A Framework for Online Conformance Checking

  • Andrea BurattinEmail author
  • Josep Carmona
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 308)


Conformance checking – a branch of process mining – focuses on establishing to what extent actual executions of a process are in line with the expected behavior of a reference model. Current conformance checking techniques only allow for a-posteriori analysis: the amount of (non-)conformant behavior is quantified after the completion of the process instance. In this paper we propose a framework for online conformance checking: not only do we quantify (non-)conformant behavior as the execution is running, we also restrict the computation to constant time complexity per event analyzed, thus enabling the online analysis of a stream of events. The framework is instantiated with ideas coming from the theory of regions, and state similarity. An implementation is available in ProM and promising results have been obtained.


Online process mining Conformance checking Event stream 



We would like to thank Jorge Munoz-Gama for discussing early stage ideas of the approach. This work was partially funded by the Spanish Ministry for Economy and Competitiveness (MINECO) and the EU (FEDER funds) under grant COMMAS (TIN2013-46181-C2-1-R).


  1. 1.
    van der Aalst, W.M.: Process Mining: Discovery Conformance and Enhancement of Business Processes. Springer, Heidelberg (2011). CrossRefzbMATHGoogle Scholar
  2. 2.
    van der Aalst, W.M., Adriansyah, A., van Dongen, B.: Replaying history on process models for conformance checking and performance analysis. Wiley Interdisc. Rev. Data Min. Knowl. Discov. 2(2), 182–192 (2012)CrossRefGoogle Scholar
  3. 3.
    Adriansyah, A.: Aligning observed and modeled behavior. Ph. D. thesis, Technische Universiteit Eindhoven (2014)Google Scholar
  4. 4.
    Aggarwal, C.C.: Data Streams: Models and Algorithms, Advances in Database Systems, vol. 31. Springer, Boston, MA (2007). CrossRefGoogle Scholar
  5. 5.
    Bifet, A., Holmes, G., Kirkby, R., Pfahringer, B.: MOA: massive online analysis learning examples. J. Mach. Learn. Res. 11, 1601–1604 (2010)Google Scholar
  6. 6.
    vanden Broucke, S.K.L.M., Munoz-Gama, J., Carmona, J., Baesens, B., Vanthienen, J.: Event-based real-time decomposed conformance analysis. In: Meersman, R., Panetto, H., Dillon, T., Missikoff, M., Liu, L., Pastor, O., Cuzzocrea, A., Sellis, T. (eds.) OTM 2014. LNCS, vol. 8841, pp. 345–363. Springer, Heidelberg (2014). Google Scholar
  7. 7.
    Burattin, A.: Process Mining Techniques in Business Environments: Theoretical Aspects, Algorithms, Techniques and Open Challenges in Process Mining. LNBIP, vol. 207. Springer, Cham (2015). Google Scholar
  8. 8.
    Burattin, A.: PLG2 : Multiperspective process randomization with online and offline simulations. In: Proceedings of the BPM Demo Track. (2016)Google Scholar
  9. 9.
    Burattin, A.: Online conformance checking for petri nets and event streams. In: Online Proceedings of BPM Demo Track. (2017)Google Scholar
  10. 10.
    Burattin, A., Cimitile, M., Maggi, F.M., Sperduti, A.: Online discovery of declarative process models from event streams. IEEE TSC 8(6), 833–846 (2015)Google Scholar
  11. 11.
    Burattin, A., Maggi, F.M., Cimitile, M.: Lights, camera, action! business process movies for online process discovery. In: Proceedings of TAProViz (2014)Google Scholar
  12. 12.
    Burattin, A., Sperduti, A., van der Aalst, W.M.: Control-flow discovery from event streams. In: Proceedings of IEEE CEC, pp. 2420–2427. IEEE (2014)Google Scholar
  13. 13.
    Gaber, M.M., Zaslavsky, A., Krishnaswamy, S.: Mining data streams: a review. ACM Sigmod Rec. 34(2), 18–26 (2005)CrossRefzbMATHGoogle Scholar
  14. 14.
    Gama, J.: Knowledge Discovery from Data Streams. Chapman & Hall/CRC, Boca Raton (2010)CrossRefzbMATHGoogle Scholar
  15. 15.
    Golab, L., Özsu, M.T.: Issues in data stream management. ACM SIGMOD Rec. 32(2), 5–14 (2003)CrossRefGoogle Scholar
  16. 16.
    Leemans, S.J.J., Fahland, D., van der Aalst, W.M.P.: Scalable process discovery and conformance checking. Software & Systems Modeling, pp. 1–33. Springer, Heidelberg (2016). Google Scholar
  17. 17.
    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.) OTM 2013. LNCS, vol. 8185, pp. 94–111. Springer, Heidelberg (2013). CrossRefGoogle Scholar
  18. 18.
    Maggi, F.M., Montali, M., van der Aalst, W.M.: An operational decision support framework for monitoring business constraints. In: Proceedings of FASE (2012)Google Scholar
  19. 19.
    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
  20. 20.
    Munoz-Gama, J.:: Conformance Checking and Diagnosis in Process Mining - Comparing Observed and Modeled Processes. LNBIP. Springer, Cham (2016). zbMATHGoogle Scholar
  21. 21.
    Munoz-Gama, J., Carmona, J., van der Aalst, W.M.P.: Conformance checking in the large: partitioning and topology. In: Daniel, F., Wang, J., Weber, B. (eds.) BPM 2013. LNCS, vol. 8094, pp. 130–145. Springer, Heidelberg (2013). CrossRefGoogle Scholar
  22. 22.
    Murata, T.: Petri nets: properties, analysis and applications. Proc. IEEE 77(4), 541–580 (1989)CrossRefGoogle Scholar
  23. 23.
    Rozinat, A., van der Aalst, W.M.: Conformance checking of processes based on monitoring real behavior. Inf. Syst. 33(1), 64–95 (2008)CrossRefGoogle Scholar
  24. 24.
    van Zelst, S.J., van Dongen, B.F., van der Aalst, W.M.P.: Event stream-based process discovery using abstract representations. Knowl. Inf. Syst. 53, 1–29 (2017)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Technical University of DenmarkKongens LyngbyDenmark
  2. 2.University of InnsbruckInnsbruckAustria
  3. 3.Universitat Politècnica de CatalunyaBarcelonaSpain

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