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Big data

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A visualization created by IBM of Wikipedia edits. At multiple terabytes in size, the text and images of Wikipedia are a classic example of big data.

In information technology, big data[1][2] is a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. The challenges include capture, curation, storage,[3] search, sharing, analysis,[4] and visualization. The trend to larger data sets is due to the additional information derivable from analysis of a single large set of related data, as compared to separate smaller sets with the same total amount of data, allowing correlations to be found to "spot business trends, determine quality of research, prevent diseases, link legal citations, combat crime, and determine real-time roadway traffic conditions."[5][6][7]

As of 2012, limits on the size of data sets that are feasible to process in a reasonable amount of time were on the order of exabytes of data.[8][9] Scientists regularly encounter limitations due to large data sets in many areas, including meteorology, genomics,[10] connectomics, complex physics simulations,[11] and biological and environmental research.[12] The limitations also affect Internet search, finance and business informatics. Data sets grow in size in part because they are increasingly being gathered by ubiquitous information-sensing mobile devices, aerial sensory technologies (remote sensing), software logs, cameras, microphones, radio-frequency identification readers, and wireless sensor networks.[13][14] The world's technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s;[15] as of 2012, every day 2.5 quintillion (2.5×1018) bytes of data were created.[16]

Big data is difficult to work with using relational databases and desktop statistics and visualization packages, requiring instead "massively parallel software running on tens, hundreds, or even thousands of servers".[17] What is considered "big data" varies depending on the capabilities of the organization managing the set, and on the capabilities of the applications that are traditionally used to process and analyze the data set in its domain. "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."[18]

Definition

Big data usually includes data sets with sizes beyond the ability of commonly-used software tools to capture, curate, manage, and process the data within a tolerable elapsed time. Big data sizes are a constantly moving target, as of 2012 ranging from a few dozen terabytes to many petabytes of data in a single data set. With this difficulty, a new platform of "big data" tools has arisen to handle sensemaking over large quantities of data, as in the Apache Hadoop Big Data Platform.

MIKE2.0, an open approach to Information Management, defines big data in terms of useful permutations, complexity, and difficulty to delete individual records.

In a 2001 research report[19] 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 variety (range of data types and sources). Gartner, and now much of the industry, continue to use this "3Vs" model for describing big data.[20] In 2012, Gartner updated its definition as follows: "Big Data are 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."[21]

Examples

Examples include web logs, RFID, sensor networks, social networks, social data (due to the social data revolution), Internet text and documents, Internet search indexing, call detail records, astronomy, atmospheric science, genomics, biogeochemical, biological, and other complex and often interdisciplinary scientific research, military surveillance, medical records, photography archives, video archives, and large-scale e-commerce.

Science and research

  • When the Sloan Digital Sky Survey (SDSS) began collecting astronomical data in 2000, it amassed more in its first few weeks than all data collected in the history of astronomy. Continuing at a rate of about 200 GB per night, SDSS has amassed more than 140 terabytes of information. When the Large Synoptic Survey Telescope, successor to SDSS, comes online in 2016 it is anticipated to acquire that amount of data every five days.[5]
  • In total, the four main detectors at the Large Hadron Collider (LHC) produced 13 petabytes of data in 2010 (13,000 terabytes).[22]
  • Decoding the human genome originally took 10 years to process; now it can be achieved in one week.[5]
  • Computational social science — Tobias Preis et al. used Google Trends data to demonstrate that Internet users from countries with a higher per capita gross domestic product (GDP) are more likely to search for information about the future than information about the past. The findings suggest there may be a link between online behaviour and real-world economic indicators.[23][24][25] The authors of the study examined Google queries logs made by Internet users in 45 different countries in 2010 and calculated the ratio of the volume of searches for the coming year (‘2011’) to the volume of searches for the previous year (‘2009’), which they call the ‘future orientation index’.[26] They compared the future orientation index to the per capita GDP of each country and found a strong tendency for countries in which Google users enquire more about the future to exhibit a higher GDP. The results hint that there may potentially be a relationship between the economic success of a country and the information-seeking behavior of its citizens captured in big data.

