Divide-and-Conquer Strategies for Process Mining

  • Josep Carmona
  • Jordi Cortadella
  • Michael Kishinevsky
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5701)


The goal of Process Mining is to extract process models from logs of a system. Among the possible models to represent a process, Petri nets is an ideal candidate due to its graphical representation, clear semantics and expressive power. The theory of regions can be used to transform a log into a Petri net, but unfortunately the transformation requires algorithms with high complexity. This paper provides techniques to overcome this limitation. Either by using decomposition techniques, or by clustering events in the log and working on projections, the proposed approach can be used to widen the applicability of classical region-based techniques.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
  2. 2.
    Badouel, E., Bernardinello, L., Darondeau, P.: Polynomial algorithms for the synthesis of bounded nets. In: Mosses, P.D., Schwartzbach, M.I., Nielsen, M. (eds.) CAAP 1995, FASE 1995, and TAPSOFT 1995. LNCS, vol. 915, pp. 364–383. Springer, Heidelberg (1995)CrossRefGoogle Scholar
  3. 3.
    Bergenthum, R., Desel, J., Lorenz, R., Mauser, S.: Process mining based on regions of languages. In: Proc. 5th Int. Conf. on Business Process Management, September 2007, pp. 375–383 (2007)Google Scholar
  4. 4.
    Carmona, J., Cortadella, J., Kishinevsky, M.: Divide-and-conquer strategies for process mining. Technical Report LSI-08-35-R, Software Department, Universitat Politécnica de Catalunya (2008)Google Scholar
  5. 5.
    Carmona, J., Cortadella, J., Kishinevsky, M.: A region-based algorithm for discovering Petri nets from event logs. In: Dumas, M., Reichert, M., Shan, M.-C. (eds.) BPM 2008. LNCS, vol. 5240, pp. 358–373. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  6. 6.
    Carmona, J., Cortadella, J., Kishinevsky, M., Kondratyev, A., Lavagno, L., Yakovlev, A.: A symbolic algorithm for the synthesis of bounded Petri nets. In: 29th International Conference on Application and Theory of Petri Nets and Other Models of Concurrency (June 2008)Google Scholar
  7. 7.
    Cortadella, J., Kishinevsky, M., Lavagno, L., Yakovlev, A.: Deriving Petri nets from finite transition systems. IEEE Transactions on Computers 47(8), 859–882 (1998)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Cvetković, D., Rowlinson, P., Simić, S.: Eigenspaces of Graphs. Cambridge University Press, Cambridge (1997)CrossRefzbMATHGoogle Scholar
  9. 9.
    de Medeiros, A.K.A., Guzzo, A., Greco, G., van der Aalst, W.M.P., Weijters, A.J.M.M.T., van Dongen, B.F., Saccà, D.: Process mining based on clustering: A quest for precision. In: ter Hofstede, A.H.M., Benatallah, B., Paik, H.-Y. (eds.) BPM Workshops 2007. LNCS, vol. 4928, pp. 17–29. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  10. 10.
    Desel, J., Reisig, W.: The synthesis problem of Petri nets. Acta Inf. 33(4), 297–315 (1996)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Ehrenfeucht, A., Rozenberg, G.: Partial (Set) 2-Structures. Part I, II. Acta Informatica 27, 315–368 (1990)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Fiduccia, C.M., Mattheyses, R.M.: A linear-time heuristic for improving network partitions. In: DAC 1982: Proceedings of the 19th conference on Design automation, Piscataway, NJ, USA, 1982, pp. 175–181. IEEE Computer Society Press, Los Alamitos (1982)Google Scholar
  13. 13.
    Hack, M.: Analysis of production schemata by Petri nets. M.s. thesis, MIT (Feburary 1972)Google Scholar
  14. 14.
    Kernighan, B.W., Lin, S.: An efficient heuristic procedure for partitioning graphs. The Bell system technical journal 49(1), 291–307 (1970)CrossRefzbMATHGoogle Scholar
  15. 15.
    Murata, T.: Petri Nets: Properties, analysis and applications. Proceedings of the IEEE, 541–580 (April 1989)Google Scholar
  16. 16.
    Pretorius, A.J.: Visualization of State Transition Graphs. PhD thesis, Technical University of Eindhoven (2008)Google Scholar
  17. 17.
    Rozinat, A., van der Aalst, W.M.P.: Conformance checking of processes based on monitoring real behavior. Inf. Syst. 33(1), 64–95 (2008)CrossRefGoogle Scholar
  18. 18.
    van der Aalst, W.M.P., Rubin, V., Verbeek, H., van Dongen, B., Kindler, E., Günther, C.: Process mining: A two-step approach to balance between underfitting and overfitting. Technical Report BPM-08-01, BPM Center (2008)Google Scholar
  19. 19.
    van der Aalst, W.M.P., van Dongen, B.F., Günther, C.W., Mans, R.S., de Medeiros, A.K.A., Rozinat, A., Rubin, V., Song, M., Verbeek, H.M.W(E.), Weijters, A.J.M.M.T.: ProM 4.0: Comprehensive support for real process analysis. In: Kleijn, J., Yakovlev, A. (eds.) ICATPN 2007. LNCS, vol. 4546, pp. 484–494. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  20. 20.
    van der Aalst, W.M.P., Weijters, T., Maruster, L.: Workflow mining: Discovering process models from event logs. IEEE Trans. Knowl. Data Eng. 16(9), 1128–1142 (2004)CrossRefGoogle Scholar
  21. 21.
    van der Werf, J.M.E.M., van Dongen, B.F., Hurkens, C.A.J., Serebrenik, A.: Process discovery using integer linear programming. In: van Hee, K.M., Valk, R. (eds.) PETRI NETS 2008. LNCS, vol. 5062, pp. 368–387. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  22. 22.
    Vogler, W.: Modular Construction and Partial Order Semantics of Petri Nets. LNCS, vol. 625. Springer, Heidelberg (1992)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Josep Carmona
    • 1
  • Jordi Cortadella
    • 1
  • Michael Kishinevsky
    • 2
  1. 1.Universitat Politècnica de CatalunyaSpain
  2. 2.Intel CorporationUSA

Personalised recommendations