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Big Data Analytics pp 451–457Cite as

Parallel Mining of Frequent Itemsets from Memory-Mapped Files

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 654))

Abstract

Due to digitization of data in different fields, data are increasing in leaps and bounds. Mining of these large amounts of data requires two major issues to deal with. The first is the potential to deal with huge data which can be dealt with parallel algorithms as serial algorithms may take very long time or sometimes may not process. The second is the I/O overhead which can be dealt with memory mapping of files. This chapter brings together both parallelization and memory mapping of files concepts in mining the frequent itemsets. Our experiments proved that there is almost 20% more speedup on parallelizing our frequent itemset mining algorithm with memory mapping when compared to conventional I/O without memory mapping.

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Author information

Authors and Affiliations

  1. Department of ECM, KL University, Guntur, India

    T. Anuradha

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  1. T. Anuradha
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Editors and Affiliations

  1. Jagan Institute of Management Studies, New Delhi, Delhi, India

    V. B. Aggarwal

  2. Department of Computer Science, University of Delhi, New Delhi, Delhi, India

    Vasudha Bhatnagar

  3. Microsoft Innovation Centre, Sri Aurobindo Institute of Technology, Indore, Madhya Pradesh, India

    Durgesh Kumar Mishra

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Anuradha, T. (2018). Parallel Mining of Frequent Itemsets from Memory-Mapped Files. In: Aggarwal, V., Bhatnagar, V., Mishra, D. (eds) Big Data Analytics. Advances in Intelligent Systems and Computing, vol 654. Springer, Singapore. https://doi.org/10.1007/978-981-10-6620-7_43

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  • DOI: https://doi.org/10.1007/978-981-10-6620-7_43

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6619-1

  • Online ISBN: 978-981-10-6620-7

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