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A Framework for Data Mining and Knowledge Discovery in Cloud Computing

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Data Science and Big Data Computing

Abstract

The massive amounts of data being generated in the current world of information technology have increased from terabytes to petabytes in volume. The fact that extracting knowledge from large-scale data is a challenging issue creates a great demand for cloud computing because of its potential benefits such as scalable storage and processing services. Considering this motivation, this chapter introduces a novel framework, data mining in cloud computing (DMCC), that allows users to apply classification, clustering, and association rule mining methods on huge amounts of data efficiently by combining data mining, cloud computing, and parallel computing technologies. The chapter discusses the main architectural components, interfaces, features, and advantages of the proposed DMCC framework. This study also compares the running times when data mining algorithms are executed in serial and parallel in a cloud environment through DMCC framework. Experimental results show that DMCC greatly decreases the execution times of data mining algorithms.

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, Dokuz Eylul University, Izmir, Turkey

    Derya Birant

  2. Department of Software Engineering, Celal Bayar University, Manisa, Turkey

    Pelin Yıldırım

Authors
  1. Derya Birant
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  2. Pelin Yıldırım
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Corresponding author

Correspondence to Derya Birant .

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Editors and Affiliations

  1. Department of Computing and Mathematics , University of Derby, Derby, United Kingdom

    Zaigham Mahmood

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© 2016 Springer International Publishing Switzerland

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Birant, D., Yıldırım, P. (2016). A Framework for Data Mining and Knowledge Discovery in Cloud Computing. In: Mahmood, Z. (eds) Data Science and Big Data Computing. Springer, Cham. https://doi.org/10.1007/978-3-319-31861-5_11

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  • DOI: https://doi.org/10.1007/978-3-319-31861-5_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-31859-2

  • Online ISBN: 978-3-319-31861-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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