Discovering Target-Branched Declare Constraints

  • Claudio Di Ciccio
  • Fabrizio Maria Maggi
  • Jan Mendling
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8659)

Abstract

Process discovery is the task of generating models from event logs. Mining processes that operate in an environment of high variability is an ongoing research challenge because various algorithms tend to produce spaghetti-like models. This is particularly the case when procedural models are generated. A promising direction to tackle this challenge is the usage of declarative process modelling languages like Declare, which summarise complex behaviour in a compact set of behavioural constraints. However, Declare constraints with branching are expensive to be calculated.In addition, it is often the case that hundreds of branching Declare constraints are valid for the same log, thus making, again, the discovery results unreadable. In this paper, we address these problems from a theoretical angle. More specifically, we define the class of Target-Branched Declare constraints and investigate the formal properties it exhibits. Furthermore, we present a technique for the efficient discovery of compact Target-Branched Declare models. We discuss the merits of our work through an evaluation based on a prototypical implementation using both artificial and real-world event logs.

Keywords

Process Mining Discovery Declarative Processes 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules in Large Databases. In: VLDB, pp. 487–499. Morgan Kaufmann (1994)Google Scholar
  2. 2.
    Burattin, A., Maggi, F.M., van der Aalst, W.M.P., Sperduti, A.: Techniques for a Posteriori Analysis of Declarative Processes. In: EDOC, pp. 41–50 (2012)Google Scholar
  3. 3.
    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)CrossRefGoogle Scholar
  4. 4.
    Di Ciccio, C., Marrella, A., Russo, A.: Knowledge-Intensive Processes: An Overview of Contemporary Approaches. In: KiBP, pp. 33–47 (2012)Google Scholar
  5. 5.
    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
  6. 6.
    Di Ciccio, C., Mecella, M.: Mining Artful Processes from Knowledge Workers’ Emails. IEEE Internet Computing 17(5), 10–20 (2013)CrossRefGoogle Scholar
  7. 7.
    Di Ciccio, C., Mecella, M.: A Two-Step Fast Algorithm for the Automated Discovery of Declarative Workflows. In: CIDM, pp. 135–142 (2013)Google Scholar
  8. 8.
    Dumas, M., La Rosa, M., Mendling, J., Reijers, H.A.: Fundamentals of Business Process Management. Springer (2013)Google Scholar
  9. 9.
    Fahland, D., Lübke, D., Mendling, J., Reijers, H., Weber, B., Weidlich, M., Zugal, S.: Declarative versus Imperative Process Modeling Languages: The Issue of Understandability. In: Halpin, T., Krogstie, J., Nurcan, S., Proper, E., Schmidt, R., Soffer, P., Ukor, R. (eds.) Enterprise, Business-Process and Information Systems Modeling. LNBIP, vol. 29, pp. 353–366. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  10. 10.
    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
  11. 11.
    Maggi, F.M., Westergaard, M., Montali, M., van der Aalst, W.M.P.: Runtime Verification of LTL-Based Declarative Process Models. In: Khurshid, S., Sen, K. (eds.) RV 2011. LNCS, vol. 7186, pp. 131–146. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  12. 12.
    Maggi, F.M.: Declarative Process Mining with the Declare Component of ProM. In: BPM (Demos). CEUR, vol. 1021 (2013)Google Scholar
  13. 13.
    Maggi, F.M., Bose, R.P.J.C., van der Aalst, W.M.P.: A Knowledge-Based Integrated Approach for Discovering and Repairing Declare Maps. In: Salinesi, C., Norrie, M.C., Pastor, Ó. (eds.) CAiSE 2013. LNCS, vol. 7908, pp. 433–448. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  14. 14.
