse.ucsc.edu/admin/jksrc.zip, implemented in C and supported on Linux. The UCSC Genome Browser is available at http://genome.ucsc.edu.

Comparative influence of dose rate and radiation nature, on lethality after big mammals irradiation

International Nuclear Information System (INIS)

Destombe, C.; Le Fleche, Ph.; Grasseau, A.; Reynal, A.

1997-01-01

For the same dose and the 30 days lethality as biological criterion, the dose rate influence is more important than the radiation nature on the results of an big mammals total body irradiation. (authors)

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

Big data-driven business how to use big data to win customers, beat competitors, and boost profits

CERN Document Server

Glass, Russell

2014-01-01

Get the expert perspective and practical advice on big data The Big Data-Driven Business: How to Use Big Data to Win Customers, Beat Competitors, and Boost Profits makes the case that big data is for real, and more than just big hype. The book uses real-life examples-from Nate Silver to Copernicus, and Apple to Blackberry-to demonstrate how the winners of the future will use big data to seek the truth. Written by a marketing journalist and the CEO of a multi-million-dollar B2B marketing platform that reaches more than 90% of the U.S. business population, this book is a comprehens

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...

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...

Five Big, Big Five Issues : Rationale, Content, Structure, Status, and Crosscultural Assessment

NARCIS (Netherlands)

De Raad, Boele

1998-01-01

This article discusses the rationale, content, structure, status, and crosscultural assessment of the Big Five trait factors, focusing on topics of dispute and misunderstanding. Taxonomic restrictions of the original Big Five forerunner, the "Norman Five," are discussed, and criticisms regarding the

Big data for risk analysis: The future of safe railways

Energy Technology Data Exchange (ETDEWEB)

Figueres Esteban, M.

2016-07-01

New technology brings ever more data to support decision-making for intelligent transport systems. Big Data is no longer a futuristic challenge, it is happening right now: modern railway systems have countless sources of data providing a massive quantity of diverse information on every aspect of operations such as train position and speed, brake applications, passenger numbers, status of the signaling system or reported incidents.The traditional approaches to safety management on the railways have relied on static data sources to populate traditional safety tools such as bow-tie models and fault trees. The Big Data Risk Analysis (BDRA) program for Railways at the University of Huddersfield is investigating how the many Big Data sources from the railway can be combined in a meaningful way to provide a better understanding about the GB railway systems and the environment within which they operate.Moving to BDRA is not simply a matter of scaling-up existing analysis techniques. BDRA has to coordinate and combine a wide range of sources with different types of data and accuracy, and that is not straight-forward. BDRA is structured around three components: data, ontology and visualisation. Each of these components is critical to support the overall framework. This paper describes how these three components are used to get safety knowledge from two data sources by means of ontologies from text documents. This is a part of the ongoing BDRA research that is looking at integrating many large and varied data sources to support railway safety and decision-makers. (Author)

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....

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 ...

Ready, set, move!

CERN Multimedia

Anaïs Schaeffer

2012-01-01

This year, the CERN Medical Service is launching a new public health campaign. Advertised by the catchphrase “Move! & Eat Better”, the particular aim of the campaign is to encourage people at CERN to take more regular exercise, of whatever kind.   The CERN annual relay race is scheduled on 24 May this year. The CERN Medical Service will officially launch its “Move! & Eat Better” campaign at this popular sporting event. “We shall be on hand on the day of the race to strongly advocate regular physical activity,” explains Rachid Belkheir, one of the Medical Service doctors. "We really want to pitch our campaign and answer any questions people may have. Above all we want to set an example. So we are going to walk the same circuit as the runners to underline to people that they can easily incorporate movement into their daily routine.” An underlying concern has prompted this campaign: during their first few year...

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....

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.

Big data - a 21st century science Maginot Line? No-boundary thinking: shifting from the big data paradigm.

Science.gov (United States)

Huang, Xiuzhen; Jennings, Steven F; Bruce, Barry; Buchan, Alison; Cai, Liming; Chen, Pengyin; Cramer, Carole L; Guan, Weihua; Hilgert, Uwe Kk; Jiang, Hongmei; Li, Zenglu; McClure, Gail; McMullen, Donald F; Nanduri, Bindu; Perkins, Andy; Rekepalli, Bhanu; Salem, Saeed; Specker, Jennifer; Walker, Karl; Wunsch, Donald; Xiong, Donghai; Zhang, Shuzhong; Zhang, Yu; Zhao, Zhongming; Moore, Jason H

2015-01-01

Whether your interests lie in scientific arenas, the corporate world, or in government, you have certainly heard the praises of big data: Big data will give you new insights, allow you to become more efficient, and/or will solve your problems. While big data has had some outstanding successes, many are now beginning to see that it is not the Silver Bullet that it has been touted to be. Here our main concern is the overall impact of big data; the current manifestation of big data is constructing a Maginot Line in science in the 21st century. Big data is not "lots of data" as a phenomena anymore; The big data paradigm is putting the spirit of the Maginot Line into lots of data. Big data overall is disconnecting researchers and science challenges. We propose No-Boundary Thinking (NBT), applying no-boundary thinking in problem defining to address science challenges.

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...

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.

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.

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...

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....

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 ...

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…

Big Data Application in Biomedical Research and Health Care: A Literature Review.

Science.gov (United States)

Luo, Jake; Wu, Min; Gopukumar, Deepika; Zhao, Yiqing

2016-01-01

Big data technologies are increasingly used for biomedical and health-care informatics research. Large amounts of biological and clinical data have been generated and collected at an unprecedented speed and scale. For example, the new generation of sequencing technologies enables the processing of billions of DNA sequence data per day, and the application of electronic health records (EHRs) is documenting large amounts of patient data. The cost of acquiring and analyzing biomedical data is expected to decrease dramatically with the help of technology upgrades, such as the emergence of new sequencing machines, the development of novel hardware and software for parallel computing, and the extensive expansion of EHRs. Big data applications present new opportunities to discover new knowledge and create novel methods to improve the quality of health care. The application of big data in health care is a fast-growing field, with many new discoveries and methodologies published in the last five years. In this paper, we review and discuss big data application in four major biomedical subdisciplines: (1) bioinformatics, (2) clinical informatics, (3) imaging informatics, and (4) public health informatics. Specifically, in bioinformatics, high-throughput experiments facilitate the research of new genome-wide association studies of diseases, and with clinical informatics, the clinical field benefits from the vast amount of collected patient data for making intelligent decisions. Imaging informatics is now more rapidly integrated with cloud platforms to share medical image data and workflows, and public health informatics leverages big data techniques for predicting and monitoring infectious disease outbreaks, such as Ebola. In this paper, we review the recent progress and breakthroughs of big data applications in these health-care domains and summarize the challenges, gaps, and opportunities to improve and advance big data applications in health care.

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?”. ...

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.

Big ambitions for small reactors as investors size up power options

Energy Technology Data Exchange (ETDEWEB)

Shepherd, John [nuclear24, Redditch (United Kingdom)

2016-04-15

Earlier this year, US nuclear developer NuScale Power completed a study for the UK's National Nuclear Laboratory (NNL) that supported the suitability of NuScale's small modular reactor (SMR) technology for the effective disposition of plutonium. The UK is a frontrunner to compete in the SMR marketplace, both in terms of technological capabilities, trade and political commitment. Industry observers are openly speculating whether SMR design and construction could start to move ahead faster than 'big and conventional' nuclear construction projects - not just in the UK but worldwide. Economies of scale could increase the attraction of SMRs to investors and the general public.

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.

Changing the personality of a face: Perceived Big Two and Big Five personality factors modeled in real photographs.

Science.gov (United States)

Walker, Mirella; Vetter, Thomas

2016-04-01

General, spontaneous evaluations of strangers based on their faces have been shown to reflect judgments of these persons' intention and ability to harm. These evaluations can be mapped onto a 2D space defined by the dimensions trustworthiness (intention) and dominance (ability). Here we go beyond general evaluations and focus on more specific personality judgments derived from the Big Two and Big Five personality concepts. In particular, we investigate whether Big Two/Big Five personality judgments can be mapped onto the 2D space defined by the dimensions trustworthiness and dominance. Results indicate that judgments of the Big Two personality dimensions almost perfectly map onto the 2D space. In contrast, at least 3 of the Big Five dimensions (i.e., neuroticism, extraversion, and conscientiousness) go beyond the 2D space, indicating that additional dimensions are necessary to describe more specific face-based personality judgments accurately. Building on this evidence, we model the Big Two/Big Five personality dimensions in real facial photographs. Results from 2 validation studies show that the Big Two/Big Five are perceived reliably across different samples of faces and participants. Moreover, results reveal that participants differentiate reliably between the different Big Two/Big Five dimensions. Importantly, this high level of agreement and differentiation in personality judgments from faces likely creates a subjective reality which may have serious consequences for those being perceived-notably, these consequences ensue because the subjective reality is socially shared, irrespective of the judgments' validity. The methodological approach introduced here might prove useful in various psychological disciplines. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

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'.

Psychophysiological responses to competition and the big five personality traits.

Science.gov (United States)

Binboga, Erdal; Guven, Senol; Catıkkaş, Fatih; Bayazıt, Onur; Tok, Serdar

2012-06-01

This study examines the relationship between psychophysiological arousal, cognitive anxiety, and personality traits in young taekwondo athletes. A total of 20 male and 10 female taekwondo athletes (mean age = 18.6 years; ± 1.8) volunteered for the study. The Five Factor Personality Inventory and the state scale of the Spielberger State-Trait Anxiety Inventory (STAI) were used to measure personality and cognitive state anxiety. Electrodermal activity (EDA) was measured twice, one day and approximately one hour prior to the competition, to determine psychophysiological arousal. Descriptive statistics, Pearson product-moment correlations, and stepwise regression were used to analyze the data. Several "Big Five" facets were related to the EDA delta scores that were measured both one day and one hour before the competition. Two stepwise regressions were conducted to examine whether personality traits could significantly predict both EDA delta scores. The final model, containing only neuroticism from the Big Five factors, can significantly explain the variations in the EDA delta scores measured one day before the competition. Agreeableness can significantly explain variations in the EDA delta scores measured one hour before the competition. No relationship was found between cognitive anxiety and the EDA delta scores measured one hour before the competition. In conclusion, personality traits, especially agreeableness and neuroticism, might be useful in understanding arousal responses to competition.

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...

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

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

Study on the application of big cup membranate stent on restenosis after stenting for carcinoma of esophago cardia

International Nuclear Information System (INIS)

Wang Xiuping; Yao Zhongqiang; Liu Jian; Zhang Yan

2007-01-01

Objective: To evaluate the clinical value of self-designed big cup membranate stent on restenosis after stenting for carcinoma of esophago-cardia. Methods: 12 cases of restenosis after stenting for carcinoma of esophago-cardia were involved in the study. Self-designed big cup membranate stent made by Nanjing Weichuang Company (the length of the cup was 3.5 cm)was placed into the constricted stent under guidance of fluoroscopy. Clinical effect, restenosis, and complications were observed during followed up. Results: All the 12 cases of big cup membranat stent placement went along smoothly without indigitation of the cup of the stent. Follow-up of 1.5-8 months showed that 2 cases developed severe restenosis on the big cup of the stem, resulting in third grade difficult deglutition. Among them, one occurred 1 month after stenting, caused by hyperplasia of large amount of granulation tissue; another occurred 6 months after stenting, caused by growth of tumor tissue. 3 cases developed mild to moderate restenosis, 2.3-7 months (mean: 4.6 months) after stenting, with result of first grade difficult deglutition. The remaining 7 cases (mean 5.6 months follow-up) did not have difficulty during deglutition. Conclusions: Application of big cup membranate stent on restenosis after stenting for carcinoma of esophago-cardia can effectively prevent the stent from moving downwards and thus lower down the rate of restenosis, and postpone the occurrence of restenosis. (authors)

How to use Big Data technologies to optimize operations in Upstream Petroleum Industry

Directory of Open Access Journals (Sweden)

Abdelkader Baaziz

2013-12-01

Full Text Available “Big Data is the oil of the new economy” is the most famous citation during the three last years. It has even been adopted by the World Economic Forum in 2011. In fact, Big Data is like crude! It’s valuable, but if unrefined it cannot be used. It must be broken down, analyzed for it to have value. But what about Big Data generated by the Petroleum Industry and particularly its upstream segment? Upstream is no stranger to Big Data. Understanding and leveraging data in the upstream segment enables firms to remain competitive throughout planning, exploration, delineation, and field development.Oil & Gas Companies conduct advanced geophysics modeling and simulation to support operations where 2D, 3D & 4D Seismic generate significant data during exploration phases. They closely monitor the performance of their operational assets. To do this, they use tens of thousands of data-collecting sensors in subsurface wells and surface facilities to provide continuous and real-time monitoring of assets and environmental conditions. Unfortunately, this information comes in various and increasingly complex forms, making it a challenge to collect, interpret, and leverage the disparate data. As an example, Chevron’s internal IT traffic alone exceeds 1.5 terabytes a day.Big Data technologies integrate common and disparate data sets to deliver the right information at the appropriate time to the correct decision-maker. These capabilities help firms act on large volumes of data, transforming decision-making from reactive to proactive and optimizing all phases of exploration, development and production. Furthermore, Big Data offers multiple opportunities to ensure safer, more responsible operations. Another invaluable effect of that would be shared learning.The aim of this paper is to explain how to use Big Data technologies to optimize operations. How can Big Data help experts to decision-making leading the desired outcomes?Keywords:Big Data; Analytics

Moving five-branes and membrane instantons in low energy heterotic M theory

International Nuclear Information System (INIS)

Carlos, Beatriz de; Roberts, Jonathan; Schmoehe, Yaiza

2005-01-01

We study cosmological solutions in the context of four-dimensional low energy heterotic M theory with moving bulk branes. First we present nontrivial, analytic axion solutions generated by new symmetries of the full potential-free action, and we discuss their relation to 'triple axion' solutions found in Pre-Big-Bang cosmologies. Next we consider the presence of a nonperturbative superpotential with and without a background perfect fluid. In the absence of a fluid the dilaton and the T-modulus go to the potential-free solutions at late time, while the moving brane tries to avoid colliding with the boundary and stabilize within the bulk. When the fluid is included the dynamics of the fields change, and we study their behavior both numerically and analytically. In particular we examine the possibility of this setup being a realization of the quintessential scenario and the impact of the fluid on the cosmological stabilization of the moduli

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.

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.)

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...

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.

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

Analytical Cost Metrics : Days of Future Past

Energy Technology Data Exchange (ETDEWEB)

Prajapati, Nirmal [Colorado State Univ., Fort Collins, CO (United States); Rajopadhye, Sanjay [Colorado State Univ., Fort Collins, CO (United States); Djidjev, Hristo Nikolov [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

2018-02-20

As we move towards the exascale era, the new architectures must be capable of running the massive computational problems efficiently. Scientists and researchers are continuously investing in tuning the performance of extreme-scale computational problems. These problems arise in almost all areas of computing, ranging from big data analytics, artificial intelligence, search, machine learning, virtual/augmented reality, computer vision, image/signal processing to computational science and bioinformatics. With Moore’s law driving the evolution of hardware platforms towards exascale, the dominant performance metric (time efficiency) has now expanded to also incorporate power/energy efficiency. Therefore the major challenge that we face in computing systems research is: “how to solve massive-scale computational problems in the most time/power/energy efficient manner?”

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.

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...

Models as Weapons: Review of Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy by Cathy O’Neil (2016

Directory of Open Access Journals (Sweden)

Samuel Tunstall

2018-01-01

Full Text Available Cathy O�Neil. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy (New York, NY: Crown 272 pp. ISBN 978-0553418811. Accessible to a wide readership, Cathy O�Neil�s Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy provides a lucid yet alarming account of the extensive reach of mathematical models in influencing all of our lives. With a particular eye towards social justice, O�Neil not only warns modelers to be cognizant of the effects of their work on real people�especially vulnerable groups who have less power to fight back�but also encourages laypersons to take initiative in learning about the myriad ways in which big data influences their lived experiences. In this review, I highlight O�Neil�s core argument and provide beginning thoughts on how the Numeracy community might take up the book moving forward.

