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Big data is a broad term for data sets so large or complex that traditional data processing applications are inadequate. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, and information privacy. The term often refers simply to the use of predictive analytics or other certain advanced methods to extract value from data, and seldom to a particular size of data set. Accuracy in big data may lead to more confident decision making. And better decisions can mean greater operational efficiency, cost reduction and reduced risk. Analysis of data sets can find new correlations, to "spot business trends, prevent diseases, combat crime and so on." Scientists, business executives, practitioners of media, and advertising and governments alike regularly meet difficulties with large data sets in areas including Internet search, finance and business informatics. Scientists encounter limitations in e-Science work, including meteorology, genomics, connectomics, complex physics simulations,〔(【引用サイトリンク】date=4 August 2009 )〕 and biological and environmental research. Data sets grow in size in part because they are increasingly being gathered by cheap and numerous information-sensing mobile devices, aerial (remote sensing), software logs, cameras, microphones, radio-frequency identification (RFID) readers, and wireless sensor networks.〔(【引用サイトリンク】url=http://bookreviews.infoversant.com/data-crush-christopher-surdak/ )〕 The world's technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s; , every day 2.5 exabytes (2.5×1018) of data were created;〔(【引用サイトリンク】title=IBM What is big data? — Bringing big data to the enterprise )〕 The challenge for large enterprises is determining who should own big data initiatives that straddle the entire organization.〔Oracle and FSN, ("Mastering Big Data: CFO Strategies to Transform Insight into Opportunity" ), December 2012〕 Work with big data is necessarily uncommon; most analysis is of "PC size" data, on a desktop PC or notebook〔(【引用サイトリンク】title=Computing Platforms for Analytics, Data Mining, Data Science )〕 that can handle the available data set. Relational database management systems and desktop statistics and visualization packages often have difficulty handling big data. The work instead requires "massively parallel software running on tens, hundreds, or even thousands of servers". What is considered "big data" varies depending on the capabilities of the users and their tools, and expanding capabilities make big data a moving target. Thus, what is considered "big" one year becomes ordinary later. "For some organizations, facing hundreds of gigabytes of data for the first time may trigger a need to reconsider data management options. For others, it may take tens or hundreds of terabytes before data size becomes a significant consideration." == Definition == Big data usually includes data sets with sizes beyond the ability of commonly used software tools to capture, curate, manage, and process data within a tolerable elapsed time. Big data "size" is a constantly moving target, ranging from a few dozen terabytes to many petabytes of data. Big data is a set of techniques and technologies that require new forms of integration to uncover large hidden values from large datasets that are diverse, complex, and of a massive scale. In a 2001 research report and related lectures, META Group (now Gartner) analyst Doug Laney defined data growth challenges and opportunities as being three-dimensional, i.e. increasing volume (amount of data), velocity (speed of data in and out), and (range of data types and sources). Gartner, and now much of the industry, continue to use this "3Vs" model for describing big data. In 2012, Gartner updated its definition as follows: "Big data is high volume, high velocity, and/or high variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization." Additionally, a new V "Veracity" is added by some organizations to describe it.〔(【引用サイトリンク】url=http://www.villanovau.com/university-online-programs/what-is-big-data/ )〕 Gartner’s definition of the 3Vs is still widely used, and in agreement with a consensual definition that states that "Big Data represents the Information assets characterized by such a High Volume, Velocity and Variety to require specific Technology and Analytical Methods for its transformation into Value". The 3Vs have been expanded to other complementary characteristics of big data:〔 * Volume: big data doesn't sample. It just observes and tracks what happens * Velocity: big data is often available in real-time * Variety: big data draws from text, images, audio, video; plus it completes missing pieces through data fusion * Machine Learning: big data often doesn't ask why and simply detects patterns〔Mayer-Schönberger, V., & Cukier, K. (2013). Big data: a revolution that will transform how we live, work and think. London: John Murray.〕 * Digital footprint: big data is often a cost-free byproduct of digital interaction〔Hilbert, M. (2015). Digital Technology and Social Change (Online Course at the University of California ) (freely available). https://www.youtube.com/watch?v=XRVIh1h47sA&index=51&list=PLtjBSCvWCU3rNm46D3R85efM0hrzjuAIg Retrieved from https://canvas.instructure.com/courses/949415〕 The growing maturity of the concept fosters a more sound difference between big data and Business Intelligence, regarding data and their use:〔http://www.bigdataparis.com/presentation/mercredi/PDelort.pdf?PHPSESSID=tv7k70pcr3egpi2r6fi3qbjtj6#page=4〕 * Business Intelligence uses descriptive statistics with data with high information density to measure things, detect trends etc.; * Big data uses inductive statistics and concepts from nonlinear system identification 〔Billings S.A. "Nonlinear System Identification: NARMAX Methods in the Time, Frequency, and Spatio-Temporal Domains". Wiley, 2013〕 to infer laws (regressions, nonlinear relationships, and causal effects) from large sets of data with low information density〔Delort P., Big data Paris 2013 http://www.andsi.fr/tag/dsi-big-data/〕 to reveal relationships, dependencies and perform predictions of outcomes and behaviors.〔〔Delort P., Big Data car Low-Density Data ? La faible densité en information comme facteur discriminant http://lecercle.lesechos.fr/entrepreneur/tendances-innovation/221169222/big-data-low-density-data-faible-densite-information-com〕 In a popular tutorial article published in IEEE Access Journal, the authors classified existing definitions of big data into three categories, namely, Attribute Definition, Comparative Definition and Architectural Definition. The authors also presented a big-data technology map that illustrates the key technology evolution for big data. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Big data」の詳細全文を読む スポンサード リンク
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