Skip to main content
SpringerLink
Log in
Book cover

Big Data Analytics: Systems, Algorithms, Applications pp 1–23Cite as

Big Data Analytics

  • Chapter
  • First Online:

Abstract

The latest disruptive trends and developments in digital age comprise social networking, mobility, analytics and cloud, popularly known as SMAC. The year 2016 saw Big Data Technologies being leveraged to power business intelligence applications. What holds in store for 2020 and beyond?

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   54.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   69.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References and Bibliography

  1. C.B.B.D Manyika, Big Data: The Next Frontier for Innovation, Competition and Productivity (McKinsey Global Institute, 2011)

    Google Scholar 

  2. IBM, Big Data and Netezza Channel Development (2012)

    Google Scholar 

  3. http://hadoop.apache.org [online]

  4. D.R John Gantz, The Digital Universe in 2020: Big Data, Bigger Digital Shadows, and Biggest Growth in the Far in the East (IDC, 2013)

    Google Scholar 

  5. http://ercoppa.github.io/HadoopInternals [online]

  6. S. Acharya, S. Chellappan, Big Data and Analytics (2015)

    Google Scholar 

  7. A. Ghoting, SystemML: Declarative Machine Learning on Map Reduce (IBW Watson research center, 2011)

    Google Scholar 

  8. K.D. Strang, Z. Sun, Big data paradigm: what is the status of privacy and security? Ann. Data Sci. Heidelb. 4(1), 1–17 (2017)

    Google Scholar 

  9. L. Cui, F.R. Yu, Q. Yan, When big data meets software-defined networking: SDN for big data and big data for SDN. IEEE Netw. 30(1), 58 (New York, Jan–Feb 2016)

    Google Scholar 

  10. R.M. Alguliyev, R.T. Gasimova, R.N. Abbasli, The obstacles in big data process. Int. J. Modern Edu. Comput. Sci. 9(3), (Hong Kong, Mar 2017)

    Google Scholar 

  11. F. Pourkamali-Anaraki, S. Becker, Preconditioned Data Scarification for Big Data with Applications to PCA and K-Means. IEEE Trans. Inf. Theory 63(5), 2954–2974 (New York, 2017)

    Google Scholar 

  12. K. Vishwa, Trends 2016: Big Data, IOT take the plunge (Voice & Data, New Delhi, Apr 5, 2016

    Google Scholar 

  13. J. Kremer, K. Stensbo-Smidt, F. Gieseke, K.S. Pedersen, C. Igel, Big Universe, big data: machine learning and image analysis for Astronomy. IEEE Intell. Syst. 32(2),16–22 (Los Alamitos, 2017)

    Google Scholar 

  14. R. Varshney, Why Enterprises will En-route India for Big Data Analytics (Express Computer, Mumbai, Jul 15, 2016)

    Google Scholar 

  15. G. Guruswamy, How to Avoid the Common Big Data Follies in 2016 (Express Computer, Mumbai, Apr 22, 2016)

    Google Scholar 

  16. Big Data, Big Science: Students Share ‘Big Data’ Research at Poster Session US Fed News Service, Including US State News; Washington, D.C. (Washington, D.C, 01 May 2017)

    Google Scholar 

  17. Electronics and Telecommunications Research Institute; Researchers Submit Patent Application, Big Data Distribution Brokerage System Using Data Verification and Method Thereof, for Approval (USPTO 20170140351) Information Technology (Newsweekly, Atlanta, Jun 6, 2017), p. 910

    Google Scholar 

  18. P. Lade, R. Ghosh, S. Srinivasan, Manufacturing analytics and industrial internet of things. IEEE Intell. Syst. 32(3), 74–79 (Los Alamitos, 2017)

    Google Scholar 

  19. Research and Markets; Securing Big Data Infrastructure: An Evolving Market Ecosystem-Research and Markets Information Technology (Newsweekly, Atlanta, Feb 23, 2016), p. 453

    Google Scholar 

  20. N.A. Shozi, J. Mtsweni, Big data privacy and security: a systematic analysis of current and future challenges, in International Conference on Cyber Warfare and Security; Reading: 296-XI. (Academic Conferences International Limited, Reading, 2016)

    Google Scholar 

  21. Big Data in Leading Industry Verticals: Retail, Insurance, Healthcare, Government, and Manufacturing 2015–2020—Research and Markets (Business Wire, New York, 27 Jan 2016)

    Google Scholar 

  22. Securing Big Data Infrastructure: An Evolving Market Ecosystem (PR Newswire, New York, 08 Feb 2016)

    Google Scholar 

  23. Big data report 2016—Global Strategic Business Report 2014–2022: The Need to Turn Big Data’ Into Big Advantage Drives Focus on Big Data Technologies & Services NASDAQ OMX’s News Release Distribution Channel (New York, 19 Dec 2016)

    Google Scholar 

  24. K. Yang, H. Qi, H. Li, K. Zheng, S. Zhou, et al., An efficient and fine-grained big data access control scheme with privacy-preserving policy. IEEE Internet Th. J. 4(2), 563–571 (Piscataway, 2017)

    Google Scholar 

  25. C.P. Chullipparambil, Big Data Analytics Using Hadoop Tools (San Diego State University, ProQuest Dissertations Publishing, 2016). 10106013

    Google Scholar 

  26. M. Pascalev, Privacy exchanges: restoring consent in privacy self-management. Eth. Inf. Technol. 19(1), 39–48 (Dordrecht, 2017)

    Google Scholar 

  27. W. Feng, E.A. Mack, R. Maciewjewski, Analyzing entrepreneurial social networks with big data wang. Ann. Am. Assoc. Geogr. 107(1), 130–150 (Washington, 2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. National Informatics Centre, New Delhi, Delhi, India

    Dr. C.S.R. Prabhu

  2. Advanced Analytics Institute, University of Technology, Sydney, Ultimo, NSW, Australia

    Dr. Aneesh Sreevallabh Chivukula

  3. Saarland University, Saarbrücken, Saarland, Germany

    Dr. Aditya Mogadala

  4. Qure.ai, Goregaon East, Mumbai, Maharashtra, India

    Rohit Ghosh

  5. School of Computing Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India

    Dr. L.M. Jenila Livingston

Authors
  1. Dr. C.S.R. Prabhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dr. Aneesh Sreevallabh Chivukula
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dr. Aditya Mogadala
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rohit Ghosh
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Dr. L.M. Jenila Livingston
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to C.S.R. Prabhu .

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Prabhu, C., Chivukula, A., Mogadala, A., Ghosh, R., Livingston, L. (2019). Big Data Analytics. In: Big Data Analytics: Systems, Algorithms, Applications. Springer, Singapore. https://doi.org/10.1007/978-981-15-0094-7_1

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-0094-7_1

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-0093-0

  • Online ISBN: 978-981-15-0094-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation