Discovery of process models from data and domain knowledge: A rough-granular approach

Novel Developments in Granular Computing: Applications for Advanced Human Reasoning and Soft Computation

Volumn , Issue , 2010, Pages 16-47

  Nguyen, Hung Son ^a   Jankowski, Andrzej ^b,e   Peters, James F ^c   Skowron, Andrzej ^a   Stepaniuk, Jarosław ^d   Szczuka, Marcin ^a  

^a UNIVERSITY OF WARSAW   (Poland)

^b Institute of Decision Processes Support and AdgaM Solutions Sp Z Oo   (Poland)

^c UNIVERSITY OF MANITOBA   (Canada)

^d BIALYSTOK UNIVERSITY OF TECHNOLOGY   (Poland)

^e University of North Carolina at Charlotte   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84855758268     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.4018/978-1-60566-324-1.ch002     Document Type: Chapter

References (76)

1. Aggarwal, C. (2007). Data Streams: Models and Algorithms. Berlin, Germany: Springer.
2. Bargiela, A., & Pedrycz, W. (2003). Granular Computing: An Introduction. Dordrecth, The Netherlands: Kluwer Academic Publishers.
3. Barwise, J., & Seligman, J. (1997). Information Flow: The Logic of Distributed Systems. Cambridge, UK: Cambridge University Press.
4. Bazan, J., Kruczek, P., Bazan-Socha, S., Skowron, A., & Pietrzyk, J. J. (2006, July 2-7). Risk pattern identification in the treatment of infants with respiratory failure through rough set modeling. In Proceedings of Information Processing and Management under Uncertainty in Knowledge-Based Systems (IPMU'2006), (no. 3, pp. 2650-2657). Paris, France
5. Bazan, J., Kruczek, P., Bazan-Socha, S., Skowron, A., & Pietrzyk, J. J. (2006a). 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., and Słowiński, R. (eds.), Fifth International Conference on Rough Sets and Current Trends in Computing (RSCTC 2006), Kobe, Japan, November 6-8, 2006, volume 4259 of Lecture Notes in Artificial Intelligence, (pp. 418-427). Heidelberg, Germany: Springer.
6. Bazan, J., Peters, J. F., & Skowron, A. (2005, September 1-3). Behavioral pattern identification through rough set modelling. In Ślezak, D., Szczuka, M., Duntsch, I., and Yao, Y., (eds.), Proceedings of the Tenth International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing (RSFDGrC 2005), Regina, Canada (volume 3642 of Lecture Notes in Artificial Intelligence, pp. 688-697). Berlin: Springer.
7. Bazan, J., & Skowron, A. (2005a). Classifiers based on approximate reasoning schemes. In Dunin-Ke{ogonek}plicz, B., Jankowski, A., Skowron, A., & Szczuka, M. (Eds.), Monitoring, Security, and Rescue Techniques in Multiagent Systems, Advances in Soft Computing (pp. 191-202). Heidelberg, Germany: Springer. doi:10.1007/3-540-32370-8_13
8. Bazan, J., Skowron, A., & Swiniarski, R. (2006). Rough sets and vague concept approximation: From sample approximation to adaptive learning. Transactions on Rough Sets V, LNCS 4100(5), 39-62.
9. Borrett, S. R., & Bridewell, W.R., R. P. P. L. K. Arrigo. (2007). A method for representing and developing process models. Ecological Complexity, 4(1-2), 1-12. doi:10.1016/j.ecocom.2007.02.017
10. Breiman, L. (2001). Statistical modeling: the two cultures. Statistical Science, 16(3), 199-231. doi:10.1214/ ss/1009213726
11. Bronshtein, I., Semendyayev, K., Musiol, G., & Muehlig, H. (Eds.). (2003). Handbook of Mathematics. Berlin Heidelberg, Germany: Springer Verlag.
12. de Medeiros, A. K. A., Weijters, A. J. M. M., & van der Aalst, W. M. P. P. (2007). Genetic process mining: An experimental evaluation. Data Mining and Knowledge Discovery, 14, 245-304. doi:10.1007/ s10618-006-0061-7
