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Big Data Analytics

Author Affiliations

  • 1M.C.A. Department, M.D. University, Rohtak, Haryana, India
  • 2M.C.A. Department, M.D. University, Rohtak, Haryana, India

Res. J. Computer & IT Sci., Volume 4, Issue (2), Pages 1-4, February,20 (2016)

Abstract

Big data analytics refers to the method of analyzing huge volumes of data, or big data. The big data is collected from a large assortment of sources, such as social networks, videos, digital images, and sensors. The major aim of Big Data Analytics is to discover new patterns and relationships which might be invisible, and it can provide new insights about the users who created it. There are a number of tools available for mining of Big Data and Analysis of Big Data, both professional and non-professional. In this paper, we have summarised different big data analytic methods and tools.

References

  1. Min Chen,, Shiwen Mao and Yunhao Liu (2014). BigData: A Survey,, © Springer Science+Business MediaNew York 2014, published online: 22 january.
  2. Laney D 3-d data management:, controlling dataVolume,, velocity and variety. META Group ResearchNote, 6 February (2001)
  3. Olaiya Folorunsho (2013)., Comparative Study ofDifferent Data Mining Techniques Performance inknowledge Discovery from Medical Database., International Journal of Advanced Research in ComputerScience and Software Engineering 3(3), March 2013ISSN: 2277 128X.4. Zikopoulos P and Eaton C et al (2011).
  4. Zikopoulos P and Eaton C et al (2011). Understanding big data: analyticsfor enterprise class hadoop and streaming data. McGraw-Hill Osborne Media(2011)
  5. Beyer M,, Gartner says solving big data challengeinvolves more than just managing volumes of data., Gartner. http://www.gartner.com/it/page.jsp.
  6. O. R. Team Big data now:, current perspectives fromOReilly Radar. OReilly Media Gantz J,, Reinsel D (2011) Extracting value from chaos. IDC iView, 1–12 (2011)
  7. Mayer-Sch¨onberger V and Cukier K (2013)., Big data:arevolution that will transform how we live, work, andthink., Eamon Dolan/Houghton Mifflin Harcourt.
  8. Duren Che, Mejdl Safran and Zhiyong Peng (2013).From Big Data to Big Data Mining: Challenges,, Issuesand Opportunities, © Springer-Verlag Berlin Heidelberg.
  9. Petra Kuhnert and Bill Venables, ” An Introduction to R:Software for StatisticalModelling & Computing”,, CSIROMathematical and information Sciences Cleveland,Australia (2011)
  10. Sebastian Land and Simon Fischer (2012)., RapidMiner 5RapidMiner in academic use 27th August.,
  11. Berthold MR, Cebron N, Dill F, Gabriel TR, K¨otter T,Meinl T, Ohl P, Sieb C, Thiel K and Wiswedel B (2008)., KNIME: the Konstanz information miner”., Springer.
  12. Michael R. Berthold etal (2010). Knime:, The Konstanz Information Miner Technical Report,, Altana Chair forBioinformatics and Information Mining.
  13. Raymond Gardiner Goss and Kousikan Veeramuthu(2010)., Heading Towards Big Data- Building A BetterData Warehouse For More Data,, More Speed, And More Users.
  14. Xindong Wu, Xingquan Zhu, Gong-Qing Wu, Wei Ding(2014)., Data Mining with Big Data,, IEEE TransactionsOn Knowledge And Data Engineering, 26(1).
  15. Bharti Thakur and Manish Mann(2014)., Data Mining forBig Data: A Review,, IJARCSSE, 4(5).
  16. Avita Katal et al (2013) Big Data: Issue, Challenge, Tools and Good Practices, IEEE.
  17. Seref Sagiroglu and Duygu Sinanc,, Big Data: A review,, IEEE January (2013)
  18. 18. Zaiying Liu, Ping Yang and Lixiao Zhang (2013)., ASketch of Big Data Technologies IEEE SeventhInternational conference on Internet Computing forEngineering and Science.,
  19. 19. Wei Fan, Albert Bifet (2012)., Mining Big Data: CurrentStatus, and Forecast to the Future,, SIGKDDExplorations, 14(2).
  20. Zikopoulos P, Eaton C et al. (2011). Understanding bigdata: analytics for enterprise class hadoop and streaming data, McGraw- Hill Osborne Media.
  21. Mayer-Sch¨, onberger V and Cukier K (2013). Big data: arevolution that will transform how we live, work, andthink, Eamon Dolan/Houghton Mifflin Harcourt.
  22. 22. Albert Bifet, “Mining Big Data in Real Time”, (2010)
  23. Meijer E (2011)., The world according to linq., Communications of the ACM 54(10), 45–51.
  24. Manyika J,, McKinsey Global Institute,, Chui M, BrownB, Bughin J, Dobbs R, Roxburgh C (2011). Byers AHBig data: the next frontier for innovation, competitionand productivity. McKinsey Global