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Introduction

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Big Data

Part of the book series: SpringerBriefs in Computer Science ((BRIEFSCOMPUTER))

Abstract

The term of big data was coined under the explosive increase of global data and was mainly used to describe these enormous datasets. In this chapter, we introduce the definition of big data, and review its evolution in the past 20 years. In particular, we introduce the defining features of big data, as well as its 4Vs characteristics, including Volume, Variety, Velocity, and Value. The challenges brought about by big data is also examined in this chapter.

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References

  1. John Gantz and David Reinsel. Extracting value from chaos. IDC iView, pages 1–12, 2011.

    Google Scholar 

  2. Kenneth Cukier. Data, data everywhere: A special report on managing information. Economist Newspaper, 2010.

    Google Scholar 

  3. Drowning in numbers - digital data will flood the planet- and help us understand it better. http://www.economist.com/blogs/dailychart/2011/11/bigdata-0, 2011.

  4. Steve Lohr. The age of big data. New York Times, 11, 2012.

    Google Scholar 

  5. Noguchi Yuki. Following digital breadcrumbs to big data gold. http://www.npr.org/2011/11/29/142521910/thedigitalbreadcrumbs-that-lead-to-big-data, 2011.

  6. Noguchi Yuki. The search for analysts to make sense of big data. http://www.npr.org/2011/11/30/142893065/the-searchforanalysts-to-make-sense-of-big-data, 2011.

  7. Big data. http://www.nature.com/news/specials/bigdata/index.html, 2008.

  8. Special online collection: Dealing with big data. http://www.sciencemag.org/site/special/data/, 2011.

  9. Fact sheet: Big data across the federal government. http://www.whitehouse.gov/sites/default/files/microsites/ostp/big_data_fact_sheet_3_29_2012.pdf, 2012.

  10. O. R. Team. Big Data Now: Current Perspectives from O’Reilly Radar. O’Reilly Media, 2011.

    Google Scholar 

  11. M Grobelnik. Big data tutorial. http://videolectures.net/eswc2012grobelnikbigdata/, 2012.

  12. James Manyika, McKinsey Global Institute, Michael Chui, Brad Brown, Jacques Bughin, Richard Dobbs, Charles Roxburgh, and Angela Hung Byers. Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute, 2011.

    Google Scholar 

  13. Viktor Mayer-Schönberger and Kenneth Cukier. Big Data: A Revolution that Will Transform how We Live, Work, and Think. Eamon Dolan/Houghton Mifflin Harcourt, 2013.

    Google Scholar 

  14. Douglas Laney. 3-d data management: Controlling data volume, velocity and variety. META Group Research Note, February, 6, 2001.

    Google Scholar 

  15. Paul Zikopoulos, Chris Eaton, et al. Understanding big data: Analytics for enterprise class hadoop and streaming data. McGraw-Hill Osborne Media, 2011.

    Google Scholar 

  16. Erik Meijer. The world according to linq. Communications of the ACM, 54(10):45–51, 2011.

    Article  Google Scholar 

  17. Mark Beyer. Gartner says solving ‘big data’ challenge involves more than just managing volumes of data. Gartner. http://www.gartner.com/it/page.jsp, 2011.

  18. Jeremy Ginsberg, Matthew H Mohebbi, Rajan S Patel, Lynnette Brammer, Mark S Smolinski, and Larry Brilliant. Detecting influenza epidemics using search engine query data. Nature, 457(7232):1012–1014, 2008.

    Google Scholar 

  19. David DeWitt and Jim Gray. Parallel database systems: the future of high performance database systems. Communications of the ACM, 35(6):85–98, 1992.

    Article  Google Scholar 

  20. T Walter. Teradata past, present, and future. UCI ISG Lecture Series on Scalable Data Management.

    Google Scholar 

  21. Sanjay Ghemawat, Howard Gobioff, and Shun-Tak Leung. The google file system. In ACM SIGOPS Operating Systems Review, volume 37, pages 29–43. ACM, 2003.

    Google Scholar 

  22. Jeffrey Dean and Sanjay Ghemawat. Mapreduce: simplified data processing on large clusters. Communications of the ACM, 51(1):107–113, 2008.

    Article  Google Scholar 

  23. Anthony JG Hey, Stewart Tansley, Kristin Michele Tolle, et al. The fourth paradigm: data-intensive scientific discovery. 2009.

    Google Scholar 

  24. John H Howard, Michael L Kazar, Sherri G Menees, David A Nichols, Mahadev Satyanarayanan, Robert N Sidebotham, and Michael J West. Scale and performance in a distributed file system. ACM Transactions on Computer Systems (TOCS), 6(1):51–81, 1988.

    Google Scholar 

  25. Rick Cattell. Scalable sql and nosql data stores. ACM SIGMOD Record, 39(4):12–27, 2011.

    Article  Google Scholar 

  26. Alexandros Labrinidis and HV Jagadish. Challenges and opportunities with big data. Proceedings of the VLDB Endowment, 5(12):2032–2033, 2012.

    Google Scholar 

  27. Surajit Chaudhuri, Umeshwar Dayal, and Vivek Narasayya. An overview of business intelligence technology. Communications of the ACM, 54(8):88–98, 2011.

    Article  Google Scholar 

  28. D Agrawal, P Bernstein, E Bertino, S Davidson, U Dayal, M Franklin, J Gehrke, L Haas, A Halevy, J Han, et al. Challenges and opportunities with big data. a community white paper developed by leading researchers across the united states, 2012.

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China

    Min Chen & Yin Zhang

  2. Auburn University, Auburn, AL, USA

    Shiwen Mao

  3. Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC, Canada

    Victor C. M. Leung

Authors
  1. Min Chen
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  2. Shiwen Mao
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  3. Yin Zhang
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  4. Victor C. M. Leung
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Chen, M., Mao, S., Zhang, Y., Leung, V.C.M. (2014). Introduction. In: Big Data. SpringerBriefs in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-319-06245-7_1

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  • DOI: https://doi.org/10.1007/978-3-319-06245-7_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-06244-0

  • Online ISBN: 978-3-319-06245-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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