13. Doherty, P. ÃLukaszewicz, W., Skowron, A., &Szałas, A. (2006). Knowledge Representation Techniques: A Rough Set Approach, volume 202 of Studies in Fuzziness and Soft Computing. Berlin Heidelberg, Germany: Springer Verlag.
14. Domingos, P. P. (2007). Toward knowledge-rich data mining. Data Mining and Knowledge Discovery, 15, 21-28. doi:10.1007/s10618-007-0069-7
15. Feng, J., Jost, J., & Minping, Q. (2007). Network: From Biology to Theory. Berlin Heidelberg, Germany: Springer Verlag. doi:10.1007/978-1-84628-780-0
16. Friedman, J. H. (1997). Data mining and statistics. What's the connection? Keynote address. In Proceedings of the 29th Symposium on the Interface: Computing Science and Statistics, Houston, TX.
17. Gerbrandy, J., & Groeneveld, W. (1997). Reasoning about information change. Journal of Logic Language and Information, 6(2), 147-169. doi:10.1023/A:1008222603071
18. Goldin, D., Smolka, S., & Wegner, P. P. (2006). Interactive Computation: The New Paradigm. Berlin Heidelberg, Germany: Springer Verlag.
19. Guerrero, F. (2008). Solving the oil equation by a team of geophysicists and computer scientists. IEEE Spectrum, 33-36.
20. Jankowski, A., Peters, J., Skowron, A., & Stepaniuk, J. (2008). Optimization in discovery of compound granules. Fundamenta Informaticae, 85(1-4), 249-265.
21. Jankowski, A., & Skowron, A. (2007). A wistech paradigm for intelligent systems. Transactions on Rough Sets VI, LNCS, 4374(5), 94-132. doi:10.1007/978-3-540-71200-8_7
22. Jankowski, A., & Skowron, A. (2008a). 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). Amsterdam: IOS Press.
23. Kleinberg, J., Papadimitriou, C., & Raghavan, P. P. (1998). A microeconomic view of data mining. Data Mining and Knowledge Discovery, 2, 311-324. doi:10.1023/A:1009726428407
24. Kriegel, H.-P., Borgwardt, K. M., Kroger, P., Pryakhin, A., Schubert, M., & Zimek, A. (2007). Future trends in data mining. Data Mining and Knowledge Discovery, 15(1), 87-97. doi:10.1007/s10618-007-0067-9
25. Luck, M., McBurney, P., & Preist, C. (2003). Agent technology. Enabling next generation computing: A roadmap for agent based computing. Retrieved from AgentLink.org
26. Mitchell, M. (2007). Complex systems: Network thinking. Artificial Intelligence, 170(18), 1194-1212. doi:10.1016/j.artint.2006.10.002
27. Nguyen, H. S., & Skowron, A. (2008). 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.
28. Nguyen, S. H., Bazan, J., Skowron, A., & Nguyen, H. S. (2004). Layered learning for concept synthesis. Transactions on Rough Sets I, LNCS 3100(1), 187-208.
29. Nguyen, T. T., Paddon, C. P. P. W. D. J., & Nguyen, H. S. (2006). Learning sunspot classification. Fundamenta Informaticae, 72(1-3), 295-309.
30. Pal, S. K., Bandoyopadhay, S., & Biswas, S. (Eds.). (2005, December 18-22). Proceedings of the First International Conference on Pattern Recognition and Machine Intelligence (PReMI'05), Indian Statistical Institute, volume 3776 of Lecture Notes in Computer Science, Berlin Heidelberg, Germany. Springer Verlag.
31. Pal, S. K., Polkowski, L., & Skowron, A. (Eds.). (2004). RoughNeural Computing: Techniques for Computing with Words. Berlin: Cognitive Technologies. Springer.
32. Pancerz, K., & Suraj, Z. (2004). Discovering concurrent models from data tables with the ROSECON. Fundamenta Informaticae, 60(1-4), 251-268.
33. Pawlak, Z. (1981). Classification of objects by means of attributes. (Technical Report 429), Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland.
34. Pawlak, Z. (1982). Rough sets. International Journal of Computer and Information Sciences, 11, 341-356. doi:10.1007/BF01001956
35. Pawlak, Z. (1991). Rough Sets: Theoretical Aspects of Reasoning about Data: Vol. 9. System Theory, Knowledge Engineering and Problem Solving. Dordrecht, The Netherlands: Kluwer Academic Publishers.
