Discovery of Process Models from Data and Domain Knowledge: A Rough-Granular Approach

  • Andrzej Skowron
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4815)


The rapid expansion of the Internet has resulted not only in the ever growing amount of data therein stored, but also in the burgeoning complexity of the concepts and phenomena pertaining to those data. This issue has been vividly compared [14] by the renowned statistician, prof. Friedman of Stanford University, to the advances in human mobility from the period of walking afoot to the era of jet travel. These essential changes in data have brought new challenges to the development of new data mining methods, especially that the treatment of these data increasingly involves complex processes that elude classic modeling paradigms. “Hot” datasets like biomedical, financial or netuser behavior data are just a few examples. Mining such temporal or stream data is on the agenda of many research centers and companies worldwide (see, e.g., [31, 1]). In the data mining community, there is a rapidly growing interest in developing methods for the discovery of structures of temporal processes from data. Works on discovering models for processes from data have recently been undertaken by many renowned centers worldwide (e.g., [34, 19, 36, 9], langley/,


Domain Knowledge Multiagent System Approximate Reasoning Vague Concept Handwritten Digit Recognition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Aggarwal, C. (ed.): Data Streams: Models and Algorithms. Springer, Berlin (2007)zbMATHGoogle Scholar
  2. 2.
    Bazan, J., Peters, J.F., Skowron, A.: Behavioral pattern identification through rough set modelling. In: Ślȩzak, D., et al. (eds.) pp. 688–697 [33] (2005)Google Scholar
  3. 3.
    Bazan, J., Skowron, A.: On-line elimination of non-relevant parts of complex objects in behavioral pattern identification. In: Pal, S.K., et al. (eds.) pp. 720–725 [24](2005)Google Scholar
  4. 4.
    Bazan, J., Skowron, A.: Classifiers based on approximate reasoning schemes. In: Dunin-Kȩplicz, B., et al. (eds.) pp. 191–202 [13] (2005)Google Scholar
  5. 5.
    Bazan, J., Skowron, A., Swiniarski, R.: Rough sets and vague concept approximation: From sample approximation to adaptive learning. In: Peters, J.F., Skowron, A., Grzymała-Busse, J.W., Kostek, B., Świniarski, R.W., Szczuka, M. (eds.) Transactions on Rough Sets I. LNCS, vol. 3100, pp. 39–63. Springer, Heidelberg (2004)Google Scholar
  6. 6.
    Bazan, J., Kruczek, P., Bazan-Socha, S., Skowron, A., Pietrzyk, J.J.: Risk pattern identification in the treatment of infants with respiratory failure through rough set modeling. In: Proceedings of IPMU 2006, Paris, France, Paris, July 2-7, 2006, pp. 2650–2657. Éditions E.D.K (2006)Google Scholar
  7. 7.
    Bazan, J., Kruczek, P., Bazan-Socha, S., Skowron, A., Pietrzyk, J.J.: Automatic planning of treatment of infants with respiratory failure through rough set modeling. In: Greco, S., Hata, Y., Hirano, S., Inuiguchi, M., Miyamoto, S., Nguyen, H.S., Słowiński, R. (eds.) RSCTC 2006. LNCS (LNAI), vol. 4259, pp. 418–427. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  8. 8.
    Bazan, J.: Rough sets and granular computing in behavioral pattern identification and planning. In: Pedrycz, W., et al. (eds.) [29] (2007) (in press)Google Scholar
  9. 9.
    Borrett, S.R., Bridewell, W., Langely, P., Arrigo, K.R.: A method for representing and developing process models. Ecological Complexity 4(1-2), 1–12 (2007)CrossRefGoogle Scholar
  10. 10.
    Breiman, L.: Statistical modeling: The two Cultures. Statistical Science 16(3), 199–231 (2001)zbMATHCrossRefMathSciNetGoogle Scholar
  11. 11.
    Doherty, P., Łukaszewicz, W., Skowron, A., Szałas, A.: Knowledge Representation Techniques: A Rough Set Approach. Studies in Fuzziness and Soft Computing 202. Springer, Heidelberg (2006)Google Scholar
  12. 12.
    Domingos, P.: Toward knowledge-rich data mining. Data Mining and Knowledge Discovery 15, 21–28 (2007)CrossRefGoogle Scholar
  13. 13.
    Dunin-Kȩplicz, B., Jankowski, A., Skowron, A., Szczuka, M.: Monitoring, Security, and Rescue Tasks in Multiagent Systems (MSRAS 2004). Series in Soft Computing. Springer, Heidelberg (2005)Google Scholar
  14. 14.
    Friedman, J.H.: Data mining and statistics. What’s the connection? Keynote Address. In: Proceedings of the 29th Symposium on the Interface: Computing Science and Statistics, Houston, Texas (May 1997)Google Scholar
  15. 15.
    Jankowski, A., Skowron, A.: A wistech paradigm for intelligent systems. In: Transactions on Rough Sets VI: Journal Subline. LNCS, vol. 4374, pp. 94–132. Springer, Heidelberg (2006)Google Scholar
