A Knowledge-Based Integrated Approach for Discovering and Repairing Declare Maps

  • Fabrizio M. Maggi
  • R. P. Jagadeesh Chandra Bose
  • Wil M. P. van der Aalst
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7908)


Process mining techniques can be used to discover process models from event data. Often the resulting models are complex due to the variability of the underlying process. Therefore, we aim at discovering declarative process models that can deal with such variability. However, for real-life event logs involving dozens of activities and hundreds or thousands of cases, there are often many potential constraints resulting in cluttered diagrams. Therefore, we propose various techniques to prune these models and remove constraints that are not interesting or implied by other constraints. Moreover, we show that domain knowledge (e.g., a reference model or grouping of activities) can be used to guide the discovery approach. The approach has been implemented in the process mining tool ProM and evaluated using an event log from a large Dutch hospital. Even in such highly variable environments, our approach can discover understandable declarative models.


Process Discovery Model Repair Linear Temporal Logic Declare 


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  1. 1.
    van der Aalst, W.M.P.: Process Mining: Discovery, Conformance and Enhancement of Business Processes. Springer (2011)Google Scholar
  2. 2.
    van der Aalst, W.M.P., Pesic, M., Schonenberg, H.: Declarative Workflows: Balancing Between Flexibility and Support. Computer Science - R&D, 99–113 (2009)Google Scholar
  3. 3.
    Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: VLDB 1994, pp. 487–499 (1994)Google Scholar
  4. 4.
    Case, M.L.: Online Algorithms to Mantain a Transitive Reduction, Department of EECS, University of California, Berkeley, CS 294-8 (2006)Google Scholar
  5. 5.
    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.) ToPNoC II. LNCS, vol. 5460, pp. 278–295. Springer, Heidelberg (2009)Google Scholar
  6. 6.
    Di Ciccio, C., Mecella, M.: Mining constraints for artful processes. In: Abramowicz, W., Kriksciuniene, D., Sakalauskas, V. (eds.) BIS 2012. LNBIP, vol. 117, pp. 11–23. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  7. 7.
    Goedertier, S., Martens, D., Vanthienen, J., Baesens, B.: Robust process discovery with artificial negative events. Journal of Machine Learning Research 10, 1305–1340 (2009)MathSciNetMATHGoogle Scholar
  8. 8.
    Kupferman, O., Vardi, M.Y.: Vacuity Detection in Temporal Model Checking. International Journal on Software Tools for Technology Transfer, 224–233 (2003)Google Scholar
  9. 9.
    Lichtenstein, O., Pnueli, A., Zuck, L.: The Glory of the Past. In: Parikh, R. (ed.) Logic of Programs. LNCS, vol. 193, pp. 196–218. Springer, Heidelberg (1985)CrossRefGoogle Scholar
  10. 10.
    Maggi, F.M., Bose, R.P.J.C., van der Aalst, W.M.P.: Efficient Discovery of Understandable Declarative Process Models from Event Logs. In: Ralyté, J., Franch, X., Brinkkemper, S., Wrycza, S. (eds.) CAiSE 2012. LNCS, vol. 7328, pp. 270–285. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  11. 11.
    Maggi, F.M., Mooij, A.J., van der Aalst, W.M.P.: User-Guided Discovery of Declarative Process Models. In: IEEE Symposium on Computational Intelligence and Data Mining, vol. 2725, pp. 192–199. IEEE Computer Society (2011)Google Scholar
  12. 12.
    Pichler, P., Weber, B., Zugal, S., Pinggera, J., Mendling, J., Reijers, H.A.: Imperative Versus Declarative Process Modeling Languages: An Empirical Investigation. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds.) BPM Workshops 2011, Part I. LNBIP, vol. 99, pp. 383–394. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  13. 13.
    Pnueli, A.: The Temporal Logic of Programs. In: Annual IEEE Symposium on Foundations of Computer Science, pp. 46–57 (1977)Google Scholar
  14. 14.
    Westergaard, M., Maggi, F.M.: Declare: A tool suite for declarative workflow modeling and enactment. In: Proceedings of the Demo Track of the Ninth Conference on Business Process Management 2011, Clermont-Ferrand, France, August 31. CEUR Workshop Proceedings, vol. 820. CEUR-WS.org (2011)Google Scholar
  15. 15.
    Zugal, S., Pinggera, J., Weber, B.: The impact of Testcases on the Maintainability of Declarative Process Models. In: Halpin, T., Nurcan, S., Krogstie, J., Soffer, P., Proper, E., Schmidt, R., Bider, I. (eds.) BPMDS 2011 and EMMSAD 2011. LNBIP, vol. 81, pp. 163–177. Springer, Heidelberg (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Fabrizio M. Maggi
    • 1
  • R. P. Jagadeesh Chandra Bose
    • 1
  • Wil M. P. van der Aalst
    • 1
  1. 1.Eindhoven University of TechnologyThe Netherlands

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