Log-Based Understanding of Business Processes through Temporal Logic Query Checking

  • Margus Räim
  • Claudio Di Ciccio
  • Fabrizio Maria Maggi
  • Massimo Mecella
  • Jan Mendling
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8841)


Process mining is a discipline that aims at discovering, monitoring and improving real-life processes by extracting knowledge from event logs. Process discovery and conformance checking are the two main process mining tasks. Process discovery techniques can be used to learn a process model from example traces in an event log, whereas the goal of conformance checking is to compare the observed behavior in the event log with the modeled behavior. In this paper, we propose an approach based on temporal logic query checking, which is in the middle between process discovery and conformance checking. It can be used to discover those LTL-based business rules that are valid in the log, by checking against the log a (user-defined) class of rules. The proposed approach is not limited to provide a boolean answer about the validity of a business rule in the log, but it rather provides valuable diagnostics in terms of traces in which the rule is satisfied (witnesses) and traces in which the rule is violated (counterexamples). We have implemented our approach as a proof of concept and conducted a wide experimentation using both synthetic and real-life logs.


Process Discovery Business Rules Linear Temporal Logic Temporal Logic Query Checking 


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  1. 1.
    3TU Data Center. BPI Challenge, Event Log (2011), doi:10.4121/uuid:d9769f3d-0ab0-4fb8-803b-0d1120ffcf54Google Scholar
  2. 2.
    Bruns, G., Godefroid, P.: Temporal logic query checking. In: LICS, pp. 409–417. IEEE Computer Society (2001)Google Scholar
  3. 3.
    Chan, W.: Temporal-logic queries. In: Emerson, E.A., Sistla, A.P. (eds.) CAV 2000. LNCS, vol. 1855, pp. 450–463. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  4. 4.
    Chechik, M., Devereux, B., Easterbrook, S.M., Gurfinkel, A.: Multi-valued symbolic model-checking. ACM Trans. Softw. Eng. Methodol. 12(4), 371–408 (2003)CrossRefGoogle Scholar
  5. 5.
    Chechik, M., Easterbrook, S.M., Petrovykh, V.: Model-checking over multi-valued logics. In: Oliveira, J.N., Zave, P. (eds.) FME 2001. LNCS, vol. 2021, pp. 72–98. Springer, Heidelberg (2001)Google Scholar
  6. 6.
    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
  7. 7.
    Clarke, E.M., Emerson, E.A., Sistla, A.P.: Automatic verification of finite-state concurrent systems using temporal logic specifications. ACM Trans. Program. Lang. Syst. 8(2), 244–263 (1986)CrossRefzbMATHGoogle Scholar
  8. 8.
    Clarke, E.M., Grumberg, O., Peled, D.: Model checking. MIT Press (2001)Google Scholar
  9. 9.
    De Giacomo, G., De Masellis, R., Montali, M.: Reasoning on LTL on finite traces: Insensitivity to infiniteness. In: AAAI (2014)Google Scholar
  10. 10.
    De Giacomo, G., Vardi, M.Y.: Linear temporal logic and linear dynamic logic on finite traces. In: IJCAI (2013)Google Scholar
  11. 11.
    Deutch, D., Milo, T.: A structural/temporal query language for business processes. J. Comput. Syst. Sci. 78(2), 583–609 (2012)CrossRefzbMATHMathSciNetGoogle Scholar
  12. 12.
    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
  13. 13.
    Di Ciccio, C., Mecella, M.: Studies on the discovery of declarative control flows from error-prone data. In: SIMPDA, pp. 31–45 (2013)Google Scholar
  14. 14.
    Di Ciccio, C., Mecella, M.: A two-step fast algorithm for the automated discovery of declarative workflows. In: CIDM, pp. 135–142. IEEE (2013)Google Scholar
  15. 15.
    Gurfinkel, A., Chechik, M., Devereux, B.: Temporal logic query checking: A tool for model exploration. IEEE TSE 29(10), 898–914 (2003)Google Scholar
  16. 16.
    Hildebrandt, T.T., Mukkamala, R.R., Slaats, T., Zanitti, F.: Contracts for cross-organizational workflows as timed dynamic condition response graphs. J. Log. Algebr. Program. 82(5-7), 164–185 (2013)CrossRefzbMATHMathSciNetGoogle Scholar
  17. 17.
    Kupferman, O., Vardi, M.Y., Wolper, P.: An automata-theoretic approach to branching-time model checking. J. ACM 47(2), 312–360 (2000)CrossRefzbMATHMathSciNetGoogle Scholar
  18. 18.
    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
  19. 19.
    Maggi, F.M., Burattin, A., Cimitile, M., Sperduti, A.: Online process discovery to detect concept drifts in LTL-based declarative process models. In: Meersman, R., Panetto, H., Dillon, T., Eder, J., Bellahsene, Z., Ritter, N., De Leenheer, P., Dou, D. (eds.) ODBASE 2013. LNCS, vol. 8185, pp. 94–111. Springer, Heidelberg (2013)Google Scholar
  20. 20.
    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
  21. 21.
    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
  22. 22.
    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
  23. 23.
    Pesic, M., Schonenberg, H., van der Aalst, W.M.P.: Declare: Full support for loosely-structured processes. In: EDOC, pp. 287–300. IEEE (2007)Google Scholar
  24. 24.
    Pnueli, A.: The temporal logic of programs. In: FSTTCS, pp. 46–57. IEEE (1977)Google Scholar
  25. 25.
    van Dongen, B.F., de Medeiros, A.K.A., Verbeek, H.M.W., Weijters, A.J.M.M.T., 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
  26. 26.
    Weidlich, M., Mendling, J., Weske, M.: Efficient consistency measurement based on behavioral profiles of process models. IEEE Trans. Software Eng. 37(3), 410–429 (2011)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Margus Räim
    • 1
  • Claudio Di Ciccio
    • 2
  • Fabrizio Maria Maggi
    • 1
  • Massimo Mecella
    • 3
  • Jan Mendling
    • 2
  1. 1.University of TartuEstonia
  2. 2.Vienna University of Economics and BusinessAustria
  3. 3.Sapienza Università di RomaItaly

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