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A Spectrum of Big Data Applications for Data Analytics

  • Chapter
Computational Intelligence for Big Data Analysis

Part of the book series: Adaptation, Learning, and Optimization ((ALO,volume 19))

Abstract

As technology is gaining its insights, vast amount of data is getting collected from various resources. Foremost complex nature of data is providing challenging task among the researchers to store, process and analyze big data. At present, big data analytics tends to be an emerging domain which potentially has limitless opportunities for possible future outcomes. However, big data mining provides application capabilities to extract hidden information from large volumes of data for knowledge discovery process. In fact big data mining is demonstration varied challenges and vast opportunity among researchers and scientist for another upcoming decade. This chapter provides broad view of big data in medical application domain. In addition, a framework which can handle big data by using several preprocessing and data mining technique to discover hidden knowledge from large scale databases is designed and implemented. The proposed chapter also discuss the challenges in big data to gain insight knowledge for future outcomes.

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Author information

Authors and Affiliations

  1. Amity University, Sector 125, Noida, India

    Ritu Chauhan

  2. Jamia Hamdard University, Delhi, India

    Harleen Kaur

Authors
  1. Ritu Chauhan
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  2. Harleen Kaur
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Correspondence to Ritu Chauhan .

Editor information

Editors and Affiliations

  1. School of Computing Sciences and Engineering, VIT University, Vellore, India

    D.P. Acharjya

  2. Department of Information and Communication Technology, Fakir Mohan University, Balasore, India

    Satchidananda Dehuri

  3. Corporate Technology Office, Tata Consultancy Services, Mumbai, Maharashtra, India

    Sugata Sanyal

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Chauhan, R., Kaur, H. (2015). A Spectrum of Big Data Applications for Data Analytics. In: Acharjya, D., Dehuri, S., Sanyal, S. (eds) Computational Intelligence for Big Data Analysis. Adaptation, Learning, and Optimization, vol 19. Springer, Cham. https://doi.org/10.1007/978-3-319-16598-1_7

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  • DOI: https://doi.org/10.1007/978-3-319-16598-1_7

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16597-4

  • Online ISBN: 978-3-319-16598-1

  • eBook Packages: EngineeringEngineering (R0)

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