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International Conference of Pioneering Computer Scientists, Engineers and Educators

ICYCSEE 2016: Social Computing pp 453–457Cite as

MapReduce for Big Data Analysis: Benefits, Limitations and Extensions

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 623))

Abstract

Big data becomes a hot topic. MapReduce is a popular programming paradigm for big data analysis with many benefits. Even though it has widely applications in industry, MapReduce still has limitations in some applications. For these limitations, some extensions have been proposed. In these brief communications, we discuss the benefits and limitations of MapReduce programming paradigm and also its extensions to make MapReduce go beyond the limitations.

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Acknowledgments

This paper was partially supported by National Sci-Tech Support Plan 2015BAH10F01 and NSFC grant U1509216, 61472099, 61133002 and the Scientific Research Foundation for the Returned Overseas Chinese Scholars of Heilongjiang Provience LC2016026.

Author information

Authors and Affiliations

  1. Harbin Institute of Technology, P.O. Box 750, Harbin, 150001, China

    Yang Song, Hongzhi Wang, Jianzhong Li & Hong Gao

Authors
  1. Yang Song
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  2. Hongzhi Wang
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  3. Jianzhong Li
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  4. Hong Gao
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Corresponding author

Correspondence to Hongzhi Wang .

Editor information

Editors and Affiliations

  1. Harbin Institute of Technology , Harbin, China

    Wanxiang Che

  2. Harbin Engineering University , Harbin, China

    Qilong Han

  3. Harbin Institute of Technology , Harbin, China

    Hongzhi Wang

  4. Northeast Forestry University , Harbin, China

    Weipeng Jing

  5. National University of Defense Technology , Changsha, China

    Shaoliang Peng

  6. Harbin Engineering University , Harbin, China

    Junyu Lin

  7. Harbin Univ. of Science and Technology , Harbin, China

    Guanglu Sun

  8. Harbin Univ. of Science and Technology , Harbin, China

    Xianhua Song

  9. Harbin Engineering University , Harbin, China

    Hongtao Song

  10. Harbin Sea of Clouds & Computer Tech. , Harbin, China

    Zeguang Lu

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© 2016 Springer Science+Business Media Singapore

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Cite this paper

Song, Y., Wang, H., Li, J., Gao, H. (2016). MapReduce for Big Data Analysis: Benefits, Limitations and Extensions. In: Che, W., et al. Social Computing. ICYCSEE 2016. Communications in Computer and Information Science, vol 623. Springer, Singapore. https://doi.org/10.1007/978-981-10-2053-7_40

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  • DOI: https://doi.org/10.1007/978-981-10-2053-7_40

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-2052-0

  • Online ISBN: 978-981-10-2053-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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