Accounting for the social: Investigating commensuration and Big Data practices at Facebook

Directory of Open Access Journals (Sweden)

Fernando N van der Vlist

2016-02-01

Full Text Available This study explores Big Data practices at Facebook through an investigation of the role of commensuration or ‘the transformation of different qualities into a common metric’ in the structuration of analysis and interaction with a major online social media platform. It proposes a conceptual framework and demonstrates the empirical potential of a pragmatic approach based on reading published materials and available documentation. Facebook’s Data Warehousing and Analytics Infrastructure serves as an illustrative example to begin tracing out and describe data assemblages in more detail. In being attentive to the motivations, drivers and challenges engineers face when dealing with Big Data, it is argued that their solutions can enable and support but also constrain specific analytical and transactional capabilities or data flows between various devices and actors. The analysis thus moves beyond methodological critiques of the utility of Big Data that lack empirical support and specificity. It is further argued that analytics not just describe but also actively participate in the enactment of social worlds, thereby opening possibilities for new markets or market segments to arise. Online sociality accounts for a model of the social that makes it visible and measurable qua markets inviting data recontextualisation and the creation of value along multiple axes. Contra Facebook’s claim to make the web more ‘social’, an investigation of commensuration brings to the fore the question how the social is accounted for in the first place.

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.

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.

Big Bayou Creek and Little Bayou Creek Watershed Monitoring Program

Energy Technology Data Exchange (ETDEWEB)

Kszos, L.A.; Peterson, M.J.; Ryon; Smith, J.G.

1999-03-01

Biological monitoring of Little Bayou and Big Bayou creeks, which border the Paducah Site, has been conducted since 1987. Biological monitoring was conducted by University of Kentucky from 1987 to 1991 and by staff of the Environmental Sciences Division (ESD) at Oak Ridge National Laboratory (ORNL) from 1991 through March 1999. In March 1998, renewed Kentucky Pollutant Discharge Elimination System (KPDES) permits were issued to the US Department of Energy (DOE) and US Enrichment Corporation. The renewed DOE permit requires that a watershed monitoring program be developed for the Paducah Site within 90 days of the effective date of the renewed permit. This plan outlines the sampling and analysis that will be conducted for the watershed monitoring program. The objectives of the watershed monitoring are to (1) determine whether discharges from the Paducah Site and the Solid Waste Management Units (SWMUs) associated with the Paducah Site are adversely affecting instream fauna, (2) assess the ecological health of Little Bayou and Big Bayou creeks, (3) assess the degree to which abatement actions ecologically benefit Big Bayou Creek and Little Bayou Creek, (4) provide guidance for remediation, (5) provide an evaluation of changes in potential human health concerns, and (6) provide data which could be used to assess the impact of inadvertent spills or fish kill. According to the cleanup will result in these watersheds [Big Bayou and Little Bayou creeks] achieving compliance with the applicable water quality criteria.

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.

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.

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…

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...

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.

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...

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...

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.

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.

Market Segmentation in (In)Action: Marketing and 'Yet to Be Installed' Role of Big and Social Media Data

OpenAIRE

Pridmore, Jason; Hämäläinen, Lalu Elias

2017-01-01

textabstract(In)Aktive Marktsegmentierung: Marketing und noch zu installierende Rolle von Big- und Social-Media-Data«. Marketing has always been dependent on the input of new forms of consumer data throughout its history, relying on translations of this data into more and more effective means for targeting and engaging consumers. The focus on the digital segmentation of consumers has been subject to differing marketing orientations, beginning with relationship marketing and moving towards exp...

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.

BIG hydrogen: hydrogen technology in the oil and gas sector

International Nuclear Information System (INIS)

2006-01-01

The BIG Hydrogen workshop was held in Calgary, Alberta, Canada on February 13, 2006. About 60 representatives of industry, academia and government attended this one-day technical meeting on hydrogen production for the oil and gas industry. The following themes were identified from the presentations and discussion: the need to find a BIG hydrogen replacement for Steam Methane Reformer (SMR) because of uncertainty regarding cost and availability of natural gas, although given the maturity of SMR process (reliability, known capital cost) how high will H2 prices have to rise?; need for a national strategy to link the near-term and the longer-term hydrogen production requirements, which can take hydrogen from chemical feedstock to energy carrier; and in the near-term Canada should get involved in demonstrations and build expertise in large hydrogen systems including production and carbon capture and sequestration

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.

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.

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.

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

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

EnviroAtlas - Big Game Hunting Recreation Demand by 12-Digit HUC in the Conterminous United States

Data.gov (United States)

U.S. Environmental Protection Agency — This EnviroAtlas dataset includes the total number of recreational days per year demanded by people ages 18 and over for big game hunting by location in the...

Analysis of Present Day Election Processes vis-à-vis Elections Through Blockchain Technology

OpenAIRE

Hegadekatti, Kartik

2017-01-01

Currently, Democracy is realised through representatives elected by the people. These elections are periodic activities. They involve expenditure of big amounts of manpower, money, time and other resources. It is important to note that during an election, the administration and day-to-day lives of people are affected as election activities take centre stage. Present day elections are amenable to influence where Voters can possibly be intimidated to vote against their will. In many instances, ...

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.

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.

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.

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)

Perspectives on making big data analytics work for oncology.

Science.gov (United States)

El Naqa, Issam

2016-12-01

Oncology, with its unique combination of clinical, physical, technological, and biological data provides an ideal case study for applying big data analytics to improve cancer treatment safety and outcomes. An oncology treatment course such as chemoradiotherapy can generate a large pool of information carrying the 5Vs hallmarks of big data. This data is comprised of a heterogeneous mixture of patient demographics, radiation/chemo dosimetry, multimodality imaging features, and biological markers generated over a treatment period that can span few days to several weeks. Efforts using commercial and in-house tools are underway to facilitate data aggregation, ontology creation, sharing, visualization and varying analytics in a secure environment. However, open questions related to proper data structure representation and effective analytics tools to support oncology decision-making need to be addressed. It is recognized that oncology data constitutes a mix of structured (tabulated) and unstructured (electronic documents) that need to be processed to facilitate searching and subsequent knowledge discovery from relational or NoSQL databases. In this context, methods based on advanced analytics and image feature extraction for oncology applications will be discussed. On the other hand, the classical p (variables)≫n (samples) inference problem of statistical learning is challenged in the Big data realm and this is particularly true for oncology applications where p-omics is witnessing exponential growth while the number of cancer incidences has generally plateaued over the past 5-years leading to a quasi-linear growth in samples per patient. Within the Big data paradigm, this kind of phenomenon may yield undesirable effects such as echo chamber anomalies, Yule-Simpson reversal paradox, or misleading ghost analytics. In this work, we will present these effects as they pertain to oncology and engage small thinking methodologies to counter these effects ranging from

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.

"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...

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...

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…

Big domains are novel Ca²+-binding modules: evidences from big domains of Leptospira immunoglobulin-like (Lig) proteins.

Science.gov (United States)

Raman, Rajeev; Rajanikanth, V; Palaniappan, Raghavan U M; Lin, Yi-Pin; He, Hongxuan; McDonough, Sean P; Sharma, Yogendra; Chang, Yung-Fu

2010-12-29

Many bacterial surface exposed proteins mediate the host-pathogen interaction more effectively in the presence of Ca²+. Leptospiral immunoglobulin-like (Lig) proteins, LigA and LigB, are surface exposed proteins containing Bacterial immunoglobulin like (Big) domains. The function of proteins which contain Big fold is not known. Based on the possible similarities of immunoglobulin and βγ-crystallin folds, we here explore the important question whether Ca²+ binds to a Big domains, which would provide a novel functional role of the proteins containing Big fold. We selected six individual Big domains for this study (three from the conserved part of LigA and LigB, denoted as Lig A3, Lig A4, and LigBCon5; two from the variable region of LigA, i.e., 9(th) (Lig A9) and 10(th) repeats (Lig A10); and one from the variable region of LigB, i.e., LigBCen2. We have also studied the conserved region covering the three and six repeats (LigBCon1-3 and LigCon). All these proteins bind the calcium-mimic dye Stains-all. All the selected four domains bind Ca²+ with dissociation constants of 2-4 µM. Lig A9 and Lig A10 domains fold well with moderate thermal stability, have β-sheet conformation and form homodimers. Fluorescence spectra of Big domains show a specific doublet (at 317 and 330 nm), probably due to Trp interaction with a Phe residue. Equilibrium unfolding of selected Big domains is similar and follows a two-state model, suggesting the similarity in their fold. We demonstrate that the Lig are Ca²+-binding proteins, with Big domains harbouring the binding motif. We conclude that despite differences in sequence, a Big motif binds Ca²+. This work thus sets up a strong possibility for classifying the proteins containing Big domains as a novel family of Ca²+-binding proteins. Since Big domain is a part of many proteins in bacterial kingdom, we suggest a possible function these proteins via Ca²+ binding.

Questioning the "big assumptions". Part II: recognizing organizational contradictions that impede institutional change.

Science.gov (United States)

Bowe, Constance M; Lahey, Lisa; Kegan, Robert; Armstrong, Elizabeth

2003-08-01

Well-designed medical curriculum reforms can fall short of their primary objectives during implementation when unanticipated or unaddressed organizational resistance surfaces. This typically occurs if the agents for change ignore faculty concerns during the planning stage or when the provision of essential institutional safeguards to support new behaviors are neglected. Disappointing outcomes in curriculum reforms then result in the perpetuation of or reversion to the status quo despite the loftiest of goals. Institutional resistance to change, much like that observed during personal development, does not necessarily indicate a communal lack of commitment to the organization's newly stated goals. It may reflect the existence of competing organizational objectives that must be addressed before substantive advances in a new direction can be accomplished. The authors describe how the Big Assumptions process (see previous article) was adapted and applied at the institutional level during a school of medicine's curriculum reform. Reform leaders encouraged faculty participants to articulate their reservations about considered changes to provided insights into the organization's competing commitments. The line of discussion provided an opportunity for faculty to appreciate the gridlock that existed until appropriate test of the school's long held Big Assumptions could be conducted. The Big Assumptions process proved useful in moving faculty groups to recognize and questions the validity of unchallenged institutional beliefs that were likely to undermine efforts toward change. The process also allowed the organization to put essential institutional safeguards in place that ultimately insured that substantive reforms could be sustained.

Act-Frequency Signatures of the Big Five.

Science.gov (United States)

Chapman, Benjamin P; Goldberg, Lewis R

2017-10-01

The traditional focus of work on personality and behavior has tended toward "major outcomes" such as health or antisocial behavior, or small sets of behaviors observable over short periods in laboratories or in convenience samples. In a community sample, we examined a wide set (400) of mundane, incidental or "every day" behavioral acts, the frequencies of which were reported over the past year. Using an exploratory methodology similar to genomic approaches (relying on the False Discovery Rate) revealed 26 prototypical acts for Intellect, 24 acts for Extraversion, 13 for Emotional Stability, nine for Conscientiousness, and six for Agreeableness. Many links were consistent with general intuition-for instance, low Conscientiousness with work and procrastination. Some of the most robust associations, however, were for acts too specific for a priori hypothesis. For instance, Extraversion was strongly associated with telling dirty jokes, Intellect with "loung[ing] around [the] house without clothes on", and Agreeableness with singing in the shower. Frequency categories for these acts changed with markedly non-linearity across Big Five Z-scores. Findings may help ground trait scores in emblematic acts, and enrich understanding of mundane or common behavioral signatures of the Big Five.

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...

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

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.

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

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.

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.

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.

Intelligent Test Mechanism Design of Worn Big Gear

Directory of Open Access Journals (Sweden)

Hong-Yu LIU

2014-10-01

Full Text Available With the continuous development of national economy, big gear was widely applied in metallurgy and mine domains. So, big gear plays an important role in above domains. In practical production, big gear abrasion and breach take place often. It affects normal production and causes unnecessary economic loss. A kind of intelligent test method was put forward on worn big gear mainly aimed at the big gear restriction conditions of high production cost, long production cycle and high- intensity artificial repair welding work. The measure equations transformations were made on involute straight gear. Original polar coordinate equations were transformed into rectangular coordinate equations. Big gear abrasion measure principle was introduced. Detection principle diagram was given. Detection route realization method was introduced. OADM12 laser sensor was selected. Detection on big gear abrasion area was realized by detection mechanism. Tested data of unworn gear and worn gear were led in designed calculation program written by Visual Basic language. Big gear abrasion quantity can be obtained. It provides a feasible method for intelligent test and intelligent repair welding on worn big gear.

[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.

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...

Making big sense from big data in toxicology by read-across.

Science.gov (United States)

Hartung, Thomas

2016-01-01

Modern information technologies have made big data available in safety sciences, i.e., extremely large data sets that may be analyzed only computationally to reveal patterns, trends and associations. This happens by (1) compilation of large sets of existing data, e.g., as a result of the European REACH regulation, (2) the use of omics technologies and (3) systematic robotized testing in a high-throughput manner. All three approaches and some other high-content technologies leave us with big data--the challenge is now to make big sense of these data. Read-across, i.e., the local similarity-based intrapolation of properties, is gaining momentum with increasing data availability and consensus on how to process and report it. It is predominantly applied to in vivo test data as a gap-filling approach, but can similarly complement other incomplete datasets. Big data are first of all repositories for finding similar substances and ensure that the available data is fully exploited. High-content and high-throughput approaches similarly require focusing on clusters, in this case formed by underlying mechanisms such as pathways of toxicity. The closely connected properties, i.e., structural and biological similarity, create the confidence needed for predictions of toxic properties. Here, a new web-based tool under development called REACH-across, which aims to support and automate structure-based read-across, is presented among others.

[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.

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...

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.

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

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.

Harnessing the Power of Big Data to Improve Graduate Medical Education: Big Idea or Bust?

Science.gov (United States)

Arora, Vineet M

2018-06-01

With the advent of electronic medical records (EMRs) fueling the rise of big data, the use of predictive analytics, machine learning, and artificial intelligence are touted as transformational tools to improve clinical care. While major investments are being made in using big data to transform health care delivery, little effort has been directed toward exploiting big data to improve graduate medical education (GME). Because our current system relies on faculty observations of competence, it is not unreasonable to ask whether big data in the form of clinical EMRs and other novel data sources can answer questions of importance in GME such as when is a resident ready for independent practice.The timing is ripe for such a transformation. A recent National Academy of Medicine report called for reforms to how GME is delivered and financed. While many agree on the need to ensure that GME meets our nation's health needs, there is little consensus on how to measure the performance of GME in meeting this goal. During a recent workshop at the National Academy of Medicine on GME outcomes and metrics in October 2017, a key theme emerged: Big data holds great promise to inform GME performance at individual, institutional, and national levels. In this Invited Commentary, several examples are presented, such as using big data to inform clinical experience and provide clinically meaningful data to trainees, and using novel data sources, including ambient data, to better measure the quality of GME training.

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…

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

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

78 FR 3911 - Big Stone National Wildlife Refuge, Big Stone and Lac Qui Parle Counties, MN; Final Comprehensive...

Science.gov (United States)

2013-01-17

... DEPARTMENT OF THE INTERIOR Fish and Wildlife Service [FWS-R3-R-2012-N259; FXRS1265030000-134-FF03R06000] Big Stone National Wildlife Refuge, Big Stone and Lac Qui Parle Counties, MN; Final Comprehensive... significant impact (FONSI) for the environmental assessment (EA) for Big Stone National Wildlife Refuge...

Big domains are novel Ca²+-binding modules: evidences from big domains of Leptospira immunoglobulin-like (Lig proteins.

Directory of Open Access Journals (Sweden)

Rajeev Raman

Full Text Available BACKGROUND: Many bacterial surface exposed proteins mediate the host-pathogen interaction more effectively in the presence of Ca²+. Leptospiral immunoglobulin-like (Lig proteins, LigA and LigB, are surface exposed proteins containing Bacterial immunoglobulin like (Big domains. The function of proteins which contain Big fold is not known. Based on the possible similarities of immunoglobulin and βγ-crystallin folds, we here explore the important question whether Ca²+ binds to a Big domains, which would provide a novel functional role of the proteins containing Big fold. PRINCIPAL FINDINGS: We selected six individual Big domains for this study (three from the conserved part of LigA and LigB, denoted as Lig A3, Lig A4, and LigBCon5; two from the variable region of LigA, i.e., 9(th (Lig A9 and 10(th repeats (Lig A10; and one from the variable region of LigB, i.e., LigBCen2. We have also studied the conserved region covering the three and six repeats (LigBCon1-3 and LigCon. All these proteins bind the calcium-mimic dye Stains-all. All the selected four domains bind Ca²+ with dissociation constants of 2-4 µM. Lig A9 and Lig A10 domains fold well with moderate thermal stability, have β-sheet conformation and form homodimers. Fluorescence spectra of Big domains show a specific doublet (at 317 and 330 nm, probably due to Trp interaction with a Phe residue. Equilibrium unfolding of selected Big domains is similar and follows a two-state model, suggesting the similarity in their fold. CONCLUSIONS: We demonstrate that the Lig are Ca²+-binding proteins, with Big domains harbouring the binding motif. We conclude that despite differences in sequence, a Big motif binds Ca²+. This work thus sets up a strong possibility for classifying the proteins containing Big domains as a novel family of Ca²+-binding proteins. Since Big domain is a part of many proteins in bacterial kingdom, we suggest a possible function these proteins via Ca²+ binding.

