Exploiting Inductive Logic Programming Techniques for Declarative Process Mining

  • Federico Chesani
  • Evelina Lamma
  • Paola Mello
  • Marco Montali
  • Fabrizio Riguzzi
  • Sergio Storari
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5460)


In the last few years, there has been a growing interest in the adoption of declarative paradigms for modeling and verifying process models. These paradigms provide an abstract and human understandable way of specifying constraints that must hold among activities executions rather than focusing on a specific procedural solution. Mining such declarative descriptions is still an open challenge. In this paper, we present a logic-based approach for tackling this problem. It relies on Inductive Logic Programming techniques and, in particular, on a modified version of the Inductive Constraint Logic algorithm. We investigate how, by properly tuning the learning algorithm, the approach can be adopted to mine models expressed in the ConDec notation, a graphical language for the declarative specification of business processes. Then, we sketch how such a mining framework has been concretely implemented as a ProM plug-in called DecMiner. We finally discuss the effectiveness of the approach by means of an example which shows the ability of the language to model concurrent activities and of DecMiner to learn such a model.


Business Process Integrity Constraint Inductive Logic Programming Execution Trace Constraint Logic Programming 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    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)Google Scholar
  2. 2.
    Alberti, M., Chesani, F., Gavanelli, M., Lamma, E., Mello, P., Torroni, P.: Verifiable agent interaction in abductive logic programming: the SCIFF framework. ACM T. Comput. Logic 9(4) (2008)Google Scholar
  3. 3.
    Chesani, F., Mello, P., Montali, M., Riguzzi, F., Sebastianis, M., Storari, S.: Checking compliance of execution traces to business rules. In: Ardagna, D., et al. (eds.) BPM 2008 Workshops. LNBIP, vol. 17, pp. 134–145. Springer, Heidelberg (2009)Google Scholar
  4. 4.
    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
  5. 5.
    Clark, K.L.: Negation as failure. In: Logic and Databases. Plenum Press (1978)Google Scholar
  6. 6.
    De Raedt, L., Van Laer, W.: Inductive constraint logic. In: Zeugmann, T., Shinohara, T., Jantke, K.P. (eds.) ALT 1995. LNCS (LNAI), vol. 997, pp. 80–94. Springer, Heidelberg (1995)CrossRefGoogle Scholar
  7. 7.
    Desel, J., Erwin, T.: Hybrid specifications: looking at workflows from a run-time perspective. Int. J. Computer System Science & Engineering 15(5), 291–302 (2000)Google Scholar
  8. 8.
    Bergenthum, R., Desel, J., Mauser, S., Lorenz, R.: Construction of process models from example runs. In: Jensen, K., van der Aalst, W. (eds.) ToPNoC II. LNCS, vol. 5460, pp. 243–259. Springer, Heidelberg (2009)Google Scholar
  9. 9.
    Ferreira, H.M., Ferreira, D.R.: An integrated life cycle for workflow management based on learning and planning. Int. J. Cooperative Inf. Syst. 15(4), 485–505 (2006)CrossRefGoogle Scholar
  10. 10.
    Goedertier, S.: Declarative techniques for modeling and mining business processes. PhD thesis, Katholieke Universiteit Leuven, Faculteit Economie en Bedrijfswetenschappen (2008)Google Scholar
  11. 11.
    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
  12. 12.
    Jaffar, J., Maher, M.J.: Constraint logic programming: a survey. J. Logic Program. 19(20), 503–582 (1994)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    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
  14. 14.
    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, vol. 4894, pp. 132–146. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  15. 15.
    Muggleton, S., De Raedt, L.: Inductive logic programming: Theory and methods. J. Logic Program. 19(20), 629–679 (1994)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Pesic, M.: Constraint-Based Workflow Management Systems. PhD thesis, Technische Universiteit Eindhoven, Department of Technology Management (2008)Google Scholar
  17. 17.
    Pesic, M., Schonenberg, H., van der Aalst, W.M.P.: Declare: Full support for loosely-structured processes. In: 11th IEEE International Enterprise Distributed Object Computing Conference, pp. 287–300. IEEE Computer Society, Los Alamitos (2007)Google Scholar
  18. 18.
    Pesic, M., van der Aalst, W.M.P.: 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)CrossRefGoogle Scholar
  19. 19.
    Robinson, J.A.: A machine-oriented logic based on the resolution principle. J. ACM 12(1), 23–41 (1965)MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Truong, H.L., Dustdar, S.: Online interaction analysis framework for ad-hoc collaborative processes in SOA-based environments. In: Jensen, K., van der Aalst, W. (eds.) ToPNoC II. LNCS, vol. 5460, pp. 260–277. Springer, Heidelberg (2009)Google Scholar
  21. 21.
    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
  22. 22.
    van Dongen, B., de Medeiros, A.K.A., Wen, L.: Process mining: Overview and Outlook of Petri net discovery algorithms. In: Jensen, K., van der Aalst, W. (eds.) ToPNoC II. LNCS, vol. 5460, pp. 225–242. Springer, Heidelberg (2009)Google Scholar
  23. 23.
    van Dongen, B.F., de Medeiros, A.K.A., Verbeek, H.M.W., Weijters, A.J.M.M., van der Aalst, W.M.P.: The ProM framework: A new era in process mining tool support. In: Ciardo, G., Darondeau, P. (eds.) ICATPN 2005. LNCS, vol. 3536, pp. 444–454. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  24. 24.
    van Dongen, B.F., van der Aalst, W.M.P.: Multi-phase process mining: Building instance graphs. In: Atzeni, P., Chu, W., Lu, H., Zhou, S., Ling, T.-W. (eds.) ER 2004. LNCS, vol. 3288, pp. 362–376. Springer, Heidelberg (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Federico Chesani
    • 1
  • Evelina Lamma
    • 2
  • Paola Mello
    • 1
  • Marco Montali
    • 1
  • Fabrizio Riguzzi
    • 2
  • Sergio Storari
    • 2
  1. 1.DEISUniversità di BolognaBolognaItaly
  2. 2.ENDIFUniversità di FerraraFerraraItaly

Personalised recommendations