Government

  • In 2012, the Obama administration announced the Big Data Research and Development Initiative, which explored how big data could be used to address important problems facing the government.[27] The initiative was composed of 84 different big data programs spread across six departments.[28]
  • The United States Federal Government owns six of the ten most powerful supercomputers in the world.[29]

Private sector

  • Walmart handles more than 1 million customer transactions every hour, which is imported into databases estimated to contain more than 2.5 petabytes of data – the equivalent of 167 times the information contained in all the books in the US Library of Congress.[5]
  • Facebook handles 40 billion photos from its user base.
  • FICO Falcon Credit Card Fraud Detection System protects 2.1 billion active accounts world-wide.[30]
  • The volume of business data worldwide, across all companies, doubles every 1.2 years, according to estimates.[31]

Market

"Big data" has increased the demand of information management specialists in that Software AG, Oracle Corporation, IBM, Microsoft, SAP, and HP have spent more than $15 billion on software firms only specializing in data management and analytics. This industry on its own is worth more than $100 billion and growing at almost 10 percent a year, about twice as fast as the software business as a whole.[5]

Developed economies make increasing use of data-intensive technologies. There are 4.6 billion mobile-phone subscriptions worldwide and there are between 1 billion and 2 billion people accessing the internet.[5] Between 1990 and 2005, more than 1 billion people worldwide entered the middle class which means more and more people who gain money will become more literate which in turn leads to information growth. The world's effective capacity to exchange information through telecommunication networks was 281 petabytes in 1986, 471 petabytes in 1993, 2.2 exabytes in 2000, 65 exabytes in 2007[15] and it is predicted that the amount of traffic flowing over the internet will reach 667 exabytes annually by 2013.[5]

Technologies

DARPA’s Topological Data Analysis program seeks the fundamental structure of massive data sets.

Big data requires exceptional technologies to efficiently process large quantities of data within tolerable elapsed times. A 2011 McKinsey report[32] suggests suitable technologies include A/B testing, association rule learning, classification, cluster analysis, crowdsourcing, data fusion and integration, ensemble learning, genetic algorithms, machine learning, natural language processing, neural networks, pattern recognition, anomaly detection, predictive modelling, regression, sentiment analysis, signal processing, supervised and unsupervised learning, simulation, time series analysis and visualisation. Additional technologies being applied to big data include massively parallel-processing (MPP) databases, search-based applications, data-mining grids, distributed file systems, distributed databases, cloud based infrastructure (applications, storage and computing resources) and the Internet.[citation needed]

Some but not all MPP relational databases have the ability to store and manage petabytes of data. Implicit is the ability to load, monitor, back up, and optimize the use of the large data tables in the RDBMS.[33]

The practitioners of big data analytics processes are generally hostile to slower shared storage[citation needed], preferring direct-attached storage (DAS) in its various forms from solid state disk (SSD) to high capacity SATA disk buried inside parallel processing nodes. The perception of shared storage architectures—SAN and NAS—is that they are relatively slow, complex, and expensive. These qualities are not consistent with big data analytics systems that thrive on system performance, commodity infrastructure, and low cost.

Real or near-real time information delivery is one of the defining characteristics of big data analytics. Latency is therefore avoided whenever and wherever possible. Data in memory is good—data on spinning disk at the other end of a FC SAN connection is not. The cost of a SAN at the scale needed for analytics applications is very much higher than other storage techniques.

There are advantages as well as disadvantages to shared storage in big data analytics, but big data analytics practitioners as of 2011 did not favour it.[34]

Research activities

In March 2012, The White House announced a national "Big Data Initiative" that consisted of six Federal departments and agencies committing more than $200 million to Big Data research projects.[35]

The initiative included a National Science Foundation "Expeditions in Computing" grant of $10 million over 5 years to the AMPLab[36] at the University of California, Berkeley.[37] The AMPLab also received funds from DARPA, and over a dozen industrial sponsors and uses Big Data to attack a wide range of problems from predicting traffic congestion[38] to fighting cancer.[39]

The White House Big Data Initiative also included a commitment by the Department of Energy to provide $25 million in funding over 5 years to establish the Scalable Data Management, Analysis and Visualization (SDAV) Institute,[40] led by the Energy Department’s Lawrence Berkeley National Laboratory. The SDAV Institute aims to bring together the expertise of six national laboratories and seven universities to develop new tools to help scientists manage and visualize data on the Department’s supercomputers.

