Skip to main content

Discovery of Processes and Their Interactions from Data and Domain Knowledge

  • Conference paper
Book cover Agent and Multi-Agent Systems: Technologies and Applications (KES-AMSTA 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6070))

  • 800 Accesses

Abstract

We discuss an approach to discovery of concurrent processes from data and domain knowledge. The approach is based on interactive rough-granular computing (IRGC) and is developed in the Wisdom Technology (WisTech) program. In IRGC, computations are performed in distributed environments using interaction of granules. Granules are of different complexity. They can represent sensor measurements, classifiers of complex vague concepts, models of processes, agents or their teams. Applications related to different domains are reported.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Aggarwal, C. (ed.): Data Streams: Models and Algorithms. Springer, Berlin (2007)

    MATH  Google Scholar 

  2. Bargiela, A., Pedrycz, W.: Granular Computing: An Introduction. Kluwer Academic Publishers, Dordrecht (2003)

    MATH  Google Scholar 

  3. Bazan, J.G.: Hierarchical classifiers for complex spatio-temporal concepts. In: Peters, J.F., Skowron, A., Rybiński, H. (eds.) Transactions on Rough Sets IX. LNCS, vol. 5390, pp. 474–750. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  4. Bazan, J., Peters, J.F., Skowron, A.: Behavioral pattern identification through rough set modelling. In: Ślęzak, D., Yao, J., Peters, J.F., Ziarko, W.P., Hu, X. (eds.) RSFDGrC 2005. LNCS (LNAI), vol. 3642, pp. 688–697. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  5. Bazan, J., Skowron, A.: On-line elimination of non-relevant parts of complex objects in behavioral pattern identification. In: Pal, S.K., Bandyopadhyay, S., Biswas, S. (eds.) PReMI 2005. LNCS, vol. 3776, pp. 720–725. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  6. Bazan, J., Peters, J.F., Skowron, A.: Behavioral pattern identification through rough set modelling. In: Ślęzak, D., Yao, J., Peters, J.F., Ziarko, W.P., Hu, X. (eds.) RSFDGrC 2005. LNCS (LNAI), vol. 3642, pp. 688–697. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  7. Bazan, J., Skowron, A., Swiniarski, R.: Rough sets and vague concept approximation: From sample approximation to adaptive learning. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets V: Journal Subline. LNCS, vol. 4100, pp. 39–63. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  8. 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, pp. 2650–2657. E.D.K. Éditions, Paris (2006)

    Google Scholar 

  9. 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)

    Chapter  Google Scholar 

  10. Bazan, J.: Rough sets and granular computing in behavioral pattern identification and planning. In: Pedrycz, W., et al. (eds.) [39], pp. 777–799 (2008)

    Google Scholar 

  11. 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)

    Article  Google Scholar 

  12. Delimata, P., Moshkov, M., Skowron, A., Suraj, Z.: Inhibitory Rules in Data Analysis. In: A Rough Set Approach. Studies in Computational Intelligence, vol. 163, Springer, Heidelberg (2009)

    Google Scholar 

  13. Doherty, P., Łukaszewicz, W., Skowron, A., Szałas, A.: Knowledge Representation Techniques: A Rough Set Approach. Studies in Fuzziness and Soft Computing, vol. 202. Springer, Heidelberg (2006)

    MATH  Google Scholar 

  14. Feng, J., Jost, J., Minping, Q. (eds.): Network: From Biology to Theory. Springer, Berlin (2007)

    Google Scholar 

  15. 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 

  16. Goldin, D., Smolka, S., Wegner, P.: Interactive Computation: The New Paradigm. Springer, Heidelberg (2006)

    MATH  Google Scholar 

  17. Hastie, T., Tibshirani, R., Friedman, J.H.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd edn. Springer, Heidelberg (2008)

    Google Scholar 

  18. Jankowski, A., Peters, J., Skowron, A., Stepaniuk, J.: Optimization in discovery of compound granules. Fundamenta Informaticae 85(1-4), 249–265 (2008)

    MATH  MathSciNet  Google Scholar 

  19. Jankowski, A., Skowron, A.: A WisTech paradigm for intelligent systems. In: Peters, J.F., Skowron, A., Düntsch, I., Grzymała-Busse, J.W., Orłowska, E., Polkowski, L. (eds.) Transactions on Rough Sets VI. LNCS, vol. 4374, pp. 94–132. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  20. Jankowski, A., Skowron, A.: Logic for artificial intelligence: The Rasiowa - Pawlak school perspective. In: Ehrenfeucht, A., Marek, V., Srebrny, M. (eds.) Andrzej Mostowski and Foundational Studies, pp. 106–143. IOS Press, Amsterdam (2007)

