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Intelligent Data Analysis and Applications pp 109–119Cite as

A Survey on Big Data, Mining: (Tools, Techniques, Applications and Notable Uses)

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 370))

Abstract

Big Data is a massive set of data that is so complex to be managed by traditional applications. Nowadays, it includes huge, complex, and abundant structured, semi-structure, and unstructured data as well as hidden data that are generated and gathered from several fields and resources. The challenges for managing Big Data include extracting, analyzing, visualizing, sharing, storage, transferring and searching such data. Currently, the traditional data processing tools and its applications are not capable of managing such revolutionized data. Therefore, there is a critical need to develop effective and efficient Big Data Mining techniques. This, in turn, has opened opportunities for research frontiers by using the exploiting artificial intelligence techniques for Big Data management. This study investigates the most effective Big Data Mining techniques and their rationale applications in various social, medical and scientific fields.

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References

  1. Kudyba S (2014) Big data, mining, and analytics: components of strategic decision making. Boca Raton, CRC Press

    Google Scholar 

  2. Schroeder R, Cowls J (2014) Big data, ethics, and the social implications of knowledge production

    Google Scholar 

  3. Manyika J, Chui M, Brown B, Bughin J, Dobbs R, Roxburgh C, Byers A (2011) Big data: the next frontier for innovation, competition, and productivity [Kindle edition]. McKinsey Global Institute. Accessed 11 June 2012

    Google Scholar 

  4. Gartner IT Glossary. http://www.gartner.com/it-glossary/big-data/. Accessed 05 April 2015

  5. The Four V’s of Big Data—IBM. http://www.ibmbigdatahub.com/infographic/four-vs-big-data. Accessed 05 April 2015

  6. Elorie K (2015) The 5 V’s of big data. Avnet advantage: the blog, solution-focused insight for growth-minded VARs. http://blogging.avnet.com/ts/advantage/2014/07/the-5-vs-of-big-data/#comment-474. Accessed 05 April 2015

  7. Gupta R (2014) Journey from data mining to web mining to big data. arXiv preprint arXiv:1404.4140

    Google Scholar 

  8. Gubbi J, Buyya R, Marusic S, Palaniswami M (2013) Internet of things (IoT): a vision, architectural elements, and future directions. Future Gener Comput Syst 29(7):1645–1660

    Article  Google Scholar 

  9. Domingo MC (2012) An overview of the internet of things for people with disabilities. J Netw Comput Appl 35(2):584–596

    Article  Google Scholar 

  10. Whitmore A, Agarwal A, Da Xu L (2014) The internet of things—a survey of topics and trends. Inf Syst Front 1–14

    Google Scholar 

  11. Wu X, Zhu X, Wu GQ, Ding W (2014) Data mining with big data. IEEE Trans Knowl Data Eng 26(1):97–107

    Google Scholar 

  12. Barbierato E, Gribaudo M, Iacono M (2014) Performance evaluation of NoSQL big-data applications using multi-formalism models. Future Gener Comput Syst 37:345–353

    Article  Google Scholar 

  13. Lee KM, Park SJ, Lee JH (2014) Soft computing in big data processing

    Google Scholar 

  14. Koch C (2013) Compilation and synthesis in big data analytics. In: Big data. Springer, Berlin, pp 6–6

    Google Scholar 

  15. Srinivasa S, Bhatnagar V (eds) (2012) Big data analytics: first international conference, BDA 2012, New Delhi, India, 24–26 December 2012: Proceedings (vol 7678). Springer

    Google Scholar 

  16. Verzani J (2014) Using R for introductory statistics. CRC Press

    Google Scholar 

  17. Jain N, Srivastava V (2013) Data mining techniques: a survey paper. IJRET: Int J Res Eng Technol

    Google Scholar 

  18. Sayad S (2012) Data mining map, an introduction to data mining. http://www.saedsayad.com/. Accessed 05 April 2015

  19. Zaki MJ, Meira Jr W (2014) Data mining and analysis: fundamental concepts and algorithms. Cambridge University Press

    Google Scholar 

  20. Ghore S (2014) Data mining used of neural networks approach, Department, CSE, Govt. Engg. College Bilaspur, Chhattisgarh, India. ISSN: 2348 – 7968