36. Pawlak, Z. (1992). Concurrent versus sequential the rough sets perspective. Bulletin of the EATCS, 48, 178-190.
37. Pawlak, Z. (1999). Rough sets, rough functions and rough calculus. In Pal, S., & Skowron, A. (Eds.), Rough Fuzzy Hybridization, A New Trend in Decision Making (pp. 99-109). Berlin, Germany: Springer.
38. Pawlak, Z., Peters, J. F., Skowron, A., Suraj, Z., Ramanna, S., & Borkowski, M. (2001). Rough measures and integrals: A brief introduction. In Terano, T., Nishida, T., Namatame, A., Tsumoto, S., Ohsawa, Y., & Washio, T. (Eds.), New Frontiers in Artificial Intelligence, Joint JSAI 2001 Workshop Post Proceedings, Lecture Notes in Artificial Intelligence (Vol. 2253, pp. 374-379). Berlin, Germany: Springer.
39. Pawlak, Z., & Skowron, A. (2007). Rudiments of rough sets; Rough sets: Some extensions; Rough sets and boolean reasoning. Information Sciences, 177(1), 3-27, 28-40, 41-73. doi:10.1016/j.ins.2006.06.003
40. Pedrycz, W. (Ed.). (2001). Granular Computing. Berlin/Heidelberg, Germany: Springer/Verlag.
41. Pedrycz, W., Skowron, A., & Kreinovich, V. (Eds.). (2008). Handbook of Granular Computing. New York: John Wiley & Sons. doi:10.1002/9780470724163
42. Peters, J. F. (1998). Time and clock information systems: Concepts and roughly fuzzy petri net models. In Rough Sets in Knowledge Discovery 2: Applications, Case Studies and Software Systems, of Studies in Fuzziness and Soft Computing (Vol. 19, pp. 385-418). Berlin/Heidelberg, Germany: Springer/Verlag.
43. Peters, J. F. (2005). Rough ethology: Towards a biologically-inspired study of collective behavior in intelligent systems with approximation spaces. Transactions on Rough Sets III, LNCS, 3100(3), 153-174.
44. Peters, J. F. (2007a). Near sets: General theory about nearness of objects. Applied Mathematical Sciences, 1(53), 2609-2629.
45. Peters, J. F. (2007b). Near sets: Special theory about nearness of objects. Fundamenta Informaticae, 75(1-4), 407-433.
46. Peters, J. F. (2008a). Classification of perceptual objects by means of features. International Journal of Information Technology and Intelligent Computing, 3(2), 1-35.
47. Peters, J. F. (2008b). Discovery of perceptually near information granules. In Yao, J. (Ed.), Novel Developments in Granular Computing: Applications for Advanced Human Reasoning and Soft Computation. Hershey, PA: IGI Global.
48. Peters, J. F., Shahfar, S., Ramanna, S., & Szturm, T. (2007a). Biologically-inspired adaptive learning: A near set approach. In Proceedings of Frontiers in the Convergence of Bioscience and Information Technologies (FBIT07), (pp. 403-408). Washington, DC: SERSC, IEEE Computer Society.
49. Peters, J. F., & Skowron, A. (1999). Approximate realtime decision making: Concepts and rough fuzzy petri net models. International Journal of Intelligent Systems, 14, 805-839. doi:10.1002/(SICI)1098111X(199908)14:8<805::AID-INT5>3.0.CO;2-R
50. Peters, J. F., Skowron, A., & Stepaniuk, J. (2007b). Nearness of objects: Extension of approximation space model. Fundamenta Informaticae, 79(3-4), 497-512.
51. Peters, J. F., & Wasilewski, P. P. (2008). (Manuscript submitted for publication). Foundations of near sets. [pending publication.]. Information Sciences.
52. Poggio, T., & Smale, S. (2003). The mathematics of learning: Dealing with data. Notices of the AMS, 50(5), 537-544.
53. Ramsay, J. O., & Silverman, B. W. (2002). Applied Functional Data Analysis. Berlin: Springer. doi:10.1007/b98886
54. Ras, Z. W., & Wyrzykowska, E. (2007). Extended action rule discovery based on single classification rules and reducts. In Hassanien, A., Suraj, Z., Slezak, D., & Lingras, P. (Eds.), Rough Computing: Theories, Technologies and Applications (pp. 175-184). Hershey, PA: IGI Global.
