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Hardware Supported Rule-Based Classification on Big Datasets

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Rough Sets (IJCRS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10313))

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Abstract

In this paper we propose a combination of capabilities of the Field Programmable Gate Arrays based device and PC computer for data processing resulting in classification using previously generated decision rules. Solution is focused on big datasets. Presented architecture has been tested in programmable unit on real datasets. Obtained results confirm the significant acceleration of the computation time using hardware supported operations in comparison to software implementation.

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Acknowledgements

The present study was supported by a grant S/WI/3/2013 from Bialystok University of Technology and founded from the resources for research by Ministry of Science and Higher Education.

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Authors and Affiliations

  1. Faculty of Computer Science, Bialystok University of Technology, Wiejska 45A, 15-351, Bialystok, Poland

    Maciej Kopczynski, Tomasz Grzes & Jaroslaw Stepaniuk

Authors
  1. Maciej Kopczynski
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  2. Tomasz Grzes
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  3. Jaroslaw Stepaniuk
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Corresponding author

Correspondence to Jaroslaw Stepaniuk .

Editor information

Editors and Affiliations

  1. Polish-Japanese Academy of Information Technology, Warsaw, Poland

    Lech Polkowski

  2. University of Regina, Regina, SK, Canada

    Yiyu Yao

  3. University of Warmia and Mazury, Olsztyn, Poland

    Piotr Artiemjew

  4. University of Milano-Bicocca, Milano, Italy

    Davide Ciucci

  5. Southwest Jiaotong University, Chengdu, China

    Dun Liu

  6. Warsaw University, Warszawa, Poland

    Dominik Ślęzak

  7. Silesian University, Sosnowiec, Poland

    Beata Zielosko

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Kopczynski, M., Grzes, T., Stepaniuk, J. (2017). Hardware Supported Rule-Based Classification on Big Datasets. In: Polkowski, L., et al. Rough Sets. IJCRS 2017. Lecture Notes in Computer Science(), vol 10313. Springer, Cham. https://doi.org/10.1007/978-3-319-60837-2_52

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  • DOI: https://doi.org/10.1007/978-3-319-60837-2_52

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60836-5

  • Online ISBN: 978-3-319-60837-2

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