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.).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bridewell, W., Langley, P., Todorovski, L., Dzeroski, S.: Inductive process modeling. Machine Learning 71(1-32) (2008)
Brown, E.M.: Boolean Reasoning. Kluwer, Dordrecht (1990)
Cios, J.K., Pedrycz, W., Swiniarski, R.W., Kurgan, L.: Data Mining. A Knowledge Discovery Approach. Springer, Heidelberg (2007)
Delimata, P., Moshkov, M., Skowron, A., Suraj, Z.: Inhibitory Rules in Data Analysis. A Rough Set Approach. Springer, Heidelberg (2009)
Janowski, A., Peters, J., Skowron, A., Stepaniuk, J.: Optimization in Discovery of Compound Granules. Fundamenta Informaticae 85, 249–265 (2008)
Jensen, K.: Coloured Petri Nets. Basic Concepts, Analysis Methods and Practical Use, vol. 1. Springer, Heidelberg (1992)
Kodratoff, Y., Michalski, R. (eds.): Machine Learning, vol. 3. Morgan Kaufmann Publishers, CA (1990)
Kurgan, L.A., Musilek, P.: A survey of Knowledge Discovery and Data Mining process models. The Knowledge Engineering Review 21(1), 1–24 (2006)
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)
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)
Pancerz, K., Suraj, Z.: Discovering Concurrent Models from Data Tables with the ROSECON System. Fundamenta Informaticae 60(1-4), 251–268 (2004)
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)
Pawlak, Z.: Rough Sets - Theoretical Aspects of Reasoning About Data. Kluwer, Dordrecht (1991)
Pawlak, Z.: Concurrent Versus Sequential the Rough Sets Perspective. Bulletin of the EATCS 48, 178–190 (1992)
Pawlak, Z.: Flow graphs and decision algorithms. LNCS (LNAI), vol. 2639. Springer, Heidelberg (2003)
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)
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)
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)
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)
Skowron, A., Suraj, Z.: Rough Sets and Concurrency. Bulletin of the Polish Academy of Sciences 41(3), 237–254 (1993)
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)
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)
Suraj, Z.: Discovery of Concurrent Data Models from Experimental Tables: A Rough Set Approach. Fundamenta Informaticae 28(3-4), 353–376 (1996)
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)
Suraj, Z.: Reconstruction of Cooperative Information Systems under Cost Constraints: A Rough Set Approach. Information Sciences: An International Journal 111, 273–291 (1998)
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)
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)
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)
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)
Suraj, Z., Pancerz, K.: Reconstruction of concurrent system models described by decomposed data tables. Fundamenta Informaticae 71(1), 101–119 (2006)
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)
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)
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)
Swiniarski, R., Skowron, A.: Rough Sets Methods in Feature Selection and Recognition. Pattern Recognition Letters 24(6), 833–849 (2003)
Tsumoto, S., Slowinski, R., Komorowski, J., Grzymala-Busse, J.W.: RSCTC 2004. LNCS (LNAI), vol. 3066. Springer, Heidelberg (2004)
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
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
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)