    Maggi, F.M., Dumas, M., García-Bañuelos, L., Montali, M.: Discovering Data-Aware Declarative Process Models from Event Logs. In: Daniel, F., Wang, J., Weber, B. (eds.) BPM 2013. LNCS, vol. 8094, pp. 81–96. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  15. 15.
    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
  16. 16.
    Maggi, F.M., Montali, M., Westergaard, M., van der Aalst, W.M.P.: Monitoring Business Constraints with Linear Temporal Logic: An Approach Based on Colored Automata. In: Rinderle-Ma, S., Toumani, F., Wolf, K. (eds.) BPM 2011. LNCS, vol. 6896, pp. 132–147. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  17. 17.
    Maggi, F.M., Mooij, A.J., van der Aalst, W.M.P.: User-Guided Discovery of Declarative Process Models. In: CIDM, pp. 192–199. IEEE (2011)Google Scholar
  18. 18.
    Mendling, J., Strembeck, M., Recker, J.: Factors of Process Model Comprehension - Findings from a Series of Experiments. Decision Support Systems 53(1), 195–206 (2012)CrossRefGoogle Scholar
  19. 19.
    Montali, M., Chesani, F., Maggi, F.M., Mello, P.: Towards Data-Aware Constraints in Declare. In: SAC, pp. 1391–1396 (2013)Google Scholar
  20. 20.
    Montali, M., Maggi, F.M., Chesani, F., Mello, P., van der Aalst, W.M.P.: Monitoring Business Constraints with the Event Calculus. ACM TIST 5(1), 17 (2013)Google Scholar
  21. 21.
    Montali, M., Pesic, M., van der Aalst, W.M.P.: Federico Chesani, Paola Mello, and Sergio Storari. Declarative Specification and Verification of Service Choreographies. ACM Transactions on the Web 4(1) (2010)Google Scholar
  22. 22.
    Reijers, H.A., Slaats, T., Stahl, C.: Declarative Modeling–An Academic Dream or the Future for BPM? In: Daniel, F., Wang, J., Weber, B. (eds.) BPM 2013. LNCS, vol. 8094, pp. 307–322. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  23. 23.
    Schunselaar, D.M.M., Maggi, F.M., Sidorova, N.: Patterns for a Log-Based Strengthening of Declarative Compliance Models. In: Derrick, J., Gnesi, S., Latella, D., Treharne, H. (eds.) IFM 2012. LNCS, vol. 7321, pp. 327–342. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  24. 24.
    Smirnov, S., Weidlich, M., Mendling, J.: Business Process Model Abstraction Based on Synthesis from Well-Structured Behavioral Profiles. Int. J. Cooperative Inf. Syst. 21(1), 55–83 (2012)CrossRefGoogle Scholar
  25. 25.
    van der Aalst, W.M.P., Weijters, T., Maruster, L.: Workflow Mining: Discovering Process Models from Event Logs. IEEE TKDE 16(9), 1128–1142 (2004)Google Scholar
  26. 26.
    van der Aalst, W.M.P., Pesic, M., Schonenberg, H.: Declarative Workflows: Balancing between Flexibility and Support. CSRD 23(2), 99–113 (2009)Google Scholar
  27. 27.
    van Dongen, B.F.: BPI Challenge 2012 (2012)Google Scholar
  28. 28.
    Weidlich, M., Polyvyanyy, A., Desai, N., Mendling, J., Weske, M.: Process Compliance Analysis Based on Behavioural Profiles. Inf. Syst. 36(7), 1009–1025 (2011)CrossRefGoogle Scholar
  29. 29.
    Westergaard, M., Maggi, F.M.: Looking into the Future: Using Timed Automata to Provide A Priori Advice about Timed Declarative Process Models. In: Meersman, R., et al. (eds.) OTM 2012, Part I. LNCS, vol. 7565, pp. 250–267. Springer, Heidelberg (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Claudio Di Ciccio
    • 1
  • Fabrizio Maria Maggi
    • 2
  • Jan Mendling
    • 1
  1. 1.Vienna University of Business and EconomicsAustria
  2. 2.University of TartuEstonia

Personalised recommendations