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Computation of Cores in Big Datasets: An FPGA Approach

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9436))

Abstract

In this paper we propose the FPGA and softcore CPU supported device for performing core calculation for large datasets using rough set methods. Presented architecture has been tested on two real datasets by downloading and running presented solution inside FPGA. Sizes of the datasets were in range 1 000 to 10 000 000 objects. Results show the big acceleration in terms of the computation time using hardware supporting core generation unit.

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References

  1. Bezerra, E., Lettnin, D.V.: Synthesizable VHDL Design for FPGAs. Springer, New York (2014)

    Book  Google Scholar 

  2. Grześ, T., Kopczyński, M., Stepaniuk, J.: FPGA in rough set based core and reduct computation. In: Lingras, P., Wolski, M., Cornelis, C., Mitra, S., Wasilewski, P. (eds.) RSKT 2013. LNCS, vol. 8171, pp. 263–270. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  3. Kanasugi, A., Yokoyama, A.: A basic design for rough set processor. In: The 15th Annual Conference of Japanese Society for Artificial Intelligence (2001)

    Google Scholar 

  4. Kopczyński, M., Stepaniuk, J.: Hardware implementations of rough set methods in programmable logic devices. In: Skowron, A., Suraj, Z. (eds.) Rough Sets and Intelligent Systems - Professor Zdzisław Pawlak in Memoriam. ISRL, vol. 43, pp. 309–321. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  5. Kopczyński, M., Grześ, T., Stepaniuk, J.: FPGA in rough-granular computing : reduct generation. In: WI 2014 : The 2014 IEEE/WCI/ACM International Joint Conferences on Web Intelligence, vol. 2, pp. 364–370. IEEE Computer Society, Warsaw (2014)

    Google Scholar 

  6. Kopczynski, M., Grzes, T., Stepaniuk, J.: Generating core in rough set theory: design and implementation on FPGA. In: Kryszkiewicz, M., Cornelis, C., Ciucci, D., Medina-Moreno, J., Motoda, H., Raś, Z.W. (eds.) RSEISP 2014. LNCS, vol. 8537, pp. 209–216. Springer, Heidelberg (2014)

    Google Scholar 

  7. Lewis, T., Perkowski, M., Jozwiak, L.: Learning in hardware: architecture and implementation of an FPGA-based rough set machine. In: 25th Euromicro Conference (EUROMICRO 1999), vol. 1, p. 1326 (1999)

    Google Scholar 

  8. Lichman, M.: UCI Machine Learning Repository, Irvine, CA: University of California, School of Information and Computer Science (2013). http://archive.ics.uci.edu/ml

  9. Muraszkiewicz, M., Rybiński, H.: Towards a parallel rough sets computer. In: Rough Sets, Fuzzy Sets and Knowledge Discovery: Proceedings of the International Workshop on Rough Sets and Knowledge Discovery, RSKD pp. 434–443 (1994)

    Chapter  Google Scholar 

  10. Nguyen, H.S.: Approximate boolean reasoning: foundations and applications in data mining. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets V. LNCS, vol. 4100, pp. 334–506. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  11. Pawlak, Z.: Elementary rough set granules: Toward a rough set processor. In: Rough-Neurocomputing: Techniques for Computing with Words. Cognitive Technologies, pp. 5–14. Springer, Berlin (2004)

    Chapter  Google Scholar 

  12. Pawlak, Z., Skowron, A.: Rudiments of rough sets. Inf. Sci. 177(1), 3–27 (2007)

    Article  MathSciNet  Google Scholar 

  13. Stepaniuk, J.: Knowledge discovery by application of rough set models. In: Polkowski, L., Tsumoto, S., Lin, T.Y. (eds.) Rough Set Methods and Applications: New Developments in Knowledge Discovery in Information Systems, pp. 137–233. Physica-Verlag, Heidelberg (2000)

    Chapter  Google Scholar 

  14. Stepaniuk, J.: Rough-Granular Computing in Knowledge Discovery and Data Mining. Springer, New York (2008)

    MATH  Google Scholar 

  15. Stepaniuk, J., Kopczyński, M., Grześ, T.: The first step toward processor for rough set methods. Fundamenta Informaticae 127, 429–443 (2013)

    Google Scholar 

  16. Tiwari, K.S., Kothari, A.G.: Design and implementation of rough set algorithms on FPGA: a survey. (IJARAI) Int. J. Adv. Res. Artif. Intell. 3(9), 14–23 (2014)

    Google Scholar 

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Acknowledgements

The research is supported by the Polish National Science Centre under the grant 2012/07/B/ST6/01504 (Jaroslaw Stepaniuk, Maciej Kopczynski) and by the scientific grant S/WI/3/2013 (Tomasz Grzes).

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Correspondence to Maciej Kopczynski .

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Kopczynski, M., Grzes, T., Stepaniuk, J. (2015). Computation of Cores in Big Datasets: An FPGA Approach. In: Ciucci, D., Wang, G., Mitra, S., Wu, WZ. (eds) Rough Sets and Knowledge Technology. RSKT 2015. Lecture Notes in Computer Science(), vol 9436. Springer, Cham. https://doi.org/10.1007/978-3-319-25754-9_14

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  • DOI: https://doi.org/10.1007/978-3-319-25754-9_14

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