  16. 16.
    Jankowski, A., Skowron, A.: Logic for artificial intelligence: The Rasiowa - Pawlak school perspective. In: Ehrenfeucht, A., Marek, V., Srebrny, M. (eds.) Andrzej Mostowski: Reflections on the Polish School of Logic, IOS Press, Amsterdam (2007)Google Scholar
  17. 17.
    Jankowski, A., Skowron, A.: Wisdom Granular Computing. In: Pedrycz, W., et al. (eds.) (in press 2007)Google Scholar
  18. 18.
    Kriegel, H.-P., Borgwardt, K.M., Kröger, P., Pryakhin, A., Schubert, M., Zimek, A.: Future trends in data mining. Data Mining and Knowledge Discovery 15(1), 87–97 (2007)CrossRefGoogle Scholar
  19. 19.
    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)CrossRefGoogle Scholar
  20. 20.
    Nguyen, H.S., Bazan, J., Skowron, A., Nguyen, S.H.: Layered learning for concept synthesis. In: Peters, J.F., Skowron, A., Grzymała-Busse, J.W., Kostek, B., Świniarski, R.W., Szczuka, M. (eds.) Transactions on Rough Sets I. LNCS, vol. 3100, pp. 187–208. Springer, Heidelberg (2004)Google Scholar
  21. 21.
    Nguyen, T.T.: Eliciting domain knowledge in handwritten digit recognition. In: Pal, S., et al. (eds.) pp. 762–767 [24] (2005)Google Scholar
  22. 22.
    Nguyen, T.T.: Outlier and exception analysis in rough sets and granular computing. In: Pedrycz, W., et al. (eds.) [29] (in press 2007)Google Scholar
  23. 23.
    Nguyen, T.T., Willis, C.P., Paddon, D.J., Nguyen, S.H., Nguyen, H.S.: Learning Sunspot Classification. Fundamenta Informaticae 72(1-3), 295–309 (2006)zbMATHMathSciNetGoogle Scholar
  24. 24.
    Pal, S.K., Bandyopadhyay, S., Biswas, S. (eds.): PReMI 2005. LNCS, vol. 3776, pp. 18–22. Springer, Heidelberg (2005)Google Scholar
  25. 25.
    Pawlak, Z.: Rough sets. International Journal of Computer and Information Sciences 11, 341–356 (1982)CrossRefMathSciNetGoogle Scholar
  26. 26.
    Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning about Data. System Theory, Knowledge Engineering and Problem Solving, vol. 9. Kluwer Academic Publishers, The Netherlands, Dordrecht (1991)Google Scholar
  27. 27.
    Pawlak, Z.: Concurrent versus sequential the rough sets perspective. Bulletin of the EATCS 48, 178–190 (1992)zbMATHGoogle Scholar
  28. 28.
    Pawlak, Z., Skowron, A.: Rudiments of rough sets. Information Sciences 177(1): 3–27; Rough sets: Some extensions. Information Sciences 177(1): 28–40; Rough sets and boolean reasoning. Information Sciences 177(1): 41–73 (2007)Google Scholar
  29. 29.
    Pedrycz, W., Skowron, A., Kreinovich, V. (eds.): Handbook of Granular Computing. John Wiley & Sons, New York (in press)Google Scholar
  30. 30.
    Poggio, T., Smale, S.: The mathematics of learning: Dealing with data. Notices of the AMS 50(5), 537–544 (2003)zbMATHMathSciNetGoogle Scholar
  31. 31.
    Roddick, J.F., Hornsby, K., Spiliopoulou, M.: An updated bibliography of temporal, spatial and spatio- temporal data mining research. In: Roddick, J.F., Hornsby, K. (eds.) TSDM 2000. LNCS (LNAI), vol. 2007, Springer, Heidelberg (2001)Google Scholar
  32. 32.
    Suraj, Z.: Rough set methods for the synthesis and analysis of concurrent processes. In: Polkowski, L., Lin, T.Y., Tsumoto, S. (eds.) Rough Set Methods and Applications: New Developments in Knowledge Discovery in Information Systems, Studies in Fuzziness and Soft Computing, vol. 56, pp. 379–488. Springer, Heidelberg (2000)Google Scholar
  33. 33.
    Ślȩzak, D., Yao, J., Peters, J.F., Ziarko, W., Hu, X.(eds.): RSFDGrC 2005. LNCS (LNAI), vol. 3642. Springer, Heidelberg (2005)Google Scholar
  34. 34.
    Unnikrishnan, K.P., Ramakrishnan, N., Sastry, P.S., Uthurusamy, R.: 4th KDD Workshop on Temporal Data Mining: Network Reconstruction from Dynamic Data Aug 20, 2006, The Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data (KDD 2006) August 20 - 23, 2006 Philadelphia, USA (2006),
  35. 35.
    Vapnik, V.: Statistical Learning Theory. John Wiley & Sons, New York (1998)zbMATHGoogle Scholar
  36. 36.
    Wu, F.-X.: Inference of gene regulatory networks and its validation. Current Bioinformatics 2(2), 139–144 (2007)Google Scholar
  37. 37.
    Zadeh, L.A.: A new direction in AI-toward a computational theory of perceptions. AI Magazine 22(1), 73–84 (2001)Google Scholar
  38. 38.
    Zadeh, L.A.: Generalized theory of uncertainty (GTU)-principal concepts and ideas. Computational Statistics and Data Analysis 51, 15–46 (2006)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Andrzej Skowron
    • 1
  1. 1.Institute of Mathematics, Warsaw University, Banacha 2, 02-097 WarsawPoland

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