A Fresh Look at Precision in Process Conformance

  • Jorge Muñoz-Gama
  • Josep Carmona
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6336)

Abstract

Process Conformance is a crucial step in the area of Process Mining: the adequacy of a model derived from applying a discovery algorithm to a log must be certified before making further decisions that affect the system under consideration. Among the different conformance dimensions, in this paper we propose a novel measure for precision, based on the simple idea of counting these situations were the model deviates from the log. Moreover, a log-based traversal of the model that avoids inspecting its whole behavior is presented. Experimental results show a significant improvement when compared to current approaches for the same task. Finally, the detection of the shortest traces in the model that lead to discrepancies is presented.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
  2. 2.
    Bergenthum, R., Desel, J., Lorenz, R., Mauser, S.: Process mining based on regions of languages. In: Alonso, G., Dadam, P., Rosemann, M. (eds.) BPM 2007. LNCS, vol. 4714, pp. 375–383. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  3. 3.
    Calders, T., Günther, C.W., Pechenizkiy, M., Rozinat, A.: Using minimum description length for process mining. In: SAC, pp. 1451–1455. ACM, New York (2009)CrossRefGoogle Scholar
  4. 4.
    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
  5. 5.
    de Medeiros, A.K.A., van der Aalst, W.M.P., Weijters, A.J.M.M.: Quantifying process equivalence based on observed behavior. Data Knowl. Eng. 64(1), 55–74 (2008)CrossRefGoogle Scholar
  6. 6.
    Greco, G., Guzzo, A., Pontieri, L., Saccà, D.: Discovering expressive process models by clustering log traces. IEEE Trans. Knowl. Data Eng. 18(8), 1010–1027 (2006)CrossRefGoogle Scholar
  7. 7.
    Murata, T.: Petri nets: Properties, analysis and applications. Proceedings of the IEEE 77(4), 541–580 (1989)CrossRefGoogle Scholar
  8. 8.
    Rozinat, A., de Medeiros, A.K.A., Günther, C.W., Weijters, A.J.M.M., van der Aalst, W.M.P.: The need for a process mining evaluation framework in research and practice. In: ter Hofstede, A.H.M., Benatallah, B., Paik, H.-Y. (eds.) BPM Workshops 2007. LNCS, vol. 4928, pp. 84–89. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  9. 9.
    Rozinat, A., de Medeiros, A.K.A., Günther, C.W., Weijters, A.J.M.M., van der Aalst, W.M.P.: Towards an evaluation framework for process mining algorithms. BPM Center Report BPM-07-06, BPMcenter. org. (2007)Google Scholar
  10. 10.
    Rozinat, A., van der Aalst, W.M.P.: Conformance testing: measuring the alignment between event logs and process models. In: BETA Working Paper Series, Eindhoven University of Technology, vol. 144, pp. 203–210. Eindhoven University of Technology, WP (2005)Google Scholar
  11. 11.
    Rozinat, A., van der Aalst, W.M.P.: Conformance checking of processes based on monitoring real behavior. Information Systems 33(1), 64–95 (2008)CrossRefGoogle Scholar
  12. 12.
    Solé, M., Carmona, J.: Process mining from a basis of state regions. In: Lilius, J., Penczek, W. (eds.) PETRI NETS 2010. LNCS, vol. 6128, pp. 226–245. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  13. 13.
    van der Aalst, W.M.P.: Process-aware information systems: Lessons to be learned from process mining. T. Petri Nets and Other Models of Concurrency 2, 1–26 (2009)CrossRefGoogle Scholar
  14. 14.
    van der Aalst, W.M.P., de Medeiros, A.K.A., Weijters, A.J.M.M.: Genetic process mining. In: Ciardo, G., Darondeau, P. (eds.) ICATPN 2005. LNCS, vol. 3536, pp. 48–69. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  15. 15.
    van der Aalst, W.M.P., Rubin, V., Verbeek, H.M.W.E., van Dongen, B.F., Kindler, E., Günther, C.W.: Process mining: a two-step approach to balance between underfitting and overfitting. Software and Systems Modeling (2009)Google Scholar
  16. 16.
    van der Aalst, W.M.P., van Dongen, B.F., Herbst, J., Maruster, L., Schimm, G., Weijters, A.J.M.M.: Workflow mining: A survey of issues and approaches. Data Knowl. Eng. 47(2), 237–267 (2003)CrossRefGoogle Scholar
  17. 17.
    van der Aalst, W.M.P., Weijters, A.J.M.M., Maruster, L.: Workflow mining: Discovering process models from event logs. IEEE Transactions on Knowledge and Data Engineering 16(9), 1128–1142 (2004)CrossRefGoogle Scholar
  18. 18.
    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
  19. 19.
    van Dongen, B.F., Mendling, J., van der Aalst, W.M.P.: Structural patterns for soundness of business process models. In: EDOC, pp. 116–128. IEEE Computer Society Press, Los Alamitos (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Jorge Muñoz-Gama
    • 1
  • Josep Carmona
    • 1
  1. 1.Universitat Politècnica de CatalunyaSpain

Personalised recommendations