55. Rissanen, J. (1985). Minimum-description-length principle. In Kotz, S., & Johnson, N. (Eds.), Encyclopedia of Statistical Sciences (pp. 523-527). New York: John Wiley & Sons.
56. Roddick, J. F., Hornsby, K., & Spiliopoulou, M. (2001). An updated bibliography of temporal, spatial and spatio-temporal data mining research. In Roddick, J. F. and Hornsby, K., (eds.), Post-Workshop Proceedings of the International Workshop on Temporal, Spatial and Spatio-Temporal Data Mining, Lecture Notes in Artificial Intelligence, (volume 2007, pp. 147-163). Berlin, Germany: Springer.
57. Shoham, Y. (1998). Reasoning about Change. Cambridge, MA: MIT Press.
58. Skowron, A., & Stepaniuk, J. (1996). Tolerance approximation spaces. Fundamenta Informaticae, 27, 245-253.
59. Skowron, A., & Stepaniuk, J. (2003). 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). Berlin, Germany: Springer.
60. Skowron, A., Stepaniuk, J., Peters, J., & Swiniarski, R. (2006). Calculi of approximation spaces. Fundamenta Informaticae, 72(1-3), 363-378.
61. Skowron, A., Stepaniuk, J., & Peters, J. F. (2003). Rough sets and infomorphisms: Towards approximation of relations in distributed environments. Fundamenta Informaticae, 54(2-3), 263-277.
62. Skowron, A., & Suraj, Z. (1993). Rough sets and concurrency. Bulletin of the Polish Academy of Sciences, 41, 237-254.
63. Skowron, A., & Suraj, Z. (1995). Discovery of concurrent data models from experimental tables: A rough set approach. In Proceedings of the First International Conference on Knowledge Discovery and Data Mining, (pp. 288-293), Menlo Park, CA: AAAI Press.
64. Skowron, A., & Synak, P. P. (2004). Complex patterns. Fundamenta Informaticae, 60(1-4), 351-366.
65. Ślȩzak, D. (2002). Approximate entropy reducts. Fundamenta Informaticae, 53(3-4), 365-387.
66. Stevens, R., Brook, P., Jacksona, K., & Arnold, S. (1998). Systems Engineering. Coping with Complexity. London: Prentice-Hall.
67. Sun, R. (Ed.). (2006). Cognition and Multi-Agent Interaction. From Cognitive Modeling to Social Simulation. Cambridge, UK: Cambridge University Press.
68. Suraj, Z. (2000). Rough set methods for the synthesis and analysis of concurrent processes. In Polkowski, L., Lin, T., & 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). Heidelberg, Germany: Springer.
69. Unnikrishnan, K. P., & Ramakrishnan, N. S., Sastry, P., &Uthurusamy, R. (eds.) (2006). Proceedings of 4th KDD Workshop on Temporal Data Mining: Network Reconstruction from Dynamic Data at KDD 2006 Conference, Philadelphia. ACM SIGKDD.
70. van Benthem, J. (2007). Cognition as interaction. In Bouma, G., KrÄamer, I., and Zwarts, J., editors, Cognitive Foundations of Interpretation, (pp. 27-38). Amsterdam, KNAW.
71. van Benthem, J. (2008To appear). Logic games: From tools to models of interaction. In Gupta, A., Parikh, R., & van Benthem, J. (Eds.), Logic at the Crossroads (pp. 283-317). Mumbai, India: Allied Publishers.
72. Vapnik, V. (1998). Statisctical Learning Theory. New York: John Wiley & Sons.
73. Wikipedia (n.d.). Wiki article on Interaction. Retrieved from http://en.wikipedia.org/wiki/Interaction
74. Wu, F.-X. (2007). Inference of gene regulatory networks and its validation. Current Bioinformatics, 2(2), 139-144. doi:10.2174/157489307780618240
75. Zadeh, L. A. (2001). A new direction in AI - toward a computational theory of perceptions. AI Magazine, 22(1), 73-84.
76. Zadeh, L. A. (2006). Generalized theory of uncertainty (GTU)-principal concepts and ideas. Computational Statistics & Data Analysis, 51, 15-46. doi:10.1016/j.csda.2006.04.029