The U.S. state of Massachusetts announced the Massachusetts Big Data Initiative in May 2012, which provides funding from the state government and private companies to a variety of research institutions.[41] The Massachusetts Institute of Technology hosts the Intel Science and Technology Center for Big Data in the MIT Computer Science and Artificial Intelligence Laboratory, combining government, corporate, and institutional funding and research efforts.[42]

Critique

danah Boyd has raised concerns about the use of big data in science neglecting principles such as choosing a representative sample by being too concerned about actually handling the huge amounts of data.[43] This approach may lead to results biased in one way or another. Integration across heterogeneous data resources — some that might be considered "big data" and others not — presents formidable logistical as well as analytical challenges, but many researchers argue that such integrations are likely to represent the most promising new frontiers in science.[44] Broader critiques have also been levelled at Chris Anderson's assertion that big data will spell the end of theory: focusing in particular on the notion that big data will always need to be contextualized in their social, economic and political contexts.[45] Even as companies invest eight- and nine-figure sums to derive insight from information streaming in from suppliers and customers, less than 40% of employees have sufficiently mature processes and skills to do so. To overcome this insight deficit, "big data", no matter how comprehensive or well analyzed, needs to be complemented by "big judgment", according to an article in the Harvard Business Review.[46]

Consumer privacy advocates are concerned about the threat to privacy represented by increasing storage and integration of personally identifiable information; expert panels have released various policy recommendations to conform practice to expectations of privacy.[47]

See also

Wikimedia Commons has media related to Database and Database models.

