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An Overview of the MapReduce Model

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Theoretical Computer Science and Discrete Mathematics (ICTCSDM 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10398))

Abstract

Data is getting accumulated fast in various domains all over the world and the data size varies from terabytes to yottabytes. Such huge size data are known as Big Data. Extraction of meaningful information from raw data using special patterns are called Data Mining and sophisticated algorithms have been designed for this purpose. In this paper, the time complexity for MapReduce-based data mining algorithm is presented.

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Acknowledgment

The first author is thankful to the management of Kalasalingam University for providing fellowship.

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Authors and Affiliations

  1. Kalasalingam University, Anand Nagar, Krishnankoil, 626126, Tamil Nadu, India

    S. Rajeswari, K. Suthendran, K. Rajakumar & S. Arumugam

Authors
  1. S. Rajeswari
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  2. K. Suthendran
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  3. K. Rajakumar
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  4. S. Arumugam
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Corresponding author

Correspondence to K. Suthendran .

Editor information

Editors and Affiliations

  1. Kalasalingam University, Krishnankoil, India

    S. Arumugam

  2. Ball State University, Muncie, Indiana, USA

    Jay Bagga

  3. Indiana University – Purdue University, Fort Wayne, Indiana, USA

    Lowell W. Beineke

  4. Indian Institute of Technology, New Delhi, India

    B.S. Panda

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Rajeswari, S., Suthendran, K., Rajakumar, K., Arumugam, S. (2017). An Overview of the MapReduce Model. In: Arumugam, S., Bagga, J., Beineke, L., Panda, B. (eds) Theoretical Computer Science and Discrete Mathematics. ICTCSDM 2016. Lecture Notes in Computer Science(), vol 10398. Springer, Cham. https://doi.org/10.1007/978-3-319-64419-6_40

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  • DOI: https://doi.org/10.1007/978-3-319-64419-6_40

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-64418-9

  • Online ISBN: 978-3-319-64419-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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