Development of imaging biomarkers and generation of big data.

Science.gov (United States)

Alberich-Bayarri, Ángel; Hernández-Navarro, Rafael; Ruiz-Martínez, Enrique; García-Castro, Fabio; García-Juan, David; Martí-Bonmatí, Luis

2017-06-01

Several image processing algorithms have emerged to cover unmet clinical needs but their application to radiological routine with a clear clinical impact is still not straightforward. Moving from local to big infrastructures, such as Medical Imaging Biobanks (millions of studies), or even more, Federations of Medical Imaging Biobanks (in some cases totaling to hundreds of millions of studies) require the integration of automated pipelines for fast analysis of pooled data to extract clinically relevant conclusions, not uniquely linked to medical imaging, but in combination to other information such as genetic profiling. A general strategy for the development of imaging biomarkers and their integration in the cloud for the quantitative management and exploitation in large databases is herein presented. The proposed platform has been successfully launched and is being validated nowadays among the early adopters' community of radiologists, clinicians, and medical imaging researchers.

Numerical simulation of groundwater and surface-water interactions in the Big River Management Area, central Rhode Island

Science.gov (United States)

Masterson, John P.; Granato, Gregory E.

2013-01-01

The Rhode Island Water Resources Board is considering use of groundwater resources from the Big River Management Area in central Rhode Island because increasing water demands in Rhode Island may exceed the capacity of current sources. Previous water-resources investigations in this glacially derived, valley-fill aquifer system have focused primarily on the effects of potential groundwater-pumping scenarios on streamflow depletion; however, the effects of groundwater withdrawals on wetlands have not been assessed, and such assessments are a requirement of the State’s permitting process to develop a water supply in this area. A need for an assessment of the potential effects of pumping on wetlands in the Big River Management Area led to a cooperative agreement in 2008 between the Rhode Island Water Resources Board, the U.S. Geological Survey, and the University of Rhode Island. This partnership was formed with the goal of developing methods for characterizing wetland vegetation, soil type, and hydrologic conditions, and monitoring and modeling water levels for pre- and post-water-supply development to assess potential effects of groundwater withdrawals on wetlands. This report describes the hydrogeology of the area and the numerical simulations that were used to analyze the interaction between groundwater and surface water in response to simulated groundwater withdrawals. The results of this analysis suggest that, given the hydrogeologic conditions in the Big River Management Area, a standard 5-day aquifer test may not be sufficient to determine the effects of pumping on water levels in nearby wetlands. Model simulations showed water levels beneath Reynolds Swamp declined by about 0.1 foot after 5 days of continuous pumping, but continued to decline by an additional 4 to 6 feet as pumping times were increased from a 5-day simulation period to a simulation period representative of long-term average monthly conditions. This continued decline in water levels with

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

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.

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...

Scalable privacy-preserving big data aggregation mechanism

Directory of Open Access Journals (Sweden)

Dapeng Wu

2016-08-01

Full Text Available As the massive sensor data generated by large-scale Wireless Sensor Networks (WSNs recently become an indispensable part of ‘Big Data’, the collection, storage, transmission and analysis of the big sensor data attract considerable attention from researchers. Targeting the privacy requirements of large-scale WSNs and focusing on the energy-efficient collection of big sensor data, a Scalable Privacy-preserving Big Data Aggregation (Sca-PBDA method is proposed in this paper. Firstly, according to the pre-established gradient topology structure, sensor nodes in the network are divided into clusters. Secondly, sensor data is modified by each node according to the privacy-preserving configuration message received from the sink. Subsequently, intra- and inter-cluster data aggregation is employed during the big sensor data reporting phase to reduce energy consumption. Lastly, aggregated results are recovered by the sink to complete the privacy-preserving big data aggregation. Simulation results validate the efficacy and scalability of Sca-PBDA and show that the big sensor data generated by large-scale WSNs is efficiently aggregated to reduce network resource consumption and the sensor data privacy is effectively protected to meet the ever-growing application requirements.

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

Epidemiology in wonderland: Big Data and precision medicine.

Science.gov (United States)

Saracci, Rodolfo

2018-03-01

Big Data and precision medicine, two major contemporary challenges for epidemiology, are critically examined from two different angles. In Part 1 Big Data collected for research purposes (Big research Data) and Big Data used for research although collected for other primary purposes (Big secondary Data) are discussed in the light of the fundamental common requirement of data validity, prevailing over "bigness". Precision medicine is treated developing the key point that high relative risks are as a rule required to make a variable or combination of variables suitable for prediction of disease occurrence, outcome or response to treatment; the commercial proliferation of allegedly predictive tests of unknown or poor validity is commented. Part 2 proposes a "wise epidemiology" approach to: (a) choosing in a context imprinted by Big Data and precision medicine-epidemiological research projects actually relevant to population health, (b) training epidemiologists, (c) investigating the impact on clinical practices and doctor-patient relation of the influx of Big Data and computerized medicine and (d) clarifying whether today "health" may be redefined-as some maintain in purely technological terms.

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.

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.

"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.

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.

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.

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.

BIG DATA IN TAMIL: OPPORTUNITIES, BENEFITS AND CHALLENGES

OpenAIRE

R.S. Vignesh Raj; Babak Khazaei; Ashik Ali

2015-01-01

This paper gives an overall introduction on big data and has tried to introduce Big Data in Tamil. It discusses the potential opportunities, benefits and likely challenges from a very Tamil and Tamil Nadu perspective. The paper has also made original contribution by proposing the ‘big data’s’ terminology in Tamil. The paper further suggests a few areas to explore using big data Tamil on the lines of the Tamil Nadu Government ‘vision 2023’. Whilst, big data has something to offer everyone, it ...

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.

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

On the Geocentric Nature of the Big Bang Theory

Science.gov (United States)

Wang, Ling Jun

2008-04-01

An expanding universe with all heavenly bodies moving isotropically away from the earth seems to suggest a geocentric theory which is evidently false. To defend the Big Bang Theory (BBT) from falling into a geocentric theory, it is argued that if the universe is expanding linearly from the singularity, the heavenly bodies would appear to be leaving away from each other with isotropic velocity distribution with respect to any observer. In this presentation we will prove rigorously with both classical and relativistic analysis that even strict linearity of Hubble's law would not save the Big Bang from falling into a geocentric theory. The key of the analysis rests on the two crucial necessary conditions for the raisin-pudding model: 1) The velocities and the positions of the earth and the galaxies must be measured simultaneously; 2) The velocity transformation between the reference frame of the earth and that of the singularity must be linear. The first condition can not be satisfied due to the speed limit of light; and the second condition can not be satisfied due to non linear velocity transformation of relativity. The whole problem is originated from the Doppler shift explanation of the red shift. Wang's Dispersive Extinction Theory (DET), however, interprets the red shift as being caused by the dispersive extinction of the star light by the space medium, and therefore does not lead to a geocentric universe. This lends a strong support to DET over BBT.

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.

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.

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.

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

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.

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

Don’t miss the Passport to the Big Bang event this Sunday!

CERN Multimedia

CERN Bulletin

2013-01-01

Word has been going around for weeks now about the inauguration of the Passport to the Big Bang on Sunday 2 June. Ideal for a family day out or a day with friends, this is a CERN event not to be missed!   The Passport to the Big Bang is a 54-km scientific tourist trail comprising ten exhibition platforms in front of ten CERN sites in the Pays de Gex and the Canton of Geneva. Linked by cycle routes, these ten platforms will mark the same number of stages in the rally for competitive cyclists and the bicycle tour for families taking place this Sunday from 9 a.m. to 12 p.m. But that’s not all: from 2 p.m., you will also have the chance to take part in a huge range of activities provided by clubs and associations from CERN and the local region. Watch an oriental dance show, have a go at building detectors out of Kapla blocks and Lego, meet different reptile species, learn about wind instruments, try your hand at Nordic walking or Zumba fitness, get a better understanding of road safety...

The Berlin Inventory of Gambling behavior - Screening (BIG-S): Validation using a clinical sample.

Science.gov (United States)

Wejbera, Martin; Müller, Kai W; Becker, Jan; Beutel, Manfred E

2017-05-18

Published diagnostic questionnaires for gambling disorder in German are either based on DSM-III criteria or focus on aspects other than life time prevalence. This study was designed to assess the usability of the DSM-IV criteria based Berlin Inventory of Gambling Behavior Screening tool in a clinical sample and adapt it to DSM-5 criteria. In a sample of 432 patients presenting for behavioral addiction assessment at the University Medical Center Mainz, we checked the screening tool's results against clinical diagnosis and compared a subsample of n=300 clinically diagnosed gambling disorder patients with a comparison group of n=132. The BIG-S produced a sensitivity of 99.7% and a specificity of 96.2%. The instrument's unidimensionality and the diagnostic improvements of DSM-5 criteria were verified by exploratory and confirmatory factor analysis as well as receiver operating characteristic analysis. The BIG-S is a reliable and valid screening tool for gambling disorder and demonstrated its concise and comprehensible operationalization of current DSM-5 criteria in a clinical setting.

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.

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

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...

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.

Mentoring in Schools: An Impact Study of Big Brothers Big Sisters School-Based Mentoring

Science.gov (United States)

Herrera, Carla; Grossman, Jean Baldwin; Kauh, Tina J.; McMaken, Jennifer

2011-01-01

This random assignment impact study of Big Brothers Big Sisters School-Based Mentoring involved 1,139 9- to 16-year-old students in 10 cities nationwide. Youth were randomly assigned to either a treatment group (receiving mentoring) or a control group (receiving no mentoring) and were followed for 1.5 school years. At the end of the first school…

Big data processing in the cloud - Challenges and platforms

Science.gov (United States)

Zhelev, Svetoslav; Rozeva, Anna

2017-12-01

Choosing the appropriate architecture and technologies for a big data project is a difficult task, which requires extensive knowledge in both the problem domain and in the big data landscape. The paper analyzes the main big data architectures and the most widely implemented technologies used for processing and persisting big data. Clouds provide for dynamic resource scaling, which makes them a natural fit for big data applications. Basic cloud computing service models are presented. Two architectures for processing big data are discussed, Lambda and Kappa architectures. Technologies for big data persistence are presented and analyzed. Stream processing as the most important and difficult to manage is outlined. The paper highlights main advantages of cloud and potential problems.

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.

Victoria Stodden: Scholarly Communication in the Era of Big Data and Big Computation

OpenAIRE

Stodden, Victoria

2015-01-01

Victoria Stodden gave the keynote address for Open Access Week 2015. "Scholarly communication in the era of big data and big computation" was sponsored by the University Libraries, Computational Modeling and Data Analytics, the Department of Computer Science, the Department of Statistics, the Laboratory for Interdisciplinary Statistical Analysis (LISA), and the Virginia Bioinformatics Institute. Victoria Stodden is an associate professor in the Graduate School of Library and Information Scien...

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.

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.

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.

Outcome Prediction after Radiotherapy with Medical Big Data.

Science.gov (United States)

Magome, Taiki

2016-01-01

Data science is becoming more important in many fields. In medical physics field, we are facing huge data every day. Treatment outcomes after radiation therapy are determined by complex interactions between clinical, biological, and dosimetrical factors. A key concept of recent radiation oncology research is to predict the outcome based on medical big data for personalized medicine. Here, some reports, which are analyzing medical databases with machine learning techniques, were reviewed and feasibility of outcome prediction after radiation therapy was discussed. In addition, some strategies for saving manual labors to analyze huge data in medical physics were discussed.

Big Data in Cloud Computing: A Resource Management Perspective

Directory of Open Access Journals (Sweden)

Saeed Ullah

2018-01-01

Full Text Available The modern day advancement is increasingly digitizing our lives which has led to a rapid growth of data. Such multidimensional datasets are precious due to the potential of unearthing new knowledge and developing decision-making insights from them. Analyzing this huge amount of data from multiple sources can help organizations to plan for the future and anticipate changing market trends and customer requirements. While the Hadoop framework is a popular platform for processing larger datasets, there are a number of other computing infrastructures, available to use in various application domains. The primary focus of the study is how to classify major big data resource management systems in the context of cloud computing environment. We identify some key features which characterize big data frameworks as well as their associated challenges and issues. We use various evaluation metrics from different aspects to identify usage scenarios of these platforms. The study came up with some interesting findings which are in contradiction with the available literature on the Internet.

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.

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 ...

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

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.

76 FR 55779 - Labor Day, 2011

Science.gov (United States)

2011-09-09

... Proclamation Every day, hard-working men and women across America prove that, even in difficult times, our... we commit to supporting their efforts in moving our economy forward. The right to organize and... Americans today are given opportunities because their parents and grandparents fought for these basic rights...

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...

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.)

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.

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...

Integrating R and Hadoop for Big Data Analysis

OpenAIRE

Bogdan Oancea; Raluca Mariana Dragoescu

2014-01-01

Analyzing and working with big data could be very diffi cult using classical means like relational database management systems or desktop software packages for statistics and visualization. Instead, big data requires large clusters with hundreds or even thousands of computing nodes. Offi cial statistics is increasingly considering big data for deriving new statistics because big data sources could produce more relevant and timely statistics than traditional sources. One of the software tools ...

Autonomous Landing on Moving Platforms

KAUST Repository

Mendoza Chavez, Gilberto

2016-08-01

This thesis investigates autonomous landing of a micro air vehicle (MAV) on a nonstationary ground platform. Unmanned aerial vehicles (UAVs) and micro air vehicles (MAVs) are becoming every day more ubiquitous. Nonetheless, many applications still require specialized human pilots or supervisors. Current research is focusing on augmenting the scope of tasks that these vehicles are able to accomplish autonomously. Precise autonomous landing on moving platforms is essential for self-deployment and recovery of MAVs, but it remains a challenging task for both autonomous and piloted vehicles. Model Predictive Control (MPC) is a widely used and effective scheme to control constrained systems. One of its variants, output-feedback tube-based MPC, ensures robust stability for systems with bounded disturbances under system state reconstruction. This thesis proposes a MAV control strategy based on this variant of MPC to perform rapid and precise autonomous landing on moving targets whose nominal (uncommitted) trajectory and velocity are slowly varying. The proposed approach is demonstrated on an experimental setup.

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.

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.

Moving In, Moving Through, and Moving Out: The Transitional Experiences of Foster Youth College Students

Science.gov (United States)

Gamez, Sara I.

2017-01-01

The purpose of this qualitative study was to explore the transitional experiences of foster youth college students. The study explored how foster youth experienced moving into, moving through, and moving out of the college environment and what resources and strategies they used to thrive during their college transitions. In addition, this study…

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...

BigData and computing challenges in high energy and nuclear physics

Science.gov (United States)

Klimentov, A.; Grigorieva, M.; Kiryanov, A.; Zarochentsev, A.

2017-06-01

In this contribution we discuss the various aspects of the computing resource needs experiments in High Energy and Nuclear Physics, in particular at the Large Hadron Collider. This will evolve in the future when moving from LHC to HL-LHC in ten years from now, when the already exascale levels of data we are processing could increase by a further order of magnitude. The distributed computing environment has been a great success and the inclusion of new super-computing facilities, cloud computing and volunteering computing for the future is a big challenge, which we are successfully mastering with a considerable contribution from many super-computing centres around the world, academic and commercial cloud providers. We also discuss R&D computing projects started recently in National Research Center ``Kurchatov Institute''

BigData and computing challenges in high energy and nuclear physics

International Nuclear Information System (INIS)

Klimentov, A.; Grigorieva, M.; Kiryanov, A.; Zarochentsev, A.

2017-01-01

In this contribution we discuss the various aspects of the computing resource needs experiments in High Energy and Nuclear Physics, in particular at the Large Hadron Collider. This will evolve in the future when moving from LHC to HL-LHC in ten years from now, when the already exascale levels of data we are processing could increase by a further order of magnitude. The distributed computing environment has been a great success and the inclusion of new super-computing facilities, cloud computing and volunteering computing for the future is a big challenge, which we are successfully mastering with a considerable contribution from many super-computing centres around the world, academic and commercial cloud providers. We also discuss R and D computing projects started recently in National Research Center ''Kurchatov Institute''

Physics with Big Karl Brainstorming. Abstracts

International Nuclear Information System (INIS)

Machner, H.; Lieb, J.

2000-08-01

Before summarizing details of the meeting, a short description of the spectrometer facility Big Karl is given. The facility is essentially a new instrument using refurbished dipole magnets from its predecessor. The large acceptance quadrupole magnets and the beam optics are new. Big Karl has a design very similar as the focussing spectrometers at MAMI (Mainz), AGOR (Groningen) and the high resolution spectrometer (HRS) in Hall A at Jefferson Laboratory with ΔE/E = 10 -4 but at some lower maximum momentum. The focal plane detectors consisting of multiwire drift chambers and scintillating hodoscopes are similar. Unlike HRS, Big Karl still needs Cerenkov counters and polarimeters in its focal plane; detectors which are necessary to perform some of the experiments proposed during the brainstorming. In addition, BIG KARL allows emission angle reconstruction via track measurements in its focal plane with high resolution. In the following the physics highlights, the proposed and potential experiments are summarized. During the meeting it became obvious that the physics to be explored at Big Karl can be grouped into five distinct categories, and this summary is organized accordingly. (orig.)

Seed bank and big sagebrush plant community composition in a range margin for big sagebrush

Science.gov (United States)

Martyn, Trace E.; Bradford, John B.; Schlaepfer, Daniel R.; Burke, Ingrid C.; Laurenroth, William K.

2016-01-01

The potential influence of seed bank composition on range shifts of species due to climate change is unclear. Seed banks can provide a means of both species persistence in an area and local range expansion in the case of increasing habitat suitability, as may occur under future climate change. However, a mismatch between the seed bank and the established plant community may represent an obstacle to persistence and expansion. In big sagebrush (Artemisia tridentata) plant communities in Montana, USA, we compared the seed bank to the established plant community. There was less than a 20% similarity in the relative abundance of species between the established plant community and the seed bank. This difference was primarily driven by an overrepresentation of native annual forbs and an underrepresentation of big sagebrush in the seed bank compared to the established plant community. Even though we expect an increase in habitat suitability for big sagebrush under future climate conditions at our sites, the current mismatch between the plant community and the seed bank could impede big sagebrush range expansion into increasingly suitable habitat in the future.

Application and Prospect of Big Data in Water Resources

Science.gov (United States)

Xi, Danchi; Xu, Xinyi

2017-04-01

Because of developed information technology and affordable data storage, we h ave entered the era of data explosion. The term "Big Data" and technology relate s to it has been created and commonly applied in many fields. However, academic studies just got attention on Big Data application in water resources recently. As a result, water resource Big Data technology has not been fully developed. This paper introduces the concept of Big Data and its key technologies, including the Hadoop system and MapReduce. In addition, this paper focuses on the significance of applying the big data in water resources and summarizing prior researches by others. Most studies in this field only set up theoretical frame, but we define the "Water Big Data" and explain its tridimensional properties which are time dimension, spatial dimension and intelligent dimension. Based on HBase, the classification system of Water Big Data is introduced: hydrology data, ecology data and socio-economic data. Then after analyzing the challenges in water resources management, a series of solutions using Big Data technologies such as data mining and web crawler, are proposed. Finally, the prospect of applying big data in water resources is discussed, it can be predicted that as Big Data technology keeps developing, "3D" (Data Driven Decision) will be utilized more in water resources management in the future.

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.

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.

Una aproximación a Big Data = An approach to Big Data

OpenAIRE

Puyol Moreno, Javier

2014-01-01

Big Data puede ser considerada como una tendencia en el avance de la tecnología que ha abierto la puerta a un nuevo enfoque para la comprensión y la toma de decisiones, que se utiliza para describir las enormes cantidades de datos (estructurados, no estructurados y semi- estructurados) que sería demasiado largo y costoso para cargar una base de datos relacional para su análisis. Así, el concepto de Big Data se aplica a toda la información que no puede ser procesada o analizada utilizando herr...

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.

Big Data Analytic, Big Step for Patient Management and Care in Puerto Rico.

Science.gov (United States)

Borrero, Ernesto E

2018-01-01

This letter provides an overview of the application of big data in health care system to improve quality of care, including predictive modelling for risk and resource use, precision medicine and clinical decision support, quality of care and performance measurement, public health and research applications, among others. The author delineates the tremendous potential for big data analytics and discuss how it can be successfully implemented in clinical practice, as an important component of a learning health-care system.

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,

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.

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...

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.

Moving from Administrivia Overload to Leadership Competency Development

Science.gov (United States)

Hogan, Amy M.

2018-01-01

Virginia Tech has intentionally moved beyond the traditional policy-heavy approach to department head orientation to employ a robust leadership development program for new and aspiring academic leaders and faculty. The annual program begins with a full-day workshop in mid-August, and continues throughout the fall semester with a series of shorter…

Classical propagation of strings across a big crunch/big bang singularity

International Nuclear Information System (INIS)

Niz, Gustavo; Turok, Neil

2007-01-01

One of the simplest time-dependent solutions of M theory consists of nine-dimensional Euclidean space times 1+1-dimensional compactified Milne space-time. With a further modding out by Z 2 , the space-time represents two orbifold planes which collide and re-emerge, a process proposed as an explanation of the hot big bang [J. Khoury, B. A. Ovrut, P. J. Steinhardt, and N. Turok, Phys. Rev. D 64, 123522 (2001).][P. J. Steinhardt and N. Turok, Science 296, 1436 (2002).][N. Turok, M. Perry, and P. J. Steinhardt, Phys. Rev. D 70, 106004 (2004).]. When the two planes are near, the light states of the theory consist of winding M2-branes, describing fundamental strings in a particular ten-dimensional background. They suffer no blue-shift as the M theory dimension collapses, and their equations of motion are regular across the transition from big crunch to big bang. In this paper, we study the classical evolution of fundamental strings across the singularity in some detail. We also develop a simple semiclassical approximation to the quantum evolution which allows one to compute the quantum production of excitations on the string and implement it in a simplified example

The Information Panopticon in the Big Data Era

Directory of Open Access Journals (Sweden)

Martin Berner

2014-04-01

Full Text Available Taking advantage of big data opportunities is challenging for traditional organizations. In this article, we take a panoptic view of big data – obtaining information from more sources and making it visible to all organizational levels. We suggest that big data requires the transformation from command and control hierarchies to post-bureaucratic organizational structures wherein employees at all levels can be empowered while simultaneously being controlled. We derive propositions that show how to best exploit big data technologies in organizations.

A data analysis framework for biomedical big data: Application on mesoderm differentiation of human pluripotent stem cells.

Science.gov (United States)

Ulfenborg, Benjamin; Karlsson, Alexander; Riveiro, Maria; Améen, Caroline; Åkesson, Karolina; Andersson, Christian X; Sartipy, Peter; Synnergren, Jane

2017-01-01

The development of high-throughput biomolecular technologies has resulted in generation of vast omics data at an unprecedented rate. This is transforming biomedical research into a big data discipline, where the main challenges relate to the analysis and interpretation of data into new biological knowledge. The aim of this study was to develop a framework for biomedical big data analytics, and apply it for analyzing transcriptomics time series data from early differentiation of human pluripotent stem cells towards the mesoderm and cardiac lineages. To this end, transcriptome profiling by microarray was performed on differentiating human pluripotent stem cells sampled at eleven consecutive days. The gene expression data was analyzed using the five-stage analysis framework proposed in this study, including data preparation, exploratory data analysis, confirmatory analysis, biological knowledge discovery, and visualization of the results. Clustering analysis revealed several distinct expression profiles during differentiation. Genes with an early transient response were strongly related to embryonic- and mesendoderm development, for example CER1 and NODAL. Pluripotency genes, such as NANOG and SOX2, exhibited substantial downregulation shortly after onset of differentiation. Rapid induction of genes related to metal ion response, cardiac tissue development, and muscle contraction were observed around day five and six. Several transcription factors were identified as potential regulators of these processes, e.g. POU1F1, TCF4 and TBP for muscle contraction genes. Pathway analysis revealed temporal activity of several signaling pathways, for example the inhibition of WNT signaling on day 2 and its reactivation on day 4. This study provides a comprehensive characterization of biological events and key regulators of the early differentiation of human pluripotent stem cells towards the mesoderm and cardiac lineages. The proposed analysis framework can be used to structure

WE-H-BRB-00: Big Data in Radiation Oncology

Energy Technology Data Exchange (ETDEWEB)

NONE

2016-06-15

Big Data in Radiation Oncology: (1) Overview of the NIH 2015 Big Data Workshop, (2) Where do we stand in the applications of big data in radiation oncology?, and (3) Learning Health Systems for Radiation Oncology: Needs and Challenges for Future Success The overriding goal of this trio panel of presentations is to improve awareness of the wide ranging opportunities for big data impact on patient quality care and enhancing potential for research and collaboration opportunities with NIH and a host of new big data initiatives. This presentation will also summarize the Big Data workshop that was held at the NIH Campus on August 13–14, 2015 and sponsored by AAPM, ASTRO, and NIH. The workshop included discussion of current Big Data cancer registry initiatives, safety and incident reporting systems, and other strategies that will have the greatest impact on radiation oncology research, quality assurance, safety, and outcomes analysis. Learning Objectives: To discuss current and future sources of big data for use in radiation oncology research To optimize our current data collection by adopting new strategies from outside radiation oncology To determine what new knowledge big data can provide for clinical decision support for personalized medicine L. Xing, NIH/NCI Google Inc.

WE-H-BRB-00: Big Data in Radiation Oncology

International Nuclear Information System (INIS)

2016-01-01

Big Data in Radiation Oncology: (1) Overview of the NIH 2015 Big Data Workshop, (2) Where do we stand in the applications of big data in radiation oncology?, and (3) Learning Health Systems for Radiation Oncology: Needs and Challenges for Future Success The overriding goal of this trio panel of presentations is to improve awareness of the wide ranging opportunities for big data impact on patient quality care and enhancing potential for research and collaboration opportunities with NIH and a host of new big data initiatives. This presentation will also summarize the Big Data workshop that was held at the NIH Campus on August 13–14, 2015 and sponsored by AAPM, ASTRO, and NIH. The workshop included discussion of current Big Data cancer registry initiatives, safety and incident reporting systems, and other strategies that will have the greatest impact on radiation oncology research, quality assurance, safety, and outcomes analysis. Learning Objectives: To discuss current and future sources of big data for use in radiation oncology research To optimize our current data collection by adopting new strategies from outside radiation oncology To determine what new knowledge big data can provide for clinical decision support for personalized medicine L. Xing, NIH/NCI Google Inc.

De impact van Big Data op Internationale Betrekkingen

NARCIS (Netherlands)

Zwitter, Andrej

Big Data changes our daily lives, but does it also change international politics? In this contribution, Andrej Zwitter (NGIZ chair at Groningen University) argues that Big Data impacts on international relations in ways that we only now start to understand. To comprehend how Big Data influences

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

Big data and analytics strategic and organizational impacts

CERN Document Server

Morabito, Vincenzo

2015-01-01

This book presents and discusses the main strategic and organizational challenges posed by Big Data and analytics in a manner relevant to both practitioners and scholars. The first part of the book analyzes strategic issues relating to the growing relevance of Big Data and analytics for competitive advantage, which is also attributable to empowerment of activities such as consumer profiling, market segmentation, and development of new products or services. Detailed consideration is also given to the strategic impact of Big Data and analytics on innovation in domains such as government and education and to Big Data-driven business models. The second part of the book addresses the impact of Big Data and analytics on management and organizations, focusing on challenges for governance, evaluation, and change management, while the concluding part reviews real examples of Big Data and analytics innovation at the global level. The text is supported by informative illustrations and case studies, so that practitioners...

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.

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.

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

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...

Seasonal variation of semen parameters correlates with environmental temperature and air pollution: A big data analysis over 6 years.

Science.gov (United States)

Santi, Daniele; Magnani, Elisa; Michelangeli, Marco; Grassi, Roberto; Vecchi, Barbara; Pedroni, Gioia; Roli, Laura; De Santis, Maria Cristina; Baraldi, Enrica; Setti, Monica; Trenti, Tommaso; Simoni, Manuela

2018-04-01

Male fertility is progressively declining in many developed countries, but the relationship between male infertility and environmental factors is still unclear. To assess the influence of environmental temperature and air pollution on semen parameters, using a big-data approach. A big data analysis of parameters related to 5131 men, living in a province of Northern Italy and undergoing semen analyses between January 2010 and March 2016 was performed. Ambient temperature was recorded on the day of analysis and the 90 days prior to the analysis and the average value of particulate matter (PM) and NO2 in the year of the test. All data were acquired by geocoding patients residential address. A data warehouse containing 990,904,591 data was generated and analysed by multiple regressions. 5573 semen analyses were collected. Both maximum and minimum temperatures registered on the day of collection were inversely related to total sperm number (p < .001), non-progressive motility (NPrM) (p < .005) and normal forms (p < .001). Results were confirmed considering temperature in the 30 and 60 days before collection, but not in the 90 days before collection. Total sperm number was lower in summer/autumn (p < .001) and was inversely related with daylight duration (p < .001). PM10 and PM2.5 were inversely related to PrM (p < .001 and p < .005) and abnormal forms (p < .001). This is the first evaluation of the relationship between male fertility-related parameters and environment using a big-data approach. A seasonal change in semen parameters was found, with a fluctuation related to both temperature and daylight duration. A negative correlation between air pollution and semen quality is suggested. Such seasonal and environmental associations should be considered when assessing changes of male fertility-related parameters over time. Copyright © 2018 Elsevier Ltd. All rights reserved.

Research on information security in big data era

Science.gov (United States)

Zhou, Linqi; Gu, Weihong; Huang, Cheng; Huang, Aijun; Bai, Yongbin

2018-05-01

Big data is becoming another hotspot in the field of information technology after the cloud computing and the Internet of Things. However, the existing information security methods can no longer meet the information security requirements in the era of big data. This paper analyzes the challenges and a cause of data security brought by big data, discusses the development trend of network attacks under the background of big data, and puts forward my own opinions on the development of security defense in technology, strategy and product.

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.

Fuzzy 2-partition entropy threshold selection based on Big Bang–Big Crunch Optimization algorithm

Directory of Open Access Journals (Sweden)

Baljit Singh Khehra

2015-03-01

Full Text Available The fuzzy 2-partition entropy approach has been widely used to select threshold value for image segmenting. This approach used two parameterized fuzzy membership functions to form a fuzzy 2-partition of the image. The optimal threshold is selected by searching an optimal combination of parameters of the membership functions such that the entropy of fuzzy 2-partition is maximized. In this paper, a new fuzzy 2-partition entropy thresholding approach based on the technology of the Big Bang–Big Crunch Optimization (BBBCO is proposed. The new proposed thresholding approach is called the BBBCO-based fuzzy 2-partition entropy thresholding algorithm. BBBCO is used to search an optimal combination of parameters of the membership functions for maximizing the entropy of fuzzy 2-partition. BBBCO is inspired by the theory of the evolution of the universe; namely the Big Bang and Big Crunch Theory. The proposed algorithm is tested on a number of standard test images. For comparison, three different algorithms included Genetic Algorithm (GA-based, Biogeography-based Optimization (BBO-based and recursive approaches are also implemented. From experimental results, it is observed that the performance of the proposed algorithm is more effective than GA-based, BBO-based and recursion-based approaches.

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

Job Surfing: Move On to Move Up.

Science.gov (United States)

Martin, Justin

1997-01-01

Looks at the process of switching jobs and changing careers. Discusses when to consider options and make the move as well as the need to be flexible and open minded. Provides a test for determining the chances of promotion and when to move on. (JOW)

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,

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

About Big Data and its Challenges and Benefits in Manufacturing

OpenAIRE

Bogdan NEDELCU

2013-01-01

The aim of this article is to show the importance of Big Data and its growing influence on companies. It also shows what kind of big data is currently generated and how much big data is estimated to be generated. We can also see how much are the companies willing to invest in big data and how much are they currently gaining from their big data. There are also shown some major influences that big data has over one major segment in the industry (manufacturing) and the challenges that appear.

Big Data Management in US Hospitals: Benefits and Barriers.

Science.gov (United States)

Schaeffer, Chad; Booton, Lawrence; Halleck, Jamey; Studeny, Jana; Coustasse, Alberto

Big data has been considered as an effective tool for reducing health care costs by eliminating adverse events and reducing readmissions to hospitals. The purposes of this study were to examine the emergence of big data in the US health care industry, to evaluate a hospital's ability to effectively use complex information, and to predict the potential benefits that hospitals might realize if they are successful in using big data. The findings of the research suggest that there were a number of benefits expected by hospitals when using big data analytics, including cost savings and business intelligence. By using big data, many hospitals have recognized that there have been challenges, including lack of experience and cost of developing the analytics. Many hospitals will need to invest in the acquiring of adequate personnel with experience in big data analytics and data integration. The findings of this study suggest that the adoption, implementation, and utilization of big data technology will have a profound positive effect among health care providers.

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 ...

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...

[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.

Big data in psychology: A framework for research advancement.

Science.gov (United States)

Adjerid, Idris; Kelley, Ken

2018-02-22

The potential for big data to provide value for psychology is significant. However, the pursuit of big data remains an uncertain and risky undertaking for the average psychological researcher. In this article, we address some of this uncertainty by discussing the potential impact of big data on the type of data available for psychological research, addressing the benefits and most significant challenges that emerge from these data, and organizing a variety of research opportunities for psychology. Our article yields two central insights. First, we highlight that big data research efforts are more readily accessible than many researchers realize, particularly with the emergence of open-source research tools, digital platforms, and instrumentation. Second, we argue that opportunities for big data research are diverse and differ both in their fit for varying research goals, as well as in the challenges they bring about. Ultimately, our outlook for researchers in psychology using and benefiting from big data is cautiously optimistic. Although not all big data efforts are suited for all researchers or all areas within psychology, big data research prospects are diverse, expanding, and promising for psychology and related disciplines. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

'Big data' in pharmaceutical science: challenges and opportunities.

Science.gov (United States)

Dossetter, Al G; Ecker, Gerhard; Laverty, Hugh; Overington, John

2014-05-01

Future Medicinal Chemistry invited a selection of experts to express their views on the current impact of big data in drug discovery and design, as well as speculate on future developments in the field. The topics discussed include the challenges of implementing big data technologies, maintaining the quality and privacy of data sets, and how the industry will need to adapt to welcome the big data era. Their enlightening responses provide a snapshot of the many and varied contributions being made by big data to the advancement of pharmaceutical science.

Connect the Book. Martin's Big Words: The Life of Dr. Martin Luther King, Jr.

Science.gov (United States)

Brodie, Carolyn S.

2005-01-01

In honor of Martin Luther King, Jr. Day, this month's featured book is "Martin's Big Words: The Life of Dr. Martin Luther King, Jr." The book was written by Doreen Rappaport and illustrated by Bryan Collier (Jump at the Sun, 2001. 40p. ISBN 0786807148). This pictorial biography of the world-renowned civil rights leader has one of the most striking…

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...

[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.

Current applications of big data in obstetric anesthesiology.

Science.gov (United States)

Klumpner, Thomas T; Bauer, Melissa E; Kheterpal, Sachin

2017-06-01

The narrative review aims to highlight several recently published 'big data' studies pertinent to the field of obstetric anesthesiology. Big data has been used to study rare outcomes, to identify trends within the healthcare system, to identify variations in practice patterns, and to highlight potential inequalities in obstetric anesthesia care. Big data studies have helped define the risk of rare complications of obstetric anesthesia, such as the risk of neuraxial hematoma in thrombocytopenic parturients. Also, large national databases have been used to better understand trends in anesthesia-related adverse events during cesarean delivery as well as outline potential racial/ethnic disparities in obstetric anesthesia care. Finally, real-time analysis of patient data across a number of disparate health information systems through the use of sophisticated clinical decision support and surveillance systems is one promising application of big data technology on the labor and delivery unit. 'Big data' research has important implications for obstetric anesthesia care and warrants continued study. Real-time electronic surveillance is a potentially useful application of big data technology on the labor and delivery unit.

Earthquake prediction in California using regression algorithms and cloud-based big data infrastructure

Science.gov (United States)

Asencio-Cortés, G.; Morales-Esteban, A.; Shang, X.; Martínez-Álvarez, F.

2018-06-01

Earthquake magnitude prediction is a challenging problem that has been widely studied during the last decades. Statistical, geophysical and machine learning approaches can be found in literature, with no particularly satisfactory results. In recent years, powerful computational techniques to analyze big data have emerged, making possible the analysis of massive datasets. These new methods make use of physical resources like cloud based architectures. California is known for being one of the regions with highest seismic activity in the world and many data are available. In this work, the use of several regression algorithms combined with ensemble learning is explored in the context of big data (1 GB catalog is used), in order to predict earthquakes magnitude within the next seven days. Apache Spark framework, H2 O library in R language and Amazon cloud infrastructure were been used, reporting very promising results.

Big Data viewed through the lens of management fashion theory

OpenAIRE

Madsen, Dag Øivind; Stenheim, Tonny

2016-01-01

Big Data (BD) is currently one of the most talked about management ideas in the business community. Many call it the “buzzword of the day.” In books and media articles, BD has been referred to as a “revolution” and “new era.” There is lots of optimistic and upbeat rhetoric surrounding BD. This has led some to question whether BD is a hyped-up management fashion. In this paper, the BD phenomenon is viewed through the lens of management fashion theory. Management fashion provides an analytical ...

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.

Using Big Book to Teach Things in My House

OpenAIRE

Effrien, Intan; Lailatus, Sa’diyah; Nuruliftitah Maja, Neneng

2017-01-01

The purpose of this study to determine students' interest in learning using the big book media. Big book is a big book from the general book. The big book contains simple words and images that match the content of sentences and spelling. From here researchers can know the interest and development of students' knowledge. As well as train researchers to remain crative in developing learning media for students.

Big Data Analytics Methodology in the Financial Industry

Science.gov (United States)

Lawler, James; Joseph, Anthony

2017-01-01

Firms in industry continue to be attracted by the benefits of Big Data Analytics. The benefits of Big Data Analytics projects may not be as evident as frequently indicated in the literature. The authors of the study evaluate factors in a customized methodology that may increase the benefits of Big Data Analytics projects. Evaluating firms in the…

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.

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

Towards Cloud Processing of GGOS Big Data

Science.gov (United States)

Weston, Stuart; Kim, Bumjun; Litchfield, Alan; Gulyaev, Sergei; Hall, Dylan; Chorao, Carlos; Ruthven, Andrew; Davies, Glyn; Lagos, Bruno; Christie, Don

2017-04-01

We report on our initial steps towards development of a cloud-like correlation infrastructure for geodetic Very Long Baseline Interferometry (VLBI), which in its raw format is of the order of 10-100 TB (big data). Data is generated by multiple VLBI radio telescopes, and is then used by for geodetic, geophysical, and astrometric research and operational activities through the International VLBI Service (IVS), as well as for corrections of GPS satellite orbits. Currently IVS data is correlated in several international Correlators (Correlation Centres), which receive data from individual radio telescope stations either in hard drives via regular mail service or via fibre using e-transfer mode. The latter is strongly limited by connectivity of existing correlation centres, which creates bottle necks and slows down the turnover of the data. This becomes critical in many applications - for example, it currently takes 1-2 weeks to generate the dUT1 parameter for corrections of GNSS orbits while less than 1-2 days delay is desirable. We started with a blade server at the AUT campus to emulate a cloud server using Virtual Machines (VMWare). The New Zealand Data Head node is connected to the high speed (100 Gbps) network ring circuit courtesy of the Research and Education Advanced Network New Zealand (REANNZ), with the additional nodes at remote physical sites connected via 10 Gbps fibre. We use real Australian Long Baseline Array (LBA) observational data from 6 radio telescopes in Australia, South Africa and New Zealand (15 baselines) of 1.5 hours in duration making 8 TB to emulate data transfer from remote locations and to provide a meaningful benchmark dataset for correlation. Data was successfully transferred using bespoke UDT network transfer tools and correlated with the speed-up factor of 0.8 using DiFX software correlator. In partnership with the New Zealand office of Catalyst IT Ltd we have moved this environment into Catalyst Cloud and report on the first

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.

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...

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.

Big data analytics in healthcare: promise and potential.

Science.gov (United States)

Raghupathi, Wullianallur; Raghupathi, Viju

2014-01-01

To describe the promise and potential of big data analytics in healthcare. The paper describes the nascent field of big data analytics in healthcare, discusses the benefits, outlines an architectural framework and methodology, describes examples reported in the literature, briefly discusses the challenges, and offers conclusions. The paper provides a broad overview of big data analytics for healthcare researchers and practitioners. Big data analytics in healthcare is evolving into a promising field for providing insight from very large data sets and improving outcomes while reducing costs. Its potential is great; however there remain challenges to overcome.

Data warehousing in the age of big data

CERN Document Server

Krishnan, Krish

2013-01-01

Data Warehousing in the Age of the Big Data will help you and your organization make the most of unstructured data with your existing data warehouse. As Big Data continues to revolutionize how we use data, it doesn't have to create more confusion. Expert author Krish Krishnan helps you make sense of how Big Data fits into the world of data warehousing in clear and concise detail. The book is presented in three distinct parts. Part 1 discusses Big Data, its technologies and use cases from early adopters. Part 2 addresses data warehousing, its shortcomings, and new architecture

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...

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.

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.

Correcting for day of the week and public holiday effects: improving a national daily syndromic surveillance service for detecting public health threats.

Science.gov (United States)

Buckingham-Jeffery, Elizabeth; Morbey, Roger; House, Thomas; Elliot, Alex J; Harcourt, Sally; Smith, Gillian E

2017-05-19

As service provision and patient behaviour varies by day, healthcare data used for public health surveillance can exhibit large day of the week effects. These regular effects are further complicated by the impact of public holidays. Real-time syndromic surveillance requires the daily analysis of a range of healthcare data sources, including family doctor consultations (called general practitioners, or GPs, in the UK). Failure to adjust for such reporting biases during analysis of syndromic GP surveillance data could lead to misinterpretations including false alarms or delays in the detection of outbreaks. The simplest smoothing method to remove a day of the week effect from daily time series data is a 7-day moving average. Public Health England developed the working day moving average in an attempt also to remove public holiday effects from daily GP data. However, neither of these methods adequately account for the combination of day of the week and public holiday effects. The extended working day moving average was developed. This is a further data-driven method for adding a smooth trend curve to a time series graph of daily healthcare data, that aims to take both public holiday and day of the week effects into account. It is based on the assumption that the number of people seeking healthcare services is a combination of illness levels/severity and the ability or desire of patients to seek healthcare each day. The extended working day moving average was compared to the seven-day and working day moving averages through application to data from two syndromic indicators from the GP in-hours syndromic surveillance system managed by Public Health England. The extended working day moving average successfully smoothed the syndromic healthcare data by taking into account the combined day of the week and public holiday effects. In comparison, the seven-day and working day moving averages were unable to account for all these effects, which led to misleading smoothing

Quantifying the semantics of search behavior before stock market moves.

Science.gov (United States)

Curme, Chester; Preis, Tobias; Stanley, H Eugene; Moat, Helen Susannah

2014-08-12

Technology is becoming deeply interwoven into the fabric of society. The Internet has become a central source of information for many people when making day-to-day decisions. Here, we present a method to mine the vast data Internet users create when searching for information online, to identify topics of interest before stock market moves. In an analysis of historic data from 2004 until 2012, we draw on records from the search engine Google and online encyclopedia Wikipedia as well as judgments from the service Amazon Mechanical Turk. We find evidence of links between Internet searches relating to politics or business and subsequent stock market moves. In particular, we find that an increase in search volume for these topics tends to precede stock market falls. We suggest that extensions of these analyses could offer insight into large-scale information flow before a range of real-world events.

Video Vortex reader II: moving images beyond YouTube

NARCIS (Netherlands)

Lovink, G.; Somers Miles, R.

2011-01-01

Video Vortex Reader II is the Institute of Network Cultures' second collection of texts that critically explore the rapidly changing landscape of online video and its use. With the success of YouTube ('2 billion views per day') and the rise of other online video sharing platforms, the moving image

Big Data Analytics, Infectious Diseases and Associated Ethical Impacts

OpenAIRE

Garattini, C.; Raffle, J.; Aisyah, D. N.; Sartain, F.; Kozlakidis, Z.

2017-01-01

The exponential accumulation, processing and accrual of big data in healthcare are only possible through an equally rapidly evolving field of big data analytics. The latter offers the capacity to rationalize, understand and use big data to serve many different purposes, from improved services modelling to prediction of treatment outcomes, to greater patient and disease stratification. In the area of infectious diseases, the application of big data analytics has introduced a number of changes ...

Evaluation of Data Management Systems for Geospatial Big Data

OpenAIRE

Amirian, Pouria; Basiri, Anahid; Winstanley, Adam C.

2014-01-01

Big Data encompasses collection, management, processing and analysis of the huge amount of data that varies in types and changes with high frequency. Often data component of Big Data has a positional component as an important part of it in various forms, such as postal address, Internet Protocol (IP) address and geographical location. If the positional components in Big Data extensively used in storage, retrieval, analysis, processing, visualization and knowledge discovery (geospatial Big Dat...

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…

West Virginia's big trees: setting the record straight

Science.gov (United States)

Melissa Thomas-Van Gundy; Robert. Whetsell

2016-01-01

People love big trees, people love to find big trees, and people love to find big trees in the place they call home. Having been suspicious for years, my coauthor and historian Rob Whetsell, approached me with a species identification challenge. There are several photographs of giant trees used by many people to illustrate the past forests of West Virginia,...

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...

DETERMINATION OF THE INFLUENCING FACTORS TO MENU PLANNING IN BIG SIZED FOOD&BEVERAGE FIRMS (SAMPLE OF TURKEY)

OpenAIRE

SARIOĞLAN, Mehmet

2016-01-01

AbstractIndividuals who spend their time in destinations far away from their home because of the facts such as business, education, health, vacation and meeting has brought about eating habit in locations (outdoors). Since that increasing rate of eating habits outdoors of the individuals day by day, bigsized Food&Beverage firms has been showed up. Big sized Food&Beverage firms become influential as a supportive role on producing factors of the public, private or corporate ente...

Implantable fiber-optic interface for parallel multisite long-term optical dynamic brain interrogation in freely moving mice

Science.gov (United States)

Doronina-Amitonova, L. V.; Fedotov, I. V.; Ivashkina, O. I.; Zots, M. A.; Fedotov, A. B.; Anokhin, K. V.; Zheltikov, A. M.

2013-01-01

Seeing the big picture of functional responses within large neural networks in a freely functioning brain is crucial for understanding the cellular mechanisms behind the higher nervous activity, including the most complex brain functions, such as cognition and memory. As a breakthrough toward meeting this challenge, implantable fiber-optic interfaces integrating advanced optogenetic technologies and cutting-edge fiber-optic solutions have been demonstrated, enabling a long-term optogenetic manipulation of neural circuits in freely moving mice. Here, we show that a specifically designed implantable fiber-optic interface provides a powerful tool for parallel long-term optical interrogation of distinctly separate, functionally different sites in the brain of freely moving mice. This interface allows the same groups of neurons lying deeply in the brain of a freely behaving mouse to be reproducibly accessed and optically interrogated over many weeks, providing a long-term dynamic detection of genome activity in response to a broad variety of pharmacological and physiological stimuli. PMID:24253232

Move! Eat better: Do you walk more than 10,000 steps a day?

CERN Multimedia

2013-01-01

All of you who borrowed a pedometer from the Infirmary will confirm that it was a useful experience and that, in order to reach your recommended daily tally of 10,000 paces, you need to add about 30 minutes of exercise to your daily routine, which equates to about 4,000 extra paces. Why 10,000 steps?   The slogan “10,000 steps a day” comes from Japan. But the number wasn’t just pulled from a hat -  it’s based on a study by Dr Yoshiro Hatano. Walking is sacrosanct in Japanese society, where they use pedometers to determine the number of steps they walk every day. Dr Yoshiro Hatano has demonstrated that people who get enough walking in every day have improved health. In other words, you have to burn at least 200 extra calories per day, through exercise, in order to start reaping the benefits in terms of improved health. A 30-minute walk roughly corresponds to the 4,000 extra steps which are recommended but rarely taken. Is 10,000 steps an achie...

D-branes in a big bang/big crunch universe: Misner space

International Nuclear Information System (INIS)

Hikida, Yasuaki; Nayak, Rashmi R.; Panigrahi, Kamal L.

2005-01-01

We study D-branes in a two-dimensional lorentzian orbifold R 1,1 /Γ with a discrete boost Γ. This space is known as Misner or Milne space, and includes big crunch/big bang singularity. In this space, there are D0-branes in spiral orbits and D1-branes with or without flux on them. In particular, we observe imaginary parts of partition functions, and interpret them as the rates of open string pair creation for D0-branes and emission of winding closed strings for D1-branes. These phenomena occur due to the time-dependence of the background. Open string 2→2 scattering amplitude on a D1-brane is also computed and found to be less singular than closed string case

D-branes in a big bang/big crunch universe: Misner space

Energy Technology Data Exchange (ETDEWEB)

Hikida, Yasuaki [Theory Group, High Energy Accelerator Research Organization (KEK), Tukuba, Ibaraki 305-0801 (Japan); Nayak, Rashmi R. [Dipartimento di Fisica and INFN, Sezione di Roma 2, ' Tor Vergata' , Rome 00133 (Italy); Panigrahi, Kamal L. [Dipartimento di Fisica and INFN, Sezione di Roma 2, ' Tor Vergata' , Rome 00133 (Italy)

2005-09-01

We study D-branes in a two-dimensional lorentzian orbifold R{sup 1,1}/{gamma} with a discrete boost {gamma}. This space is known as Misner or Milne space, and includes big crunch/big bang singularity. In this space, there are D0-branes in spiral orbits and D1-branes with or without flux on them. In particular, we observe imaginary parts of partition functions, and interpret them as the rates of open string pair creation for D0-branes and emission of winding closed strings for D1-branes. These phenomena occur due to the time-dependence of the background. Open string 2{yields}2 scattering amplitude on a D1-brane is also computed and found to be less singular than closed string case.

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.)

Inflated granularity: Spatial “Big Data” and geodemographics

Directory of Open Access Journals (Sweden)

Craig M Dalton

2015-08-01

Full Text Available Data analytics, particularly the current rhetoric around “Big Data”, tend to be presented as new and innovative, emerging ahistorically to revolutionize modern life. In this article, we situate one branch of Big Data analytics, spatial Big Data, through a historical predecessor, geodemographic analysis, to help develop a critical approach to current data analytics. Spatial Big Data promises an epistemic break in marketing, a leap from targeting geodemographic areas to targeting individuals. Yet it inherits characteristics and problems from geodemographics, including a justification through the market, and a process of commodification through the black-boxing of technology. As researchers develop sustained critiques of data analytics and its effects on everyday life, we must so with a grounding in the cultural and historical contexts from which data technologies emerged. This article and others (Barnes and Wilson, 2014 develop a historically situated, critical approach to spatial Big Data. This history illustrates connections to the critical issues of surveillance, redlining, and the production of consumer subjects and geographies. The shared histories and structural logics of spatial Big Data and geodemographics create the space for a continued critique of data analyses’ role in society.

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

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 ...

Emerging technology and architecture for big-data analytics

CERN Document Server

Chang, Chip; Yu, Hao

2017-01-01

This book describes the current state of the art in big-data analytics, from a technology and hardware architecture perspective. The presentation is designed to be accessible to a broad audience, with general knowledge of hardware design and some interest in big-data analytics. Coverage includes emerging technology and devices for data-analytics, circuit design for data-analytics, and architecture and algorithms to support data-analytics. Readers will benefit from the realistic context used by the authors, which demonstrates what works, what doesn’t work, and what are the fundamental problems, solutions, upcoming challenges and opportunities. Provides a single-source reference to hardware architectures for big-data analytics; Covers various levels of big-data analytics hardware design abstraction and flow, from device, to circuits and systems; Demonstrates how non-volatile memory (NVM) based hardware platforms can be a viable solution to existing challenges in hardware architecture for big-data analytics.

Toward a manifesto for the 'public understanding of big data'.

Science.gov (United States)

Michael, Mike; Lupton, Deborah

2016-01-01

In this article, we sketch a 'manifesto' for the 'public understanding of big data'. On the one hand, this entails such public understanding of science and public engagement with science and technology-tinged questions as follows: How, when and where are people exposed to, or do they engage with, big data? Who are regarded as big data's trustworthy sources, or credible commentators and critics? What are the mechanisms by which big data systems are opened to public scrutiny? On the other hand, big data generate many challenges for public understanding of science and public engagement with science and technology: How do we address publics that are simultaneously the informant, the informed and the information of big data? What counts as understanding of, or engagement with, big data, when big data themselves are multiplying, fluid and recursive? As part of our manifesto, we propose a range of empirical, conceptual and methodological exhortations. We also provide Appendix 1 that outlines three novel methods for addressing some of the issues raised in the article. © The Author(s) 2015.

What do Big Data do in Global Governance?

DEFF Research Database (Denmark)

Krause Hansen, Hans; Porter, Tony

2017-01-01

Two paradoxes associated with big data are relevant to global governance. First, while promising to increase the capacities of humans in governance, big data also involve an increasingly independent role for algorithms, technical artifacts, the Internet of things, and other objects, which can...... reduce the control of human actors. Second, big data involve new boundary transgressions as data are brought together from multiple sources while also creating new boundary conflicts as powerful actors seek to gain advantage by controlling big data and excluding competitors. These changes are not just...... about new data sources for global decision-makers, but instead signal more profound changes in the character of global governance....

Moving Forward, Looking Back--Historical Perspective, "Big History" and the Return of the "Longue Durée": Time to Develop Our Scale Hopping Muscles

Science.gov (United States)

Hawkey, Kate

2015-01-01

"Big history" is a term receiving a great deal of attention at present, particularly in North America where considerable sums of money have been invested in designing curricula and assessment tools to help teachers teach history at far larger scales of time than normal. Hawkey considers the pros and cons of incorporating components of…

Moving related to separation : who moves and to what distance

NARCIS (Netherlands)

Mulder, Clara H.; Malmberg, Gunnar

We address the issue of moving from the joint home on the occasion of separation. Our research question is: To what extent can the occurrence of moves related to separation, and the distance moved, be explained by ties to the location, resources, and other factors influencing the likelihood of

Big Data in Caenorhabditis elegans: quo vadis?

Science.gov (United States)

Hutter, Harald; Moerman, Donald

2015-11-05

A clear definition of what constitutes "Big Data" is difficult to identify, but we find it most useful to define Big Data as a data collection that is complete. By this criterion, researchers on Caenorhabditis elegans have a long history of collecting Big Data, since the organism was selected with the idea of obtaining a complete biological description and understanding of development. The complete wiring diagram of the nervous system, the complete cell lineage, and the complete genome sequence provide a framework to phrase and test hypotheses. Given this history, it might be surprising that the number of "complete" data sets for this organism is actually rather small--not because of lack of effort, but because most types of biological experiments are not currently amenable to complete large-scale data collection. Many are also not inherently limited, so that it becomes difficult to even define completeness. At present, we only have partial data on mutated genes and their phenotypes, gene expression, and protein-protein interaction--important data for many biological questions. Big Data can point toward unexpected correlations, and these unexpected correlations can lead to novel investigations; however, Big Data cannot establish causation. As a result, there is much excitement about Big Data, but there is also a discussion on just what Big Data contributes to solving a biological problem. Because of its relative simplicity, C. elegans is an ideal test bed to explore this issue and at the same time determine what is necessary to build a multicellular organism from a single cell. © 2015 Hutter and Moerman. This article is distributed by The American Society for Cell Biology under license from the author(s). Two months after publication it is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

76 FR 7810 - Big Horn County Resource Advisory Committee

Science.gov (United States)

2011-02-11

..., Wyoming 82801. Comments may also be sent via e-mail to [email protected] , with the words Big... DEPARTMENT OF AGRICULTURE Forest Service Big Horn County Resource Advisory Committee AGENCY: Forest Service, USDA. ACTION: Notice of meeting. SUMMARY: The Big Horn County Resource Advisory Committee...

Hot big bang or slow freeze?

Energy Technology Data Exchange (ETDEWEB)

Wetterich, C.

2014-09-07

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.

Hot big bang or slow freeze?

International Nuclear Information System (INIS)

Wetterich, C.

2014-01-01

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

Hot big bang or slow freeze?

Directory of Open Access Journals (Sweden)

C. Wetterich

2014-09-01

Full Text Available 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.

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.

Pre-big bang cosmology and quantum fluctuations

International Nuclear Information System (INIS)

Ghosh, A.; Pollifrone, G.; Veneziano, G.

2000-01-01

The quantum fluctuations of a homogeneous, isotropic, open pre-big bang model are discussed. By solving exactly the equations for tensor and scalar perturbations we find that particle production is negligible during the perturbative Pre-Big Bang phase

Analysis of Big Data Maturity Stage in Hospitality Industry

OpenAIRE

Shabani, Neda; Munir, Arslan; Bose, Avishek

2017-01-01

Big data analytics has an extremely significant impact on many areas in all businesses and industries including hospitality. This study aims to guide information technology (IT) professionals in hospitality on their big data expedition. In particular, the purpose of this study is to identify the maturity stage of the big data in hospitality industry in an objective way so that hotels be able to understand their progress, and realize what it will take to get to the next stage of big data matur...

A Multidisciplinary Perspective of Big Data in Management Research

OpenAIRE

Sheng, Jie; Amankwah-Amoah, J.; Wang, X.

2017-01-01

In recent years, big data has emerged as one of the prominent buzzwords in business and management. In spite of the mounting body of research on big data across the social science disciplines, scholars have offered little synthesis on the current state of knowledge. To take stock of academic research that contributes to the big data revolution, this paper tracks scholarly work's perspectives on big data in the management domain over the past decade. We identify key themes emerging in manageme...

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.

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...

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.”

75 FR 71069 - Big Horn County Resource Advisory Committee

Science.gov (United States)

2010-11-22

....us , with the words Big Horn County RAC in the subject line. Facsimilies may be sent to 307-674-2668... DEPARTMENT OF AGRICULTURE Forest Service Big Horn County Resource Advisory Committee AGENCY: Forest Service, USDA. ACTION: Notice of meeting. SUMMARY: The Big Horn County Resource Advisory Committee...

76 FR 26240 - Big Horn County Resource Advisory Committee

Science.gov (United States)

2011-05-06

... words Big Horn County RAC in the subject line. Facsimilies may be sent to 307-674-2668. All comments... DEPARTMENT OF AGRICULTURE Forest Service Big Horn County Resource Advisory Committee AGENCY: Forest Service, USDA. ACTION: Notice of meeting. SUMMARY: The Big Horn County Resource Advisory Committee...

Experimental feeding of DDE and PCB to female big brown bats (Eptesicus fuscus)

Science.gov (United States)

Clark, D.R.; Prouty, R.M.

1977-01-01

Twenty-two female big brown bats (Eptesicus fuscus) were collected in a house attic in Montgomery County, Maryland. Seventeen were fed mealworms (Tenebrio molitor larvae) that contained 166 ppm DDE; the other five were fed uncontaminated mealworms. After 54 days of feeding, six dosed bats were frozen and the remaining 16 were starved to death. In a second experiment, 21 female big brown bats were collected in a house attic in Prince Georges County, Maryland. Sixteen were fed mealworms that contained 9.4 ppm Aroclor 1254 (PCB). After 37 days, two bats had died, four dosed bats were frozen, and the remaining 15 were starved to death. Starvation caused mobilization of stored residues. After the feeding periods, average weights of all four groups (DDE-dosed, DDE control, PCB-dosed, PCB control) had increased. However, weights of DDE-dosed bats had increased significantly more than those of their contols, whereas weights of PCB-dosed bats had increased significantly less than those of their controls. During starvation, PCB-dosed bats lost weight significantly more slowly than controls. Because PCB levels in dosed bats resembled levels found in some free-living big brown bats, PCBs may be slowing metabolic rates of some free-living bats. It is not known how various common organochlorine residues may affect metabolism in hibernating bats. DDE and PCB increased in brains of starving bats as carcass fat was metabolized. Because the tremors and/or convulsions characteristic of neurotoxicity were not observed, we think even the maximum brain levels attained (132 ppm DDE, 20 ppm PCB) were sublethal. However, extrapolation of our DDE data predicted lethal brain levels when fat reserves declined sufficiently. PCB-dosed bats were probably in no danger of neurotoxic poisoning. However, PCB can kill by a nonneurotoxic mode, and this could explain the deaths of two bats on PCB dosage.

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.

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.

Natural regeneration processes in big sagebrush (Artemisia tridentata)

Science.gov (United States)

Schlaepfer, Daniel R.; Lauenroth, William K.; Bradford, John B.

2014-01-01

Big sagebrush, Artemisia tridentata Nuttall (Asteraceae), is the dominant plant species of large portions of semiarid western North America. However, much of historical big sagebrush vegetation has been removed or modified. Thus, regeneration is recognized as an important component for land management. Limited knowledge about key regeneration processes, however, represents an obstacle to identifying successful management practices and to gaining greater insight into the consequences of increasing disturbance frequency and global change. Therefore, our objective is to synthesize knowledge about natural big sagebrush regeneration. We identified and characterized the controls of big sagebrush seed production, germination, and establishment. The largest knowledge gaps and associated research needs include quiescence and dormancy of embryos and seedlings; variation in seed production and germination percentages; wet-thermal time model of germination; responses to frost events (including freezing/thawing of soils), CO2 concentration, and nutrients in combination with water availability; suitability of microsite vs. site conditions; competitive ability as well as seedling growth responses; and differences among subspecies and ecoregions. Potential impacts of climate change on big sagebrush regeneration could include that temperature increases may not have a large direct influence on regeneration due to the broad temperature optimum for regeneration, whereas indirect effects could include selection for populations with less stringent seed dormancy. Drier conditions will have direct negative effects on germination and seedling survival and could also lead to lighter seeds, which lowers germination success further. The short seed dispersal distance of big sagebrush may limit its tracking of suitable climate; whereas, the low competitive ability of big sagebrush seedlings may limit successful competition with species that track climate. An improved understanding of the

Digital humanitarians how big data is changing the face of humanitarian response

CERN Document Server

Meier, Patrick

2015-01-01

The Rise of Digital HumanitariansMapping Haiti LiveSupporting Search And Rescue EffortsPreparing For The Long Haul Launching An SMS Life Line Sending In The Choppers Openstreetmap To The Rescue Post-Disaster Phase The Human Story Doing Battle With Big Data Rise Of Digital Humanitarians This Book And YouThe Rise of Big (Crisis) DataBig (Size) Data Finding Needles In Big (Size) Data Policy, Not Simply Technology Big (False) Data Unpacking Big (False) Data Calling 991 And 999 Big (

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.

ATLAS starts moving in

CERN Multimedia

Della Mussia, S

2004-01-01

The first large active detector component was lowered into the ATLAS cavern on 1st March. It consisted of the 8 modules forming the lower part of the central barrel of the tile hadronic calorimeter. The work of assembling the barrel, which comprises 64 modules, started the following day. Two road trailers each with 64 wheels, positioned side by side. This was the solution chosen to transport the lower part of the central barrel of ATLAS' tile hadronic calorimeter from Building 185 to the PX16 shaft at Point 1 (see Figure 1). The transportation, and then the installation of the component in the experimental cavern, which took place over three days were, to say the least, rather spectacular. On 25 February, the component, consisting of eight 6-metre modules, was loaded on to the trailers. The segment of the barrel was transported on a steel support so that it wouldn't move an inch during the journey. On 26 February, once all the necessary safety checks had been carried out, the convoy was able to leave Buildi...

[Embracing medical innovation in the era of big data].

Science.gov (United States)

You, Suning

2015-01-01

Along with the advent of big data era worldwide, medical field has to place itself in it inevitably. The current article thoroughly introduces the basic knowledge of big data, and points out the coexistence of its advantages and disadvantages. Although the innovations in medical field are struggling, the current medical pattern will be changed fundamentally by big data. The article also shows quick change of relevant analysis in big data era, depicts a good intention of digital medical, and proposes some wise advices to surgeons.

Big Data and Health Economics: Opportunities, Challenges and Risks

Directory of Open Access Journals (Sweden)

Diego Bodas-Sagi

2018-03-01

Full Text Available Big Data offers opportunities in many fields. Healthcare is not an exception. In this paper we summarize the possibilities of Big Data and Big Data technologies to offer useful information to policy makers. In a world with tight public budgets and ageing populations we feel necessary to save costs in any production process. The use of outcomes from Big Data could be in the future a way to improve decisions at a lower cost than today. In addition to list the advantages of properly using data and technologies from Big Data, we also show some challenges and risks that analysts could face. We also present an hypothetical example of the use of administrative records with health information both for diagnoses and patients.

