Core for Large Datasets : Rough Sets on FPGA

• Autorzy: Kopczyński M. , Grześ T. , Stepaniuk J.

Identyfikatory

DOI

10.3233/FI-2016-1407

• Konferencja: International Workshop on CONCURRENCY, SPECIFICATION, and PROGRAMMING (CS&P 2015), (24; 28-30.09.2015, Rzeszów, Poland).
• Języki publikacji: EN

Abstrakty

EN

This paper presents FPGA and softcore CPU based device for large datasets core calculation using rough set methods. Presented architectures have been tested on two real datasets by downloading and running presented solutions inside FPGA. Tested datasets had 1 000 to 10 000 000 objects. The same operations were performed in software implementation. Obtained results show the big acceleration in computation time using hardware supporting core generation in comparison to pure software implementation.

Słowa kluczowe

EN

big data; rough sets; field programmable gate array (FPGA); set theory; distributed algorithms

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2016
• Tom: Vol. 147, nr 2/3
• Strony: 241--259
• Opis fizyczny: Bibliogr. 19 poz., rys., tab., wykr.

Twórcy

autor

Kopczyński M.

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

autor

Grześ T.

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

autor

Stepaniuk J.

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

Bibliografia

• [1] Borowik G, Jankowski J, Kowalski K. Fast algorithm for feature extraction. Proc. SPIE 9662, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments. 2015, 96623T. doi:10.1117/12.2205909.
• [2] Czołombitko M, Stepaniuk J. Generating core based on discernibility measure and MapReduce. Pattern recognition and machine intelligence: 6th International conference, PReMI 2015, vol. 9124 of Lecture Notes in Computer Science; 2015:367–376. doi:10.1007/978-3-319-19941-2_35.
• [3] Kanasugi A, Yokoyama A. A basic design for rough set processor. In The 15th Annual Conference of Japanese Society for Artificial Intelligence 2001. Available from: http://doi.org/10.11517/pjsai.JSAI01.0.65.0.
• [4] Kopczyński M, Stepaniuk J. Hardware Implementations of Rough Set Methods in Programmable Logic Devices. Rough Sets and Intelligent Systems - Professor Zdzisław Pawlak in Memoriam, Vol. 43 of Intelligent Systems Reference Library, Heidelberg, Springer. 2013:309–321. doi:10.1007/978-3-642-30341-8_16.
• [5] Kopczyński M, Grześ T, Stepaniuk J. FPGA in Rough-Granular Computing : Reduct Generation. WI 2014 : The 2014 IEEE/WCI/ACM International Joint Conferences on Web Intelligence Vol. 2, Warsaw, IEEE Computer Society. 2014:364-370. doi:10.1109/WI-IAT.2014.120.
• [6] Kopczyński M, Grześ T, Stepaniuk J. Generating core in rough set theory : Design and implementation on FPGA. Vol. 8537 of Lecture Notes in Computer Science, Berlin, Springer-Verlag. 2014:209-216. doi:10.1007/978-3-319-08729-0_20.
• [7] Kopczyński M, Grześ T, Stepaniuk J. Computation of Cores in Big Datasets: An FPGA Approach. Vol. 9436 of Lecture Notes in Computer Science, Berlin, Springer-Verlag. 2015:153-163. doi:10.1007/978-3-319-25754-9_14.
• [8] Lewis T, Perkowski M, Jozwiak L. Learning in Hardware: Architecture and Implementation of an FPGA-Based Rough Set Machine. Vol. 1 of Euromicro, 25th Euromicro Conf. (EUROMICRO ’99). 1999:1326. Available from: http://doi.ieeecomputersociety.org/10.1109/EURMIC.1999.794488.
• [9] Lichman M. UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science; 2013. Available from: http://archive.ics.uci.edu/ml.
• [10] Muraszkiewicz M, Rybiński H. Towards a Parallel Rough Sets Computer In: Rough Sets, Fuzzy Sets and Knowledge Discovery. Springer-Verlag. 1994:434–443. Available from: http://dl.acm.org/citation.cfm?id=646470.692043.
• [11] Nguyen HS. Approximate Boolean Reasoning: Foundations and Applications in Data Mining. Transactions on Rough Sets V, Vol. 4100 of Lecture Notes in Computer Science, Berlin, Springer-Verlag. 2006: 334–506. Available from: http://dl.acm.org/citation.cfm?id=2167843.2167862.
• [12] Pawlak Z. Elementary rough set granules: Toward a rough set processor. Rough-Neurocomputing: Techniques for Computing with Words, Cognitive Technologies, Springer-Verlag, Berlin, Germany. 2004:5–14.
• [13] Pawlak Z, Skowron A. Rudiments of rough sets. Information Sciences. 2007;177(1):3–27.
• [14] Stepaniuk J. Knowledge discovery by application of rough set models. Rough Set Methods and Applications. New Developments in Knowledge Discovery, Information Systems, Physica–Verlag, Heidelberg. 2000:137–233. doi:10.1007/978-3-7908-1840-6_5.
• [15] Stepaniuk J. Rough–Granular Computing in Knowledge Discovery and Data Mining. Springer 2008. ISBN: 978-3-540-70800-1, 978-3-642-08972-5.
• [16] Stepaniuk J, Kopczyński M, Grześ T. The First Step Toward Processor for Rough Set Methods. Fundamenta Informaticae. 2013;127(1-4):429-443. doi:10.3233/FI-2013-919.
• [17] Sun L, Xu J, Li Y. A Feature Selection Approach of Inconsistent Decision Systems in Rough Set. Journal of Computers, Academy Publisher. 2014;9(6):1333–1340. doi: 10.4304/jcp.9.6.1333-1340.
• [18] Tiwari KS, Kothari AG. Design and Implementation of Rough Set Algorithms on FPGA: A Survey. In (IJARAI) International Journal of Advanced Research in Artificial Intelligence. 2014;3(9):14–23. doi:10.14569/IJARAI.2014.030903.
• [19] Zhang J, Wong J, Pan Y, Li T. A parallel matrix-based method for computing approximations in incomplete information systems. In IEEE Transactions on Knowledge Data Engineering. 2015;27(2):326–339. doi:10.1109/TKDE.2014.2330821.

Uwagi

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).