    Google Scholar 

  21. Jankowski, A., Skowron, A.: Wisdom Granular Computing. In: Pedrycz, W., et al. (eds.) [39], pp. 229–345 (2008)

    Google Scholar 

  22. Luck, M., McBurney, P., Preist, C.: Agent Technology. Enabling Next Generation Computing: A Roadmap for Agent Based Computing. AgentLink (2003)

    Google Scholar 

  23. 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 

  24. Mitchell, M.: Complex systems: Network thinking. Artificial Intelligence 170(18), 1194–1212 (2006)

    Article  MathSciNet  Google Scholar 

  25. 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.z., Świniarski, R.W., Szczuka, M.S. (eds.) Transactions on Rough Sets I. LNCS, vol. 3100, pp. 187–208. Springer, Heidelberg (2004)

    Google Scholar 

  26. Nguyen, H.S., Skowron, A.: A rough granular computing in discovery of process models from data and domain knowledge. Journal of Chongqing University of Post and Telecommunications 20(3), 341–347 (2008)

    Google Scholar 

  27. Nguyen, H.S., Jankowski, A., Skowron, A., Stepaniuk, J., Szczuka, M.: Discovery of process models from data and domain knowledge: A rough-granular approach. In: Yao, J.T. (ed.) Novel Developments in Granular Computing: Applications for Advanced Human Reasoning and Soft Computation. IGI Global, Hershey (to appear)

    Google Scholar 

  28. Nguyen, T.T.: Eliciting domain knowledge in handwritten digit recognition. In: Pal, S.K., Bandyopadhyay, S., Biswas, S. (eds.) PReMI 2005. LNCS, vol. 3776, pp. 762–767. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  29. Nguyen, T.T.: Outlier and exception analysis in rough sets and granular computing. In: Pedrycz, W., et al. (eds.) [39], pp. 823–834 (2008)

    Google Scholar 

  30. Nguyen, T.T., Skowron, A.: Rough-granular computing in human-centric information processing. In: Bargiela, A., Pedrycz, W. (eds.) Human-Centric Information Processing Through Granular Modelling. Studies in Computational Intelligence, Springer, Heidelberg (in press)

    Google Scholar 

  31. 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)

    MATH  MathSciNet  Google Scholar 

  32. Pancerz, K., Suraj, Z.: Discovering concurrent models from data tables with the ROSECON. Fundamenta Informaticae 60(1-4), 251–268 (2004)

    MATH  MathSciNet  Google Scholar 

  33. Papageorgiou, E.I., Stylios, C.D.: Fuzzy Cognitive Maps. In: Pedrycz, W., et al. (eds.) [39], pp. 755–774 (2008)

    Google Scholar 

  34. Pal, S.K., Polkowski, L., Skowron, A. (eds.): Rough-Neural Computing: Techniques for Computing with Words. Cognitive Technologies. Springer, Berlin (2004)

    Google Scholar 

  35. Pawlak, Z.: Rough sets. International Journal of Computer and Information Sciences 11, 341–356 (1982)

    Article  MATH  MathSciNet  Google Scholar 

  36. Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning about Data. System Theory, Knowledge Engineering and Problem Solving, vol. 9. Kluwer Academic Publishers, Dordrecht (1991)

    Google Scholar 

  37. Pawlak, Z.: Concurrent versus sequential: the rough sets perspective. Bulletin of the EATCS 48, 178–190 (1992)

    MATH  Google Scholar 

  38. 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 

  39. Pedrycz, W., Skowron, A., Kreinovich, V. (eds.): Handbook of Granular Computing. John Wiley & Sons, New York (2008)

    Google Scholar 

  40. Poggio, T., Smale, S.: The mathematics of learning: Dealing with data. Notices of the AMS 50(5), 537–544 (2003)

    MATH  MathSciNet  Google Scholar 

  41. Polkowski, L., Skowron, A.: Rough mereology: A new paradigm for approximate reasoning. International Journal of Approximate Reasoning 51, 333–365 (1996)

    Article  MathSciNet  Google Scholar 

  42. Ramsay, J.O., Silverman, B.W.: Applied Functional Data Analysis. Springer, Berlin (2002)

    Book  MATH  Google Scholar 

  43. Rissanen, J.: Minimum-description-length principle. In: Kotz, S., Johnson, N. (eds.) Encyclopedia of Statistical Sciences, pp. 523–527. John Wiley & Sons, New York (1985)

    Google Scholar 

  44. Roddick, J.F., Hornsby, K., Spiliopoulou, M.: An updated bibliography of temporal, spatial and spatio- temporal data mining research. In: Roddick, J., Hornsby, K.S. (eds.) TSDM 2000. LNCS (LNAI), vol. 2007, pp. 147–163. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