Application and Exploration of Big Data Mining in Clinical Medicine.

Science.gov (United States)

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

2016-03-20

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

Big Data in Public Health: Terminology, Machine Learning, and Privacy.

Science.gov (United States)

Mooney, Stephen J; Pejaver, Vikas

2018-04-01

The digital world is generating data at a staggering and still increasing rate. While these "big data" have unlocked novel opportunities to understand public health, they hold still greater potential for research and practice. This review explores several key issues that have arisen around big data. First, we propose a taxonomy of sources of big data to clarify terminology and identify threads common across some subtypes of big data. Next, we consider common public health research and practice uses for big data, including surveillance, hypothesis-generating research, and causal inference, while exploring the role that machine learning may play in each use. We then consider the ethical implications of the big data revolution with particular emphasis on maintaining appropriate care for privacy in a world in which technology is rapidly changing social norms regarding the need for (and even the meaning of) privacy. Finally, we make suggestions regarding structuring teams and training to succeed in working with big data in research and practice.

Big Sites, Big Questions, Big Data, Big Problems: Scales of Investigation and Changing Perceptions of Archaeological Practice in the Southeastern United States

Directory of Open Access Journals (Sweden)

Cameron B Wesson

2014-08-01

Full Text Available Since at least the 1930s, archaeological investigations in the southeastern United States have placed a priority on expansive, near-complete, excavations of major sites throughout the region. Although there are considerable advantages to such large–scale excavations, projects conducted at this scale are also accompanied by a series of challenges regarding the comparability, integrity, and consistency of data recovery, analysis, and publication. We examine the history of large–scale excavations in the southeast in light of traditional views within the discipline that the region has contributed little to the ‘big questions’ of American archaeology. Recently published analyses of decades old data derived from Southeastern sites reveal both the positive and negative aspects of field research conducted at scales much larger than normally undertaken in archaeology. Furthermore, given the present trend toward the use of big data in the social sciences, we predict an increased use of large pre–existing datasets developed during the New Deal and other earlier periods of archaeological practice throughout the region.

Investigation of Great Basin big sagebrush and black greasewood as biogeochemical indicators of uranium mineralization. Final report. National Uranium Resource Evaluation

International Nuclear Information System (INIS)

Diebold, F.E.; McGrath, S.

1982-11-01

The effects of varying phosphate concentrations in natural aqueous systems upon the uptake of uranium by big sagebrush (Artemesia tridentata subsp. tridentata) and black greasewood (Sarcobatus vermiculatus (Hook) Torr.) were investigated. Two separate growth experiments with five drip-flow hyroponic units were used and plant seedlings were grown for 60 days in solutions of varying phosphate and uranium concentrations. Successful growth experiments were obtained only for big sagebrush; black greasewood did not sustain sufficient growth. The phosphate concentration of the water did affect the uptake of uranium by the big sagebrush, and this effect is most pronounced in the region of higher concentrations of uranium in the water. The ratio of the concentration of uranium in the plant to that in the water was observed to decrease with increasing uranium concentration in solution. This is indicative of an absorption barrier in the plants. The field data shows that big sagebrush responds to uranium concentrations in the soil water and not the groundwater. The manifestation of these results is that the use of big sagebrush as a biogeochemical indicator of uranium is not recommended. Since the concentration of phosphate must also be knwon in the water supplying the uranium to the plant, one should analyze this natural aqueous phase as a hydrochemical indicator rather than the big sagebrush

Big data analytics to improve cardiovascular care: promise and challenges.

Science.gov (United States)

Rumsfeld, John S; Joynt, Karen E; Maddox, Thomas M

2016-06-01

The potential for big data analytics to improve cardiovascular quality of care and patient outcomes is tremendous. However, the application of big data in health care is at a nascent stage, and the evidence to date demonstrating that big data analytics will improve care and outcomes is scant. This Review provides an overview of the data sources and methods that comprise big data analytics, and describes eight areas of application of big data analytics to improve cardiovascular care, including predictive modelling for risk and resource use, population management, drug and medical device safety surveillance, disease and treatment heterogeneity, precision medicine and clinical decision support, quality of care and performance measurement, and public health and research applications. We also delineate the important challenges for big data applications in cardiovascular care, including the need for evidence of effectiveness and safety, the methodological issues such as data quality and validation, and the critical importance of clinical integration and proof of clinical utility. If big data analytics are shown to improve quality of care and patient outcomes, and can be successfully implemented in cardiovascular practice, big data will fulfil its potential as an important component of a learning health-care system.

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.

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)

BIG´s italesættelse af BIG

DEFF Research Database (Denmark)

Brodersen, Anne Mygind; Sørensen, Britta Vilhelmine; Seiding, Mette

2008-01-01

Since Bjarke Ingels established the BIG (Bjarke Ingels Group) architectural firm in 2006, the company has succeeded in making itself heard and in attracting the attention of politicians and the media. BIG did so first and foremost by means of an overall approach to urban development that is both...... close to the political powers that be, and gain their support, but also to attract attention in the public debate. We present the issues this way: How does BIG speak out for itself? How can we explain the way the company makes itself heard, based on an analysis of the big.dk web site, the Clover Block...... by sidestepping the usual democratic process required for local plans. Politicians declared a positive interest in both the building project and a rapid decision process. However, local interest groups felt they were excluded from any influence regarding the proposal and launched a massive resistance campaign...

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

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

Correcting for day of the week and public holiday effects: improving a national daily syndromic surveillance service for detecting public health threats

Directory of Open Access Journals (Sweden)

Elizabeth Buckingham-Jeffery

2017-05-01

Full Text Available Abstract Background As service provision and patient behaviour varies by day, healthcare data used for public health surveillance can exhibit large day of the week effects. These regular effects are further complicated by the impact of public holidays. Real-time syndromic surveillance requires the daily analysis of a range of healthcare data sources, including family doctor consultations (called general practitioners, or GPs, in the UK. Failure to adjust for such reporting biases during analysis of syndromic GP surveillance data could lead to misinterpretations including false alarms or delays in the detection of outbreaks. The simplest smoothing method to remove a day of the week effect from daily time series data is a 7-day moving average. Public Health England developed the working day moving average in an attempt also to remove public holiday effects from daily GP data. However, neither of these methods adequately account for the combination of day of the week and public holiday effects. Methods The extended working day moving average was developed. This is a further data-driven method for adding a smooth trend curve to a time series graph of daily healthcare data, that aims to take both public holiday and day of the week effects into account. It is based on the assumption that the number of people seeking healthcare services is a combination of illness levels/severity and the ability or desire of patients to seek healthcare each day. The extended working day moving average was compared to the seven-day and working day moving averages through application to data from two syndromic indicators from the GP in-hours syndromic surveillance system managed by Public Health England. Results The extended working day moving average successfully smoothed the syndromic healthcare data by taking into account the combined day of the week and public holiday effects. In comparison, the seven-day and working day moving averages were unable to account for all

Research on the Impact of Big Data on Logistics

Directory of Open Access Journals (Sweden)

Wang Yaxing

2017-01-01

Full Text Available In the context of big data development, a large amount of data will appear at logistics enterprises, especially in the aspect of logistics, such as transportation, warehousing, distribution and so on. Based on the analysis of the characteristics of big data, this paper studies the impact of big data on the logistics and its action mechanism, and gives reasonable suggestions. Through building logistics data center by using the big data technology, some hidden value information behind the data will be digged out, in which the logistics enterprises can benefit from it.

Concurrence of big data analytics and healthcare: A systematic review.

Science.gov (United States)

Mehta, Nishita; Pandit, Anil

2018-06-01

The application of Big Data analytics in healthcare has immense potential for improving the quality of care, reducing waste and error, and reducing the cost of care. This systematic review of literature aims to determine the scope of Big Data analytics in healthcare including its applications and challenges in its adoption in healthcare. It also intends to identify the strategies to overcome the challenges. A systematic search of the articles was carried out on five major scientific databases: ScienceDirect, PubMed, Emerald, IEEE Xplore and Taylor & Francis. The articles on Big Data analytics in healthcare published in English language literature from January 2013 to January 2018 were considered. Descriptive articles and usability studies of Big Data analytics in healthcare and medicine were selected. Two reviewers independently extracted information on definitions of Big Data analytics; sources and applications of Big Data analytics in healthcare; challenges and strategies to overcome the challenges in healthcare. A total of 58 articles were selected as per the inclusion criteria and analyzed. The analyses of these articles found that: (1) researchers lack consensus about the operational definition of Big Data in healthcare; (2) Big Data in healthcare comes from the internal sources within the hospitals or clinics as well external sources including government, laboratories, pharma companies, data aggregators, medical journals etc.; (3) natural language processing (NLP) is most widely used Big Data analytical technique for healthcare and most of the processing tools used for analytics are based on Hadoop; (4) Big Data analytics finds its application for clinical decision support; optimization of clinical operations and reduction of cost of care (5) major challenge in adoption of Big Data analytics is non-availability of evidence of its practical benefits in healthcare. This review study unveils that there is a paucity of information on evidence of real-world use of

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.

Solution structure of leptospiral LigA4 Big domain

Energy Technology Data Exchange (ETDEWEB)

Mei, Song; Zhang, Jiahai [Hefei National Laboratory for Physical Sciences at Microscale, School of Life Sciences, University of Science and Technology of China, Hefei, Anhui 230026 (China); Zhang, Xuecheng [School of Life Sciences, Anhui University, Hefei, Anhui 230039 (China); Tu, Xiaoming, E-mail: xmtu@ustc.edu.cn [Hefei National Laboratory for Physical Sciences at Microscale, School of Life Sciences, University of Science and Technology of China, Hefei, Anhui 230026 (China)

2015-11-13

Pathogenic Leptospiraspecies express immunoglobulin-like proteins which serve as adhesins to bind to the extracellular matrices of host cells. Leptospiral immunoglobulin-like protein A (LigA), a surface exposed protein containing tandem repeats of bacterial immunoglobulin-like (Big) domains, has been proved to be involved in the interaction of pathogenic Leptospira with mammalian host. In this study, the solution structure of the fourth Big domain of LigA (LigA4 Big domain) from Leptospira interrogans was solved by nuclear magnetic resonance (NMR). The structure of LigA4 Big domain displays a similar bacterial immunoglobulin-like fold compared with other Big domains, implying some common structural aspects of Big domain family. On the other hand, it displays some structural characteristics significantly different from classic Ig-like domain. Furthermore, Stains-all assay and NMR chemical shift perturbation revealed the Ca{sup 2+} binding property of LigA4 Big domain. - Highlights: • Determining the solution structure of a bacterial immunoglobulin-like domain from a surface protein of Leptospira. • The solution structure shows some structural characteristics significantly different from the classic Ig-like domains. • A potential Ca{sup 2+}-binding site was identified by strains-all and NMR chemical shift perturbation.

Solution structure of leptospiral LigA4 Big domain

International Nuclear Information System (INIS)

Mei, Song; Zhang, Jiahai; Zhang, Xuecheng; Tu, Xiaoming

2015-01-01

Pathogenic Leptospiraspecies express immunoglobulin-like proteins which serve as adhesins to bind to the extracellular matrices of host cells. Leptospiral immunoglobulin-like protein A (LigA), a surface exposed protein containing tandem repeats of bacterial immunoglobulin-like (Big) domains, has been proved to be involved in the interaction of pathogenic Leptospira with mammalian host. In this study, the solution structure of the fourth Big domain of LigA (LigA4 Big domain) from Leptospira interrogans was solved by nuclear magnetic resonance (NMR). The structure of LigA4 Big domain displays a similar bacterial immunoglobulin-like fold compared with other Big domains, implying some common structural aspects of Big domain family. On the other hand, it displays some structural characteristics significantly different from classic Ig-like domain. Furthermore, Stains-all assay and NMR chemical shift perturbation revealed the Ca"2"+ binding property of LigA4 Big domain. - Highlights: • Determining the solution structure of a bacterial immunoglobulin-like domain from a surface protein of Leptospira. • The solution structure shows some structural characteristics significantly different from the classic Ig-like domains. • A potential Ca"2"+-binding site was identified by strains-all and NMR chemical shift perturbation.

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.

Move up,Move out

Institute of Scientific and Technical Information of China (English)

Guo Yan

2007-01-01

@@ China has already become the world's largest manufacturer of cement,copper and steel.Chinese producers have moved onto the world stage and dominated the global consumer market from textiles to electronics with amazing speed and efficiency.

New Evidence on the Development of the Word "Big."

Science.gov (United States)

Sena, Rhonda; Smith, Linda B.

1990-01-01

Results indicate that curvilinear trend in children's understanding of word "big" is not obtained in all stimulus contexts. This suggests that meaning and use of "big" is complex, and may not refer simply to larger objects in a set. Proposes that meaning of "big" constitutes a dynamic system driven by many perceptual,…

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

Investigating Seed Longevity of Big Sagebrush (Artemisia tridentata)

Science.gov (United States)

Wijayratne, Upekala C.; Pyke, David A.

2009-01-01

The Intermountain West is dominated by big sagebrush communities (Artemisia tridentata subspecies) that provide habitat and forage for wildlife, prevent erosion, and are economically important to recreation and livestock industries. The two most prominent subspecies of big sagebrush in this region are Wyoming big sagebrush (A. t. ssp. wyomingensis) and mountain big sagebrush (A. t. ssp. vaseyana). Increased understanding of seed bank dynamics will assist with sustainable management and persistence of sagebrush communities. For example, mountain big sagebrush may be subjected to shorter fire return intervals and prescribed fire is a tool used often to rejuvenate stands and reduce tree (Juniperus sp. or Pinus sp.) encroachment into these communities. A persistent seed bank for mountain big sagebrush would be advantageous under these circumstances. Laboratory germination trials indicate that seed dormancy in big sagebrush may be habitat-specific, with collections from colder sites being more dormant. Our objective was to investigate seed longevity of both subspecies by evaluating viability of seeds in the field with a seed retrieval experiment and sampling for seeds in situ. We chose six study sites for each subspecies. These sites were dispersed across eastern Oregon, southern Idaho, northwestern Utah, and eastern Nevada. Ninety-six polyester mesh bags, each containing 100 seeds of a subspecies, were placed at each site during November 2006. Seed bags were placed in three locations: (1) at the soil surface above litter, (2) on the soil surface beneath litter, and (3) 3 cm below the soil surface to determine whether dormancy is affected by continued darkness or environmental conditions. Subsets of seeds were examined in April and November in both 2007 and 2008 to determine seed viability dynamics. Seed bank samples were taken at each site, separated into litter and soil fractions, and assessed for number of germinable seeds in a greenhouse. Community composition data

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.

Integrative methods for analyzing big data in precision medicine.

Science.gov (United States)

Gligorijević, Vladimir; Malod-Dognin, Noël; Pržulj, Nataša

2016-03-01

We provide an overview of recent developments in big data analyses in the context of precision medicine and health informatics. With the advance in technologies capturing molecular and medical data, we entered the area of "Big Data" in biology and medicine. These data offer many opportunities to advance precision medicine. We outline key challenges in precision medicine and present recent advances in data integration-based methods to uncover personalized information from big data produced by various omics studies. We survey recent integrative methods for disease subtyping, biomarkers discovery, and drug repurposing, and list the tools that are available to domain scientists. Given the ever-growing nature of these big data, we highlight key issues that big data integration methods will face. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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)

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…

John C. Mather, the Big Bang, and the COBE

Science.gov (United States)

Bang theory and showing that the Big Bang was complete in the first instants, with only a tiny fraction dropdown arrow Site Map A-Z Index Menu Synopsis John C. Mather, the Big Bang, and the COBE Resources with collaborative work on understanding the Big Bang. Mather and Smoot analyzed data from NASA's Cosmic Background

Designing Cloud Infrastructure for Big Data in E-government

Directory of Open Access Journals (Sweden)

Jelena Šuh

2015-03-01

Full Text Available The development of new information services and technologies, especially in domains of mobile communications, Internet of things, and social media, has led to appearance of the large quantities of unstructured data. The pervasive computing also affects the e-government systems, where big data emerges and cannot be processed and analyzed in a traditional manner due to its complexity, heterogeneity and size. The subject of this paper is the design of the cloud infrastructure for big data storage and processing in e-government. The goal is to analyze the potential of cloud computing for big data infrastructure, and propose a model for effective storing, processing and analyzing big data in e-government. The paper provides an overview of current relevant concepts related to cloud infrastructure design that should provide support for big data. The second part of the paper gives a model of the cloud infrastructure based on the concepts of software defined networks and multi-tenancy. The final goal is to support projects in the field of big data in e-government

Translating Big Data into Smart Data for Veterinary Epidemiology.

Science.gov (United States)

VanderWaal, Kimberly; Morrison, Robert B; Neuhauser, Claudia; Vilalta, Carles; Perez, Andres M

2017-01-01

The increasing availability and complexity of data has led to new opportunities and challenges in veterinary epidemiology around how to translate abundant, diverse, and rapidly growing "big" data into meaningful insights for animal health. Big data analytics are used to understand health risks and minimize the impact of adverse animal health issues through identifying high-risk populations, combining data or processes acting at multiple scales through epidemiological modeling approaches, and harnessing high velocity data to monitor animal health trends and detect emerging health threats. The advent of big data requires the incorporation of new skills into veterinary epidemiology training, including, for example, machine learning and coding, to prepare a new generation of scientists and practitioners to engage with big data. Establishing pipelines to analyze big data in near real-time is the next step for progressing from simply having "big data" to create "smart data," with the objective of improving understanding of health risks, effectiveness of management and policy decisions, and ultimately preventing or at least minimizing the impact of adverse animal health issues.

Baryon symmetric big-bang cosmology

Energy Technology Data Exchange (ETDEWEB)

Stecker, F.W.

1978-04-01

The framework of baryon-symmetric big-bang cosmology offers the greatest potential for deducing the evolution of the universe as a consequence of physical laws and processes with the minimum number of arbitrary assumptions as to 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, and also provides the only acceptable explanation at present for the origin of the cosmic gamma ray background radiation.

Baryon symmetric big-bang cosmology

International Nuclear Information System (INIS)

Stecker, F.W.

1978-04-01

The framework of baryon-symmetric big-bang cosmology offers the greatest potential for deducing the evolution of the universe as a consequence of physical laws and processes with the minimum number of arbitrary assumptions as to 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, and also provides the only acceptable explanation at present for the origin of the cosmic gamma ray background radiation

Recharge Area, Base-Flow and Quick-Flow Discharge Rates and Ages, and General Water Quality of Big Spring in Carter County, Missouri, 2000-04

Science.gov (United States)

Imes, Jeffrey L.; Plummer, Niel; Kleeschulte, Michael J.; Schumacher, John G.

2007-01-01

during the period of record (water years 1922 through 2004) was 1,170 cubic feet per second on December 7, 1982. The daily mean water temperature of Big Spring was monitored during water years 2001 through 2004 and showed little variability, ranging from 13 to 15? C (degree Celsius). Water temperatures generally vary less than 1? C throughout the year. The warmest temperatures occur during October and November and decrease until April, indicating Big Spring water temperature does show a slight seasonal variation. The use of the traditional hydrograph separation program HYSEP to determine the base flow and quick flow or runoff components at Big Spring failed to yield base-flow and quick-flow discharge curves that matched observations of spring characteristics. Big Spring discharge data were used in combination with specific conductance data to develop an improved hydrograph separation method for the spring. The estimated annual mean quick flow ranged from 15 to 48 cubic feet per second for the HYSEP analysis and ranged from 26 to 154 cubic feet per second for the discharge and specific conductance method for water years 2001 to 2004. Using the discharge and specific conductance method, the estimated base-flow component rises abruptly as the spring hydrograph rises, attains a peak value on the same day as the discharge peak, and then declines abruptly from its peak value. Several days later, base flow begins to increase again at an approximately linear trend, coinciding with the time at which the percentage of quick flow has reached a maximum after each recharge-induced discharge peak. The interval between the discharge peak and the peak in percentage quick flow ranges from 8 to 11 days for seven hydrograph peaks, consistent with quick-flow traveltime estimates by dye-trace tests from the mature karst Hurricane Creek Basin in the central part of the recharge area. Concentrations of environmental tracers chlorofluorocarbons (CFCs: CFC-11, CFC-12, CFC-113)

SparkText: Biomedical Text Mining on Big Data Framework.

Science.gov (United States)

Ye, Zhan; Tafti, Ahmad P; He, Karen Y; Wang, Kai; He, Max M

Many new biomedical research articles are published every day, accumulating rich information, such as genetic variants, genes, diseases, and treatments. Rapid yet accurate text mining on large-scale scientific literature can discover novel knowledge to better understand human diseases and to improve the quality of disease diagnosis, prevention, and treatment. In this study, we designed and developed an efficient text mining framework called SparkText on a Big Data infrastructure, which is composed of Apache Spark data streaming and machine learning methods, combined with a Cassandra NoSQL database. To demonstrate its performance for classifying cancer types, we extracted information (e.g., breast, prostate, and lung cancers) from tens of thousands of articles downloaded from PubMed, and then employed Naïve Bayes, Support Vector Machine (SVM), and Logistic Regression to build prediction models to mine the articles. The accuracy of predicting a cancer type by SVM using the 29,437 full-text articles was 93.81%. While competing text-mining tools took more than 11 hours, SparkText mined the dataset in approximately 6 minutes. This study demonstrates the potential for mining large-scale scientific articles on a Big Data infrastructure, with real-time update from new articles published daily. SparkText can be extended to other areas of biomedical research.

Machine learning for Big Data analytics in plants.

Science.gov (United States)

Ma, Chuang; Zhang, Hao Helen; Wang, Xiangfeng

2014-12-01

Rapid advances in high-throughput genomic technology have enabled biology to enter the era of 'Big Data' (large datasets). The plant science community not only needs to build its own Big-Data-compatible parallel computing and data management infrastructures, but also to seek novel analytical paradigms to extract information from the overwhelming amounts of data. Machine learning offers promising computational and analytical solutions for the integrative analysis of large, heterogeneous and unstructured datasets on the Big-Data scale, and is gradually gaining popularity in biology. This review introduces the basic concepts and procedures of machine-learning applications and envisages how machine learning could interface with Big Data technology to facilitate basic research and biotechnology in the plant sciences. Copyright © 2014 Elsevier Ltd. All rights reserved.

33 CFR 117.677 - Big Sunflower River.

Science.gov (United States)

2010-07-01

... 33 Navigation and Navigable Waters 1 2010-07-01 2010-07-01 false Big Sunflower River. 117.677 Section 117.677 Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF HOMELAND SECURITY BRIDGES DRAWBRIDGE OPERATION REGULATIONS Specific Requirements Mississippi § 117.677 Big Sunflower River. The draw of...

Big Data, Big Consequences? Een verkenning naar privacy en big data gebruik binnen de opsporing, vervolging en rechtspraak

NARCIS (Netherlands)

Lodder, A.R.; van der Meulen, N.S.; Wisman, T.H.A.; Meij, Lisette; Zwinkels, C.M.M.

2014-01-01

In deze verkenning is ingegaan op de privacy aspecten van Big Data analysis binnen het domein Veiligheid en Justitie. Besproken zijn toepassingen binnen de rechtspraak zoals voorspellen van uitspraken en gebruik in rechtszaken. Met betrekking tot opsporing is onder andere ingegaan op predictive

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.

The structure of the big magnetic storms

International Nuclear Information System (INIS)

Mihajlivich, J. Spomenko; Chop, Rudi; Palangio, Paolo

2010-01-01

The records of geomagnetic activity during Solar Cycles 22 and 23 (which occurred from 1986 to 2006) indicate several extremely intensive A-class geomagnetic storms. These were storms classified in the category of the Big Magnetic Storms. In a year of maximum solar activity during Solar Cycle 23, or more precisely, during a phase designated as a post-maximum phase in solar activity (PPM - Phase Post maximum), near the autumn equinox, on 29, October 2003, an extremely strong and intensive magnetic storm was recorded. In the first half of November 2004 (7, November 2004) an intensive magnetic storm was recorded (the Class Big Magnetic Storm). The level of geomagnetic field variations which were recorded for the selected Big Magnetic Storms, was ΔD st=350 nT. For the Big Magnetic Storms the indicated three-hour interval indices geomagnetic activity was Kp = 9. This study presents the spectral composition of the Di - variations which were recorded during magnetic storms in October 2003 and November 2004. (Author)

Big data analytics a practical guide for managers

CERN Document Server

Pries, Kim H

2015-01-01

IntroductionSo What Is Big Data?Growing Interest in Decision MakingWhat This Book AddressesThe Conversation about Big DataTechnological Change as a Driver of Big DataThe Central Question: So What?Our Goals as AuthorsReferencesThe Mother of Invention's Triplets: Moore's Law, the Proliferation of Data, and Data Storage TechnologyMoore's LawParallel Computing, Between and Within MachinesQuantum ComputingRecap of Growth in Computing PowerStorage, Storage EverywhereGrist for the Mill: Data Used and

Database Resources of the BIG Data Center in 2018.

Science.gov (United States)

2018-01-04

The BIG Data Center at Beijing Institute of Genomics (BIG) of the Chinese Academy of Sciences provides freely open access to a suite of database resources in support of worldwide research activities in both academia and industry. With the vast amounts of omics data generated at ever-greater scales and rates, the BIG Data Center is continually expanding, updating and enriching its core database resources through big-data integration and value-added curation, including BioCode (a repository archiving bioinformatics tool codes), BioProject (a biological project library), BioSample (a biological sample library), Genome Sequence Archive (GSA, a data repository for archiving raw sequence reads), Genome Warehouse (GWH, a centralized resource housing genome-scale data), Genome Variation Map (GVM, a public repository of genome variations), Gene Expression Nebulas (GEN, a database of gene expression profiles based on RNA-Seq data), Methylation Bank (MethBank, an integrated databank of DNA methylomes), and Science Wikis (a series of biological knowledge wikis for community annotations). In addition, three featured web services are provided, viz., BIG Search (search as a service; a scalable inter-domain text search engine), BIG SSO (single sign-on as a service; a user access control system to gain access to multiple independent systems with a single ID and password) and Gsub (submission as a service; a unified submission service for all relevant resources). All of these resources are publicly accessible through the home page of the BIG Data Center at http://bigd.big.ac.cn. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Role of moving planes and moving spheres following Dupin cyclides

KAUST Repository

Jia, Xiaohong

2014-03-01

We provide explicit representations of three moving planes that form a μ-basis for a standard Dupin cyclide. We also show how to compute μ-bases for Dupin cyclides in general position and orientation from their implicit equations. In addition, we describe the role of moving planes and moving spheres in bridging between the implicit and rational parametric representations of these cyclides. © 2014 Elsevier B.V.

Role of moving planes and moving spheres following Dupin cyclides

KAUST Repository

Jia, Xiaohong

2014-01-01

We provide explicit representations of three moving planes that form a μ-basis for a standard Dupin cyclide. We also show how to compute μ-bases for Dupin cyclides in general position and orientation from their implicit equations. In addition, we describe the role of moving planes and moving spheres in bridging between the implicit and rational parametric representations of these cyclides. © 2014 Elsevier B.V.

The BIG Data Center: from deposition to integration to translation.

Science.gov (United States)

2017-01-04

Biological data are generated at unprecedentedly exponential rates, posing considerable challenges in big data deposition, integration and translation. The BIG Data Center, established at Beijing Institute of Genomics (BIG), Chinese Academy of Sciences, provides a suite of database resources, including (i) Genome Sequence Archive, a data repository specialized for archiving raw sequence reads, (ii) Gene Expression Nebulas, a data portal of gene expression profiles based entirely on RNA-Seq data, (iii) Genome Variation Map, a comprehensive collection of genome variations for featured species, (iv) Genome Warehouse, a centralized resource housing genome-scale data with particular focus on economically important animals and plants, (v) Methylation Bank, an integrated database of whole-genome single-base resolution methylomes and (vi) Science Wikis, a central access point for biological wikis developed for community annotations. The BIG Data Center is dedicated to constructing and maintaining biological databases through big data integration and value-added curation, conducting basic research to translate big data into big knowledge and providing freely open access to a variety of data resources in support of worldwide research activities in both academia and industry. All of these resources are publicly available and can be found at http://bigd.big.ac.cn. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Application and Exploration of Big Data Mining in Clinical Medicine

Science.gov (United States)

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

2016-01-01

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

Integrating R and Hadoop for Big Data Analysis

Directory of Open Access Journals (Sweden)

Bogdan Oancea

2014-06-01

Full Text Available Analyzing and working with big data could be very difficult using classical means like relational database management systems or desktop software packages for statistics and visualization. Instead, big data requires large clusters with hundreds or even thousands of computing nodes. Official statistics is increasingly considering big data for deriving new statistics because big data sources could produce more relevant and timely statistics than traditional sources. One of the software tools successfully and wide spread used for storage and processing of big data sets on clusters of commodity hardware is Hadoop. Hadoop framework contains libraries, a distributed file-system (HDFS, a resource-management platform and implements a version of the MapReduce programming model for large scale data processing. In this paper we investigate the possibilities of integrating Hadoop with R which is a popular software used for statistical computing and data visualization. We present three ways of integrating them: R with Streaming, Rhipe and RHadoop and we emphasize the advantages and disadvantages of each solution.

Effect of furosemide and dietary sodium on kidney and plasma big and small renin

International Nuclear Information System (INIS)

Iwao, H.; Michelakis, A.M.

1981-01-01

Renin was found in mouse plasma in high-molecular-weight forms (big big renin, big renin) and a low-molecular-weight form (small renin). They were measuerd by a radioimmunoassay procedure for the direct measurement of renin. In the kidney, 89% of total renin was small renin and the rest was big big and big renin. This distribution pattern of renins was not changed when the kideny tissue was homogenized in the presence of protease inhibitors. Low-sodium or high-sodium diets changed renal renin content, but not the distribution pattern of renins in the kidney. Acute stimulation of renin release by furosemide increased small renin but not big big and big renin in plasma. However, dietary sodium depletion for 2 weeks significantly increased big big, big, and small renin in plasma of mice with or without submaxillary glands. In contrast, high-sodium intake significantly decreased big big, big, and small renin in plasma of mice with or without submaxillary glands

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

Analyzing Big Data with the Hybrid Interval Regression Methods

Directory of Open Access Journals (Sweden)

Chia-Hui Huang

2014-01-01

Full Text Available Big data is a new trend at present, forcing the significant impacts on information technologies. In big data applications, one of the most concerned issues is dealing with large-scale data sets that often require computation resources provided by public cloud services. How to analyze big data efficiently becomes a big challenge. In this paper, we collaborate interval regression with the smooth support vector machine (SSVM to analyze big data. Recently, the smooth support vector machine (SSVM was proposed as an alternative of the standard SVM that has been proved more efficient than the traditional SVM in processing large-scale data. In addition the soft margin method is proposed to modify the excursion of separation margin and to be effective in the gray zone that the distribution of data becomes hard to be described and the separation margin between classes.

Automatic Moving Object Segmentation for Freely Moving Cameras

Directory of Open Access Journals (Sweden)

Yanli Wan

2014-01-01

Full Text Available This paper proposes a new moving object segmentation algorithm for freely moving cameras which is very common for the outdoor surveillance system, the car build-in surveillance system, and the robot navigation system. A two-layer based affine transformation model optimization method is proposed for camera compensation purpose, where the outer layer iteration is used to filter the non-background feature points, and the inner layer iteration is used to estimate a refined affine model based on the RANSAC method. Then the feature points are classified into foreground and background according to the detected motion information. A geodesic based graph cut algorithm is then employed to extract the moving foreground based on the classified features. Unlike the existing global optimization or the long term feature point tracking based method, our algorithm only performs on two successive frames to segment the moving foreground, which makes it suitable for the online video processing applications. The experiment results demonstrate the effectiveness of our algorithm in both of the high accuracy and the fast speed.

Moving event and moving participant in aspectual conceptions

Directory of Open Access Journals (Sweden)

Izutsu Katsunobu

2016-06-01

Full Text Available This study advances an analysis of the event conception of aspectual forms in four East Asian languages: Ainu, Japanese, Korean, and Ryukyuan. As earlier studies point out, event conceptions can be divided into two major types: the moving-event type and the moving-participant type, respectively. All aspectual forms in Ainu and Korean, and most forms in Japanese and Ryukyuan are based on that type of event conception. Moving-participant oriented Ainu and movingevent oriented Japanese occupy two extremes, between which Korean and Ryukyuan stand. Notwithstanding the geographical relationships among the four languages, Ryukyuan is closer to Ainu than to Korean, whereas Korean is closer to Ainu than to Japanese.