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Discovering Concurrent Process Models in Data: A Rough Set Approach

  • Conference paper
Rough Sets, Fuzzy Sets, Data Mining and Granular Computing (RSFDGrC 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5908))

Abstract

The aim of the lecture is to provide a survey of state of the art related to a research direction concerning relationships between rough set theory and concurrency in the context of process mining in data. The main goal of this review is the general presentation of the research in this area. Discovering of concurrent systems models from experimental data tables is very interesting and useful not only with the respect to cognitive aspect but also to possible applications. In particular, in Artificial Intelligence domains such as e.g. speech recognition, blind source separation and Independent Component Analysis, and also in other domains (e.g. in biology, molecular biology, finance, meteorology, etc.).

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References

  1. Bridewell, W., Langley, P., Todorovski, L., Dzeroski, S.: Inductive process modeling. Machine Learning 71(1-32) (2008)

    Google Scholar 

  2. Brown, E.M.: Boolean Reasoning. Kluwer, Dordrecht (1990)

    MATH  Google Scholar 

  3. Cios, J.K., Pedrycz, W., Swiniarski, R.W., Kurgan, L.: Data Mining. A Knowledge Discovery Approach. Springer, Heidelberg (2007)

    MATH  Google Scholar 

  4. Delimata, P., Moshkov, M., Skowron, A., Suraj, Z.: Inhibitory Rules in Data Analysis. A Rough Set Approach. Springer, Heidelberg (2009)

    MATH  Google Scholar 

  5. Janowski, A., Peters, J., Skowron, A., Stepaniuk, J.: Optimization in Discovery of Compound Granules. Fundamenta Informaticae 85, 249–265 (2008)

    MathSciNet  Google Scholar 

  6. Jensen, K.: Coloured Petri Nets. Basic Concepts, Analysis Methods and Practical Use, vol. 1. Springer, Heidelberg (1992)

    MATH  Google Scholar 

  7. Kodratoff, Y., Michalski, R. (eds.): Machine Learning, vol. 3. Morgan Kaufmann Publishers, CA (1990)

    Google Scholar 

  8. Kurgan, L.A., Musilek, P.: A survey of Knowledge Discovery and Data Mining process models. The Knowledge Engineering Review 21(1), 1–24 (2006)

    Article  Google Scholar 

  9. de Medeiros, A.K.A., Weijters, A.J.M.M., van der Aalst, W.M.P.: Genetic Process Mining: An Experimental Evaluation. Data Mining and Knowledge Discovery 14, 245–304 (2007)

    Article  MathSciNet  Google Scholar 

  10. Pancerz, K.: An Application of rough sets to the identification of concurrent system models. Ph.D. Thesis, Institute of Computer Science PAS, Warsaw (2006) (in Polish)

    Google Scholar 

  11. Pancerz, K., Suraj, Z.: Discovering Concurrent Models from Data Tables with the ROSECON System. Fundamenta Informaticae 60(1-4), 251–268 (2004)

    MATH  MathSciNet  Google Scholar 

  12. Pancerz, K., Suraj, Z.: Rough Sets for Discovering Concurrent System Models from Data Tables. In: Hassanien, A.E., Suraj, Z., Slezak, D., Lingras, P. (eds.) Rough Computing. Theories, Technologies, and Applications, IGI Global, 2008, pp. 239–268 (2008)

    Google Scholar 

  13. Pawlak, Z.: Rough Sets - Theoretical Aspects of Reasoning About Data. Kluwer, Dordrecht (1991)

    MATH  Google Scholar 

  14. Pawlak, Z.: Concurrent Versus Sequential the Rough Sets Perspective. Bulletin of the EATCS 48, 178–190 (1992)

    MATH  Google Scholar 

  15. Pawlak, Z.: Flow graphs and decision algorithms. LNCS (LNAI), vol. 2639. Springer, Heidelberg (2003)

    Google Scholar 

  16. Peters, J.F., Skowron, A., Suraj, Z., Pedrycz, W., Ramanna, S.: Approximate Real-Time Decision Making: Concepts and Rough Fuzzy Petri Net Models. International Journal of Intelligent Systems 14(8), 805–839 (1999)

    Article  MATH  Google Scholar 

  17. Rzasa, W., Suraj, Z.: A New Method for Determining of Extensions and Restrictions of Information Systems. In: Alpigini, J.J., Peters, J.F., Skowron, A., Zhong, N. (eds.) RSCTC 2002. LNCS (LNAI), vol. 2475, pp. 197–204. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  18. Skowron, A.: Boolean reasoning for decision rules generation. In: Komorowski, J., Raś, Z.W. (eds.) ISMIS 1993. LNCS, vol. 689, pp. 295–305. Springer, Heidelberg (1993)

    Google Scholar 

  19. Skowron, A.: Discovery of Process Models from Data and Domain Knowledge: A Rough-Granular Approach. In: Ghosh, A., De, R.K., Pal, S.K. (eds.) PReMI 2007. LNCS, vol. 4815, pp. 192–197. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  20. Skowron, A., Suraj, Z.: Rough Sets and Concurrency. Bulletin of the Polish Academy of Sciences 41(3), 237–254 (1993)

    MATH  Google Scholar 

  21. Skowron, A., Suraj, Z.: Discovery of Concurrent Data Models from Experimental Tables: A Rough Set Approach. In: Fayyad, U.M., Uthurusamy, R. (eds.) Proceedings of Knowledge Discovery and Data Mining, First International Conference, KDD 1995, Montreal, Canada, August 1995, pp. 288–293. The AAAI Press, Menlo Park (1995)

