Probabilistic Declarative Process Mining

  • Elena Bellodi
  • Fabrizio Riguzzi
  • Evelina Lamma
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6291)


The management of business processes is receiving much attention, since it can support significant efficiency improvements in organizations. One of the most interesting problems is the representation of process models in a language that allows to perform reasoning on it.

Various knowledge-based languages have been lately developed for such a task and showed to have a high potential due to the advantages of these languages with respect to traditional graph-based notations.

In this work we present an approach for the automatic discovery of knolwedge-based process models expressed by means of a probabilistic logic, starting from a set of process execution traces. The approach first uses the DPML (Declarative Process Model Learner) algorithm [16] to extract a set of integrity constraints from a collection of traces. Then, the learned constraints are translated into Markov Logic formulas and the weights of each formula are tuned using the Alchemy system. The resulting theory allows to perform probabilistic classification of traces. We tested the proposed approach on a real database of university students’ careers. The experiments show that the combination of DPML and Alchemy achieves better results than DPML alone.


Business Process Management Knowledge-based Process Models Process Mining Statistical Relational Learning 


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  1. 1.
    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 Knowledge Engineering 47(2), 237–267 (2003)CrossRefGoogle Scholar
  2. 2.
    van der Aalst, W.M.P., Pesic, M.: A declarative approach for flexible business processes management. In: Eder, J., Dustdar, S. (eds.) BPM Workshops 2006. LNCS, vol. 4103, pp. 169–180. Springer, Heidelberg (2006)Google Scholar
  3. 3.
    van der Aalst, W.M.P., Pesic, M.: DecSerFlow: Towards a truly declarative service flow language. In: Bravetti, M., Núñez, M., Zavattaro, G. (eds.) WS-FM 2006. LNCS, vol. 4184, pp. 1–23. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  4. 4.
    van der Aalst, W.M.P., Weijters, T., Maruster, L.: Workflow mining: Discovering process models from event logs. IEEE Transactions on Knowledge and Data Engineering 16(9), 1128–1142 (2004)CrossRefGoogle Scholar
  5. 5.
    Agrawal, R., Gunopulos, D., Leymann, F.: Mining process models from workflow logs. In: Schek, H.-J., Saltor, F., Ramos, I., Alonso, G. (eds.) EDBT 1998. LNCS, vol. 1377, pp. 469–483. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  6. 6.
    Alberti, M., Chesani, F., Gavanelli, M., Lamma, E., Mello, P., Torroni, P.: Verifiable agent interaction in abductive logic programming: The SCIFF framework. ACM Transactions on Computational Logic 9(4) (2008)Google Scholar
  7. 7.
    Alberti, M., Gavanelli, M., Lamma, E., Mello, P., Torroni, P.: An abductive interpretation for open societies. In: Cappelli, A., Turini, F. (eds.) AI*IA 2003. LNCS, vol. 2829, Springer, Heidelberg (2003)Google Scholar
  8. 8.
    Chesani, F., Lamma, E., Mello, P., Montali, M., Riguzzi, F., Storari, S.: Exploiting inductive logic programming techniques for declarative process mining. In: Jensen, K., van der Aalst, W.M.P. (eds.) Transactions on Petri Nets. LNCS, vol. 5460, pp. 278–295. Springer, Heidelberg (2009)Google Scholar
  9. 9.
    Chesani, F., Mello, P., Montali, M., Storari, S.: Towards a decserflow declarative semantics based on computational logic. Technical Report DEIS-LIA-07-002, DEIS, Bologna, Italy (2007)Google Scholar
  10. 10.
    Clark, K.L.: Negation as failure. In: Logic and Databases. Plenum Press, New York (1978)Google Scholar
  11. 11.
    De Raedt, L., Van Laer, W.: Inductive constraint logic. In: Zeugmann, T., Shinohara, T., Jantke, K.P. (eds.) ALT 1995. LNCS (LNAI), vol. 997, Springer, Heidelberg (1995)Google Scholar
  12. 12.
    Domingos, P., Kok, S., Lowd, D., Poon, H., Richardson, M., Singla, P.: Markov logic. In: De Raedt, L., Frasconi, P., Kersting, K., Muggleton, S.H. (eds.) Probabilistic Inductive Logic Programming. LNCS (LNAI), vol. 4911, pp. 92–117. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  13. 13.
    Georgakopoulos, D., Hornick, M.F., Sheth, A.P.: An overview of workflow management: From process modeling to workflow automation infrastructure. Distributed and Parallel Databases 3(2), 119–153 (1995)CrossRefGoogle Scholar
  14. 14.
    Greco, G., Guzzo, A., Pontieri, L., Saccà, D.: Discovering expressive process models by clustering log traces. IEEE Transactions on Knowledge and Data Engineering 18(8), 1010–1027 (2006)CrossRefGoogle Scholar
  15. 15.
    Lamma, E., Mello, P., Montali, M., Riguzzi, F., Storari, S.: Inducing declarative logic-based models from labeled traces. In: Alonso, G., Dadam, P., Rosemann, M. (eds.) BPM 2007. LNCS, vol. 4714, pp. 344–359. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  16. 16.
    Lamma, E., Mello, P., Riguzzi, F., Storari, S.: Applying inductive logic programming to process mining. In: Blockeel, H., Ramon, J., Shavlik, J., Tadepalli, P. (eds.) ILP 2007. LNCS (LNAI), vol. 4894, pp. 132–146. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  17. 17.
    Provost, F.J., Fawcett, T.: Robust classification for imprecise environments. Machine Learning 42(3), 203–231 (2001)zbMATHCrossRefGoogle Scholar
  18. 18.
    Raedt, L.D., Dehaspe, L.: Clausal discovery. Machine Learning 26(2-3), 99–146 (1997)zbMATHCrossRefGoogle Scholar
  19. 19.
    Richardson, M., Domingos, P.: Markov logic networks. Machine Learning 62(1-2), 107–136 (2006)CrossRefGoogle Scholar
  20. 20.
    Silva, R., Zhang, J., Shanahan, J.G.: Probabilistic workflow mining. In: Grossman, R., Bayardo, R.J., Bennett, K.P. (eds.) Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 275–284. ACM, New York (2005)CrossRefGoogle Scholar
  21. 21.
    Singla, P., Domingos, P.: Lifted first-order belief propagation. In: Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence, AAAI 2008, pp. 1094–1099. AAAI Press, Menlo Park (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Elena Bellodi
    • 1
  • Fabrizio Riguzzi
    • 1
  • Evelina Lamma
    • 1
  1. 1.ENDIFUniversità di FerraraFerraraItaly

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