    Google Scholar 

  21. Singh Y, Chauhan AS (2009) Neural networks in data mining. J Theor Appl Inf Technol 5(6):36–42

    Google Scholar 

  22. Lahoti AA, Ramteke PL (2014) Data mining technique its needs and using applications. IJCSMC 3(4):572–579

    Google Scholar 

  23. Infobright, Data analysis institute. https://www.infobright.com/index.php/case-study/rez-1-ad-hoc-reporting-reduced/#.VE5MOiLF98F. Accessed 05 April 2015

  24. Wang Y, Kung L, Ting C, Byrd TA (2015) Beyond a technical perspective: understanding big data capabilities in health care. In: Proceedings of 48th annual Hawaii international conference on system sciences (HICSS), Kauai, Hawaii

    Google Scholar 

  25. Akerkar R (2014) Big data computing, Chapman & Hall Book, CRC Press Western Norway Research Institute Sogndal

    Google Scholar 

  26. Kambatla K, Kollias G, Kumar V, Grama A (2014) Trends in big data analytics. J Parallel Distrib Comput 74(7):2561–2573

    Article  Google Scholar 

  27. Saraswathi K, Ganesh Babu V (2015) A survey on data mining trends, applications and techniques. History 30(135):383–389

    Google Scholar 

  28. Du R, Huang J, Huang Z, Wang H, Zhong N (2014) A system to generate mobile data based on real user behavior. In: Web information systems engineering–WISE 2013 workshops. Springer, Berlin, pp 48–61

    Google Scholar 

  29. Feinleib D (2014) Doing a big data project. In: Big data Bootcamp. Apress, New York, pp 103–123

    Google Scholar 

  30. Johnston WJ (2014) The future of business and industrial marketing and needed research. J Bus Mark Manag 7(1):296–300

    Google Scholar 

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Acknowledgments

This work was supported by the IT4Innovations Centre of Excellence project (CZ.1.05/1.1.00/02.0070), funded by the European Regional Development Fund and the national budget of the Czech Republic via the Research and Development for Innovations Operational Programme and by the SGS in VSB—Technical University of Ostrava, Czech Republic, under the grant No. SP2015/146.

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Authors and Affiliations

  1. Department of Computer Science, VŠB-Technical University of Ostrava, Ostrava, Czech Republic

    Nour E. Oweis, Waseem George & Václav Snášel

  2. Deptartment of Computer Science, FIT, Applied Science University, Amman, Jordan

    Suhail S. Owais

  3. Faculty of Information Technology, MEU-Middle East University, Amman, Jordan

    Mona G. Suliman

  4. IT4Innovations, VŠB-Technical University of Ostrava, Ostrava, Czech Republic

    Nour E. Oweis & Václav Snášel

Authors
  1. Nour E. Oweis
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  2. Suhail S. Owais
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  3. Waseem George
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  4. Mona G. Suliman
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  5. Václav Snášel
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Corresponding author

Correspondence to Nour E. Oweis .

Editor information

Editors and Affiliations

  1. Machine Intelligence Research Labs (MIR Labs), Auburn, Washington, USA

    Ajith Abraham

  2. Fujian University of Technology, Fujian, China

    Xin Hua Jiang

  3. Department of Computer Science, VSB-Technical University of Ostrava, Ostrava-Poruba, Czech Republic

    Václav Snášel

  4. Fujian University of Technology, Fujian, China

    Jeng-Shyang Pan

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© 2015 Springer International Publishing Switzerland

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Oweis, N.E., Owais, S.S., George, W., Suliman, M.G., Snášel, V. (2015). A Survey on Big Data, Mining: (Tools, Techniques, Applications and Notable Uses). In: Abraham, A., Jiang, X., Snášel, V., Pan, JS. (eds) Intelligent Data Analysis and Applications. Advances in Intelligent Systems and Computing, vol 370. Springer, Cham. https://doi.org/10.1007/978-3-319-21206-7_10

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  • DOI: https://doi.org/10.1007/978-3-319-21206-7_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-21205-0

  • Online ISBN: 978-3-319-21206-7

  • eBook Packages: EngineeringEngineering (R0)

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