In Log and Model We Trust? A Generalized Conformance Checking Framework

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9850)


While models and event logs are readily available in modern organizations, their quality can seldom be trusted. Raw event recordings are often noisy, incomplete, and contain erroneous recordings. The quality of process models, both conceptual and data-driven, heavily depends on the inputs and parameters that shape these models, such as domain expertise of the modelers and the quality of execution data. The mentioned quality issues are specifically a challenge for conformance checking. Conformance checking is the process mining task that aims at coping with low model or log quality by comparing the model against the corresponding log, or vice versa. The prevalent assumption in the literature is that at least one of the two can be fully trusted. In this work, we propose a generalized conformance checking framework that caters for the common case, when one does neither fully trust the log nor the model. In our experiments we show that our proposed framework balances the trust in model and log as a generalization of state-of-the-art conformance checking techniques.


Process mining Conformance checking Model repair Log repair 



This work was partially supported by the European Union’s Seventh Framework Programme (FP7/2007-2013) grant 612052 (SERAMIS) and the German Research Foundation (DFG), grant WE 4891/1-1.


  1. 1.
    van der Aalst, W.M.P.: Process Mining: Discovery, Conformance Checking and Enhancement of Business Processes. Springer Science & Business Media, Berlin (2011)CrossRefzbMATHGoogle Scholar
  2. 2.
    van der Aalst, W.M.P., Adriansyah, A., van Dongen, B.F.: Replaying history on process models for conformance checking and performance analysis. WIREs. Data Min. Knowl. Discov. 2, 182–192 (2012)CrossRefGoogle Scholar
  3. 3.
    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
  4. 4.
    Buijs, J.C.A.M., van Dongen, B.F., van der Aalst, W.M.P.: A genetic algorithm for discovering process trees. In: Evolutionary Computation (CEC 2012), pp. 1–8. IEEE (2012)Google Scholar
  5. 5.
    Buijs, J.C.A.M., van Dongen, B.F., van der Aalst, W.M.P.: Quality dimensions in process discovery: the importance of fitness, precision, generalization and simplicity. Int. J. Coop. Inf. Syst. 23(01), 1440001 (2014)CrossRefGoogle Scholar
  6. 6.
    Buijs, J.C.A.M., La Rosa, M., Reijers, H.A., van Dongen, B.F., van der Aalst, W.M.P.: Improving business process models using observed behavior. In: Cudre-Mauroux, P., Ceravolo, P., Gašević, D. (eds.) SIMPDA 2012. LNBIP, vol. 162, pp. 44–59. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  7. 7.
    Domshlak, C., Mirkis, V.: Deterministic oversubscription planning as heuristic search: abstractions and reformulations. J. Artif. Intell. Res. (JAIR) 52, 97–169 (2015)MathSciNetzbMATHGoogle Scholar
  8. 8.
  9. 9.
    Fahland, D., van der Aalst, W.M.P.: Model repair — aligning process models to reality. Inf. Syst. 47, 220–243 (2015)CrossRefGoogle Scholar
  10. 10.
    Günther, C.W., van der Aalst, W.M.P.: Fuzzy mining – adaptive process simplification based on multi-perspective metrics. In: Alonso, G., Dadam, P., Rosemann, M. (eds.) BPM 2007. LNCS, vol. 4714, pp. 328–343. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  11. 11.
    Kunze, M., Weidlich, M., Weske, M.: Behavioral similarity – a proper metric. In: Rinderle-Ma, S., Toumani, F., Wolf, K. (eds.) BPM 2011. LNCS, vol. 6896, pp. 166–181. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  12. 12.
    Leemans, S.J.J., Fahland, D., van der Aalst, W.M.P.: Discovering block-structured process models from event logs - a constructive approach. In: Colom, J.-M., Desel, J. (eds.) PETRI NETS 2013. LNCS, vol. 7927, pp. 311–329. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  13. 13.
    Leemans, S.J.J., Fahland, D., van der Aalst, W.M.P.: Discovering block-structured process models from event logs containing infrequent behaviour. In: Lohmann, N., Song, M., Wohed, P. (eds.) BPM 2013. LNBIP, vol. 171, pp. 66–78. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  14. 14.
    Mannhardt, F., de Leoni, M., Reijers, H.A., van der Aalst, W.M.P.: Balanced multi-perspective checking of process conformance. Computing 98(4), 407–437 (2016)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Marquard, M., Shahzad, M., Slaats, T.: Web-based modelling and collaborative simulation of declarative processes. In: Motahari-Nezhad, H.R., Recker, J., Weidlich, M. (eds.) BPM 2015. LNCS, vol. 9253, pp. 209–225. Springer, Heidelberg (2015)CrossRefGoogle Scholar
  16. 16.
    Pawlik, M., Augsten, N.: Tree edit distance: robust and memory-efficient. Inf. Syst. 56, 157–173 (2016)CrossRefGoogle Scholar
  17. 17.
    Rogge-Solti, A., Kasneci, G.: Temporal anomaly detection in business processes. In: Sadiq, S., Soffer, P., Völzer, H. (eds.) BPM 2014. LNCS, vol. 8659, pp. 234–249. Springer, Heidelberg (2014)Google Scholar
  18. 18.
    Rogge-Solti, A., Mans, R.S., van der Aalst, W.M.P., Weske, M.: Improving documentation by repairing event logs. In: Grabis, J., Kirikova, M., Zdravkovic, J., Stirna, J. (eds.) PoEM 2013. LNBIP, vol. 165, pp. 129–144. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  19. 19.
    Rozinat, A., van der Aalst, W.M.P.: Conformance checking of processes based on monitoring real behavior. Inf. Syst. 33(1), 64–95 (2008)CrossRefGoogle Scholar
  20. 20.
    Senderovich, A., Weidlich, M., Yedidsion, L., Gal, A., Mandelbaum, A., Kadish, S., Bunnell, C.A.: Conformance checking and performance improvement in scheduled processes: a queueing-network perspective. Inf. Syst. (2016, forthcoming)Google Scholar
  21. 21.
    Wang, J., Song, S., Lin, X., Zhu, X., Pei, J.: Cleaning structured event logs: a graph repair approach. In: Data Engineering (ICDE 2015), pp. 30–41. IEEE (2015)Google Scholar
  22. 22.
    Weber, I., Farshchi, M., Mendling, J., Schneider, J.: Mining processes with multi-instantiation. In: 30th Annual ACM Symposium on Applied Computing, pp. 1231–1237 (2015)Google Scholar
  23. 23.
    Weijters, A., van der Aalst, W.M.P., De Medeiros, A.K.A.: Process mining with the heuristics miner-algorithm. Technical report, 166. Technische Universiteit Eindhoven (2006)Google Scholar
  24. 24.
    Whitley, D.: An overview of evolutionary algorithms: practical issues and common pitfalls. Inf. Softw. Technol. 43(14), 817–831 (2001)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Vienna University of Economics and BusinessViennaAustria
  2. 2.Technion–Israel Institute of TechnologyHaifaIsrael
  3. 3.Humboldt University zu BerlinBerlinGermany

Personalised recommendations