    Google Scholar 

  22. Skowron, A., Suraj, Z.: A Parallel Algorithm for Real-Time Decision Making: A Rough Set Approach. Journal of Intelligent Information Systems 7, 5–28 (1996)

    Article  Google Scholar 

  23. Suraj, Z.: Discovery of Concurrent Data Models from Experimental Tables: A Rough Set Approach. Fundamenta Informaticae 28(3-4), 353–376 (1996)

    MATH  MathSciNet  Google Scholar 

  24. Suraj, Z.: An Application of Rough Set Methods to Cooperative Information Systems Reengineering, in: Rough Sets, Fuzzy Sets and Machine Discovery. In: Proceedings of 4th International Workshop, RSFD 1996, November 1996, pp. 364–371. Tokyo University, Tokyo (1996)

    Google Scholar 

  25. Suraj, Z.: Reconstruction of Cooperative Information Systems under Cost Constraints: A Rough Set Approach. Information Sciences: An International Journal 111, 273–291 (1998)

    MATH  Google Scholar 

  26. Suraj, Z.: The Synthesis Problem of Concurrent Systems Specified by Dynamic Information Systems. In: Polkowski, L., Skowron, A. (eds.) Rough Sets in Knowledge Discovery, vol. 2, pp. 418–448. Physica-Verlag (1998)

    Google Scholar 

  27. Suraj, Z.: Rough Set Methods for the Synthesis and Analysis of Concurrent Processes. In: Polkowski, L., Tsumoto, S., Lin, T.Y. (eds.) Rough Set Methods and Applications, pp. 379–488. Springer, Heidelberg (2000)

    Google Scholar 

  28. Suraj, Z., Owsiany, G., Pancerz, K.: On Consistent and Partially Consistent Extensions of Information Systems. In: Ślęzak, D., Wang, G., Szczuka, M.S., Düntsch, I., Yao, Y. (eds.) RSFDGrC 2005. LNCS (LNAI), vol. 3641, pp. 224–233. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  29. Suraj, Z., Pancerz, K.: A new method for computing partially consistent extensions of information systems: a rough set approach. In: Proceedings of Information Processing and Management of Uncertainty in Knowledge-based Systems 3, International Conference, IPMU 2006, Paris, France, Editions EDK, pp. 2618–2625 (2006)

    Google Scholar 

  30. Suraj, Z., Pancerz, K.: Reconstruction of concurrent system models described by decomposed data tables. Fundamenta Informaticae 71(1), 101–119 (2006)

    MATH  MathSciNet  Google Scholar 

  31. Suraj, Z., Swiniarski, R., Pancerz, K.: Rough Sets and Petri Nets Working Together. In: Nguyen, H.S., Szczuka, M. (eds.) Rough Set Techniques in Knowledge Discovery and Data Mining, International Workshop, RSKD 2005, Hanoi, Vietnam, pp. 27–37 (2005)

    Google Scholar 

  32. Swiniarski, R., Hunt, F., Chalvet, D., Pearson, D.: Feature Selection Using Rough Sets and Hidden Layer Expansion for Rupture Prediction in a Highly Automated Production System. Systems Science 23(1), 203–212 (1997)

    Google Scholar 

  33. Swiniarski, R.: Application of Petri Nets to Modeling and Describing of Microcomputer Sequential Control Algorithms. International Journal Advances in Modeling, and Simulation 14(3), 47–56 (1988)

    Google Scholar 

  34. Swiniarski, R., Skowron, A.: Rough Sets Methods in Feature Selection and Recognition. Pattern Recognition Letters 24(6), 833–849 (2003)

    Article  MATH  Google Scholar 

  35. Tsumoto, S., Slowinski, R., Komorowski, J., Grzymala-Busse, J.W.: RSCTC 2004. LNCS (LNAI), vol. 3066. Springer, Heidelberg (2004)

    MATH  Google Scholar 

  36. Unnikrishnan, K.P., Ramakrishnan, N., Sastry, P.S., Uthurusamy, R.: 4th KDD Workshop on Temporal Data Mining: Network Reconstruction from Dynamic Data. The Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data, Philadelphia, USA (2006), http://people.cs.vt.edu/~ramakris/kddtdm06/cfp.ktml

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Author information

Authors and Affiliations

  1. Chair of Computer Science, University of Rzeszów, Dekerta 2, 35-030, Rzeszów, Poland

    Zbigniew Suraj

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  1. Zbigniew Suraj
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Editor information

Editors and Affiliations

  1. Department of Mathematics and Computer Aided Sciences, Kyushu Institute of Technology, 804-8550, Tobata, Kitakyushu, Japan

    Hiroshi Sakai

  2. Department of Pure Mathematics, University of Calcutta, 35 Ballygunge Circular Road, 700019, Kolkata, India

    Mihir Kumar Chakraborty

  3. Information Technology Department, University of Cairo, 5 Ahmed Zewal St. Orman, Giza, Egypt

    Aboul Ella Hassanien

  4. University of Warsaw & Infobright Inc., Poland

    Dominik Ślęzak

  5. University of Electronic Science and Technology of China, Chengdu, China

    William Zhu

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© 2009 Springer-Verlag Berlin Heidelberg

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Suraj, Z. (2009). Discovering Concurrent Process Models in Data: A Rough Set Approach. In: Sakai, H., Chakraborty, M.K., Hassanien, A.E., Ślęzak, D., Zhu, W. (eds) Rough Sets, Fuzzy Sets, Data Mining and Granular Computing. RSFDGrC 2009. Lecture Notes in Computer Science(), vol 5908. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10646-0_2

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  • DOI: https://doi.org/10.1007/978-3-642-10646-0_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-10645-3

  • Online ISBN: 978-3-642-10646-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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