References

  1. ^ White, Tom (10 May 2012). Hadoop: The Definitive Guide. O'Reilly Media. p. 3. ISBN 978-1-4493-3877-0.
  2. ^ "MIKE2.0, Big Data Definition".
  3. ^ Kusnetzky, Dan. "What is "Big Data?"". ZDNet.
  4. ^ Vance, Ashley (22 April 2010). "Start-Up Goes After Big Data With Hadoop Helper". New York Times Blog.
  5. ^ a b c d e f g "Data, data everywhere". The Economist. 25 February 2010. Retrieved 9 December 2012.
  6. ^ "E-Discovery Special Report: The Rising Tide of Nonlinear Review". Hudson Global. Retrieved 1 July 2012. by Cat Casey and Alejandra Perez
  7. ^ "What Technology-Assisted Electronic Discovery Teaches Us About The Role Of Humans In Technology — Re-Humanizing Technology-Assisted Review". Forbes. Retrieved 1 July 2012.
  8. ^ Francis, Matthew (2 April 2012). "Future telescope array drives development of exabyte processing". Retrieved 24 October 2012.
  9. ^ Watters, Audrey (2010). "The Age of Exabytes: Tools and Approaches for Managing Big Data" (Website/Slideshare). Hewlett-Packard Development Company. Retrieved 24 October 2012.
  10. ^ "Community cleverness required". Nature. 455 (7209): 1. 4 September 2008. doi:10.1038/455001a.
  11. ^ "Sandia sees data management challenges spiral". HPC Projects. 4 August 2009.
  12. ^ Reichman, O.J.; Jones, M.B.; Schildhauer, M.P. (2011). "Challenges and Opportunities of Open Data in Ecology". Science. 331 (6018): 703–5. doi:10.1126/science.1197962.
  13. ^ Hellerstein, Joe (9 November 2008). "Parallel Programming in the Age of Big Data". Gigaom Blog.
  14. ^ Segaran, Toby; Hammerbacher, Jeff (2009). Beautiful Data: The Stories Behind Elegant Data Solutions. O'Reilly Media. p. 257. ISBN 978-0-596-15711-1.
  15. ^ a b Hilbert & López 2011
  16. ^ IBM What is big data? — Bringing big data to the enterprise
  17. ^ Jacobs, A. (6 July 2009). "The Pathologies of Big Data". ACMQueue.
  18. ^ Magoulas, Roger; Lorica, Ben (February 2009). "Introduction to Big Data". Release 2.0 (11). Sebastopol CA: O’Reilly Media.
  19. ^ Douglas, Laney. "3D Data Management: Controlling Data Volume, Velocity and Variety" (PDF). Gartner. Retrieved 6 February 2001.
  20. ^ Beyer, Mark. "Gartner Says Solving 'Big Data' Challenge Involves More Than Just Managing Volumes of Data". Gartner. Archived from the original on 10 July 2011. Retrieved 13 July 2011. {{cite web}}: Unknown parameter |deadurl= ignored (|url-status= suggested) (help)
  21. ^ Douglas, Laney. "The Importance of 'Big Data': A Definition". Gartner. Retrieved 21 June 2012.
  22. ^ Brumfiel, Geoff (19 January 2011). "High-energy physics: Down the petabyte highway". Nature. Vol. 469. pp. 282–83. doi:10.1038/469282a.
  23. ^ Preis, Tobias; Moat,, Helen Susannah; Stanley, H. Eugene; Bishop, Steven R. (2012). "Quantifying the Advantage of Looking Forward". Scientific Reports. 2: 350. doi:10.1038/srep00350. PMC 3320057. PMID 22482034.{{cite journal}}: CS1 maint: extra punctuation (link)
  24. ^ Marks, Paul (5 April 2012). "Online searches for future linked to economic success". New Scientist. Retrieved 9 April 2012.
  25. ^ Johnston, Casey (6 April 2012). "Google Trends reveals clues about the mentality of richer nations". Ars Technica. Retrieved 9 April 2012.
  26. ^ Tobias Preis (24 May 2012). "Supplementary Information: The Future Orientation Index is available for download" (PDF). Retrieved 24 May 2012.
  27. ^ Kalil, Tom. "Big Data is a Big Deal". White House. Retrieved 26 September 2012.
  28. ^ Executive Office of the President (2012). "Big Data Across the Federal Government" (PDF). White House. Retrieved 26 September 2012. {{cite web}}: Unknown parameter |month= ignored (help)
  29. ^ Hoover, J. Nicholas. "Government's 10 Most Powerful Supercomputers". Information Week. UBM. Retrieved 26 September 2012.
  30. ^ FICO® Falcon® Fraud Manager
  31. ^ eBay Study: How to Build Trust and Improve the Shopping Experience
  32. ^ Manyika, James; Chui, Michael; Bughin, Jaques; Brown, Brad; Dobbs, Richard; Roxburgh, Charles; Byers, Angela Hung (May 2011). Big Data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute.
  33. ^ Monash, Curt (30 April 2009). "eBay's two enormous data warehouses".
    Monash, Curt (6 October 2010). "eBay followup — Greenplum out, Teradata > 10 petabytes, Hadoop has some value, and more".
  34. ^ "How New Analytic Systems will Impact Storage". September 2011.
  35. ^ "Obama Administration Unveils "Big Data" Initiative:Announces $200 Million In New R&D Investments" (PDF). The White House.
  36. ^ AMPLab at the University of California, Berkeley
  37. ^ "NSF Leads Federal Efforts In Big Data". National Science Foundation (NSF). 29 March 2012.
  38. ^ Timothy Hunter; Teodor Moldovan; Matei Zaharia; Justin Ma; Michael Franklin; Pieter Abbeel; Alexandre Bayen (October 2011). Scaling the Mobile Millennium System in the Cloud.
  39. ^ David Patterson (5 December 2011). "Computer Scientists May Have What It Takes to Help Cure Cancer". The New York Times.
  40. ^ "Secretary Chu Announces New Institute to Help Scientists Improve Massive Data Set Research on DOE Supercomputers". "energy.gov".
  41. ^ "Governor Patrick announces new initiative to strengthen Massachusetts' position as a World leader in Big Data". Commonwealth of Massachusetts.
  42. ^ Big Data @ CSAIL
  43. ^ Danah Boyd (29 April 2010). "Privacy and Publicity in the Context of Big Data". WWW 2010 conference. Retrieved 18 April 2011. {{cite web}}: Unknown parameter |comment= ignored (help)
  44. ^ Jones, MB; Schildhauer, MP; Reichman, OJ; Bowers, S (2006). "The New Bioinformatics: Integrating Ecological Data from the Gene to the Biosphere" (PDF). Annual Review of Ecology, Evolution, and Systematics. 37 (1): 519–544. doi:10.1146/annurev.ecolsys.37.091305.110031.
  45. ^ Graham M. (2012). "Big data and the end of theory?". The Guardian.
  46. ^ "Good Data Won't Guarantee Good Decisions. Harvard Business Review". Shah, Shvetank; Horne, Andrew; Capellá, Jaime;. HBR.org. Retrieved 8 September 2012.{{cite web}}: CS1 maint: extra punctuation (link)
  47. ^ Ohm, Paul. "Don't Build a Database of Ruin". Harvard Business Review.

Further reading

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