Le Goc Marc, Benayadi Nabil


This paper is concerned with the discovery of expert’s knowledge from a sequence of alarms provided by a knowledge based system monitoring a dynamic process. The discovering process is based on the principles and the tools of the Stochastic Approach framework where a sequence is represented with a Markov chain from which binary relations between discrete event classes can be find and represented as abstract chronicle models. The problem with this approach is to reduce the search space as close as possible to the relations between the process variables. To this aim, we propose an adaptation of the J-Measure to the Stochastic Approach framework, the BJ-Measure, to build an entropic based heuristic that help in finding abstract chronicle models revealing strong relations between the process variables. The result of the application of this approach to a real world system, the Sachem system that controls the blast furnace of the Arcelor-Mittal Steel group, is provided in the paper, showing how the combination of the Stochastic Approach and the Information Theory allows finding the a priori expert’s knowledge between blast furnace variables from a sequence of alarms.


  1. Agrawal, R., Imielinski, T., and Swami, A. (1993). Mining association rules between sets of items in large databases. Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, pages 207-216.
  2. Agrawal, R. and Srikant, R. (1995). Mining sequential patterns. Proceedings of the 11th International Conference on Data Engineering (ICDE95), pages 3-14.
  3. Bayardo, R. J. and Agrawal, R. (1999). Mining the most interesting rules. In Proceedings of ACM KDD1999, ACM Press, page 145154.
  4. Benayadi, N. and Le Goc, M. (2007). Using an oriented jmeasure to prune chronicle models. To appear in the proceedings of the 18th International Workshop on the Principles of Diagnosis (DX07), Nashville, USA.
  5. Blachman, N. (1968). The amount of information that y gives about x. Information Theory, IEEE Transactions, 14:27-31.
  6. Bouché, P., Le Goc, M., and Giambiasi, N. (2005). Modeling discrete event sequences for discovering diagnosis signatures. Proceedings of the Summer Computer Simulation Conference (SCSC05) Philadelphia, USA.
  7. Cauvin, S., Cordier, M.-O., Dousson, C., Laborie, P., Lévy, F., Montmain, J., Montmain, M., Porcheron, M., Servet, I., and Travé, L. (1998). Monitoring and alarm interpretation in in-dustrial environments. AI Communications ,IOS Press , 1998, pages 139-173.
  8. Dousson, C. and Duong, T. V. (1999). Discovering chronicles with numerical time constraints from alarm logs for monitoring dynamic systems. In D. Thomas, editor, Proceedings of the 16th International Joint Conference on Artificial Intelligence (IJCAI-99), 1:620- 626.
  9. Ghallab, M. (1996). On chronicles: Representation, on-line recognition and learning. Proc. Principles of Knowledge Representation and Reasoning, Aiello, Doyle and Shapiro (Eds.) Morgan-Kauffman,, pages 597- 606.
  10. Hanks, S. and Dermott, D. M. (1994). Modeling a dynamic and uncertain world i: symbolic and probabilistic reasoning about change. Artificial Intelligence, pages 1- 55.
  11. Hatonen, K., Klemettinen, M., Mannila, H., Ronkainen, P., and Toivonen, H. (1996a). Knowledge discovery from telecommunication network alarm databases. In 12th International Conference on Data Engineering (ICDE 7896). New Orleans, LA, pages 115-122.
  12. Hatonen, K., Klemettinen, M., Mannila, H., Ronkainen, P., and Toivonen, H. (1996b). Tasa: Telecommunication alarm sequence analyzer, or how to enjoy faults in your network. In IEEE Network Operations and Management Symposium (NOMS 7896). Kyoto, Japan, pages 520-529.
  13. Hilderman, R. and Hamilton, H. (2001). Knowledge discovery and measures of interest. Kluwer Academic publishers.
  14. Huynh, X.-H., Guillet, F., and Briand., H. (2005). Arqat: An exploratory analysis tool for interestingness measures. In Proceedings of the 11th international symposium on Applied Stochastic Models and Data Analysis ASMDA-2005, page 334344.
  15. Jaroszewicz, S. and Simovici, D. A. (2001). A general measure of rule interestingness. In Proceedings of PKDD2001, Springer-Verlag, page 253265.
  16. Le Goc, M. (2004). Sachem, a real time intelligent diagnosis system based on the discrete event paradigm. Simulation, The Society for Modeling and Simulation International Ed., 80(11):591-617.
  17. Le Goc, M. (2006). Notion d'observation pour le diagnostic des processus dynamiques: Application à Sachem et à la découverte de connaissances temporelles. Hdr, Faculté des Sciences et Techniques de Saint Jéroˆme.
  18. Le Goc, M., Bouché, P., and Giambiasi, N. (2005). Stochastic modeling of continuous time discrete event sequence for diagnosis. 16th International Workshop on Principles of Diagnosis (DX'05) Pacific Grove, California, USA.
  19. Liu, B., Hsu, W., Chen, S., and Ma, Y. (2000). Analyzing the subjective interestingness of association rules. IEEE Intelligent Systems, page 4755.
  20. Mannila, H. (2002). Local and global methods in data mining: Basic techniques and open problems. 9th International Colloquium on Automata, Languages and Programming, Malaga, Spain,, 2380:57-68.
  21. Mannila, H., Toivonen, H., and Verkamo, A. I. (1997). Discovery of frequent episodes in event sequences. Data Mining and Knowledge Discovery, pages 259-289.
  22. Padmanabhan, B. and Tuzhilin, A. (1999). Unexpectedness as a measure of interestingness in knowledge discovery. Decision Support Systems, pages 303-318.
  23. Roddick, J. and Spiliopoulou, M. (2002). A survey of temporal knowledge discovery paradigms and methods. IEEE Transactions on Knowledge and Data Engineering, 14:750-767.
  24. Shannon, C. and Weaver, W. (1949). The mathematical theory of communication. University of Illinois Press, 27:379-423.
  25. Shore, J. and Johnson, R. (1980). Axiomatic derivation of the principle of maximum entropy and the principle of minimum cross-entropy. Information Theory, IEEE Transactions, 26:26-37.
  26. Smyth, P. and Goodman, R. M. (1992). An information theoretic approach to rule induction from databases. IEEE Transactions on Knowledge and Data Engineering 4, page 301316.
  27. Tan, P.-N., Kumar, V., and Srivastava, J. (2004). Selecting the right objective measure for association analysis. Information Systems, 29(4):293-313.
  28. Theil, H. (1970). On the estimation of relationships involving qualitative variables. American Journal of Sociology, pages 103-154.
  29. Vaillant, B., Lenca, P., and Lallich, S. (2004). A clustering of interestingness measures. In Proceedings of the 7th International Conference on Discovery Science, pages 290-297.

Paper Citation

in Harvard Style

Goc Marc L. and Nabil B. (2008). DISCOVERING EXPERT’S KNOWLEDGE FROM SEQUENCES OF DISCRETE EVENT CLASS OCCURRENCES . In Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-8111-37-1, pages 253-260. DOI: 10.5220/0001695702530260

in Bibtex Style

author={Le Goc Marc and Benayadi Nabil},
booktitle={Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 2: ICEIS,},

in EndNote Style

JO - Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 2: ICEIS,
SN - 978-989-8111-37-1
AU - Goc Marc L.
AU - Nabil B.
PY - 2008
SP - 253
EP - 260
DO - 10.5220/0001695702530260