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

    MATH  Google Scholar 

  46. Skowron, A., Suraj, Z.: Discovery of Concurrent Data Models from Experimental Tables: A Rough Set Approach. In: First International Conference on Knowledge Discovery and Data Mining, pp. 288–293. AAAI Press, Menlo Park (1995)

    Google Scholar 

  47. Skowron, A., Stepaniuk, J.: Tolerance approximation spaces. Fundamenta Informaticae 27, 245–253 (1996)

    MATH  MathSciNet  Google Scholar 

  48. Skowron, A., Stepaniuk, J.: Information granules and rough-neural computing. In: Pal, S.K., Polkowski, L., Skowron, A. (eds.) Rough-Neural Computing: Techniques for Computing with Words, Cognitive Technologies, pp. 43–84. Springer, Heidelberg (2003)

    Google Scholar 

  49. Skowron, A., Stepaniuk, J.: Rough sets and granular computing: Toward rough-granular computing. In: Pedrycz, W., et al. (eds.) [39], pp. 425–448 (2008)

    Google Scholar 

  50. Skowron, A., Stepaniuk, J., Peters, J., Swiniarski, R.: Calculi of approximation spaces. Fundamenta Informaticae 72(1-3), 363–378 (2006)

    MATH  MathSciNet  Google Scholar 

  51. Skowron, A., Szczuka, M.: Toward interactive computations: A rough-granular approach. In: Koronacki, J., Wierzchon, S.T., Ras, Z.W., Kacprzyk, J. (eds.) Advances in Machine learning II, Dedicated to the memory of Ryszard Michalski. Studies in Computational Intelligence, vol. 263, pp. 23–42. Springer, Heidelberg (2010)

    Google Scholar 

  52. Skowron, A., Bazan, J.G., Wojnarski, M.: Interactive rough-granular computing in pattern recognition. In: Chaudhury, S., Mitra, S., Murthy, C.A., Sastry, P.S., Pal, S.K. (eds.) PReMI 2009. LNCS (LNAI), vol. 5909, pp. 92–97. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  53. Skowron, A., Synak, P.: Complex patterns. Fundamenta Informaticae 60(1-4), 351–366 (2004)

    MATH  MathSciNet  Google Scholar 

  54. Ślȩzak, D.: Approximate entropy reducts. Fundamenta Informaticae 53(3-4), 365–387 (2002)

    MathSciNet  Google Scholar 

  55. Sun, R. (ed.): Cognition and Multi-Agent Interaction. From Cognitive Modeling to Social Simulation. Cambridge University Press, New York (2006)

    Google Scholar 

  56. 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/Physica, Heidelberg (2000)

    Google Scholar 

  57. Unnikrishnan, K.P., Ramakrishnan, N., Sastry, P.S., Uthurusamy, R.: Network Reconstruction from Dynamic Data. SIGKDD Explorations 8(2), 90–91 (2006)

    Article  Google Scholar 

  58. Wegner, P.: Why interaction is more powerful than algorithms. Communications of the ACM 40(5), 80–91 (1997)

    Article  Google Scholar 

  59. Wu, F.-X.: Inference of gene regulatory networks and its validation. Current Bioinformatics 2(2), 139–144 (2007)

    Article  Google Scholar 

  60. Zadeh, L.A.: A new direction in AI-toward a computational theory of perceptions. AI Magazine 22(1), 73–84 (2001)

    Google Scholar 

  61. Zadeh, L.A.: Generalized theory of uncertainty (GTU)-principal concepts and ideas. Computational Statistics and Data Analysis 51, 15–46 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  62. The Rough Set Interactive Classificstion Engine (RoughICE) homepage, http://logic.mimuw.edu.pl/~bazan/roughice

  63. The Rough Set Exploration System (RSES) homepage, http://logic.mimuw.edu.pl/~rses

  64. The RSES-lib project homepage, http://rsproject.mimuw.edu.pl

  65. The road simulator homepage, http://logic.mimuw.edu.pl/~bazan/simulator

  66. The TunedIT platform homepage, http://tunedit.org/

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Skowron, A. (2010). Discovery of Processes and Their Interactions from Data and Domain Knowledge. In: Jędrzejowicz, P., Nguyen, N.T., Howlet, R.J., Jain, L.C. (eds) Agent and Multi-Agent Systems: Technologies and Applications. KES-AMSTA 2010. Lecture Notes in Computer Science(), vol 6070. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13480-7_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-13480-7_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13479-1

  • Online ISBN: 978-3-642-13480-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics