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Scalable Conformance Checking of Business Processes

  • Daniel ReißnerEmail author
  • Raffaele Conforti
  • Marlon Dumas
  • Marcello La Rosa
  • Abel Armas-Cervantes
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10573)

Abstract

Given a process model representing the expected behavior of a business process and an event log recording its actual execution, the problem of business process conformance checking is that of detecting and describing the differences between the process model and the log. A desirable feature is to produce a minimal yet complete set of behavioral differences. Existing conformance checking techniques that achieve these properties do not scale up to real-life process models and logs. This paper presents an approach that addresses this shortcoming by exploiting automata-based techniques. A log is converted into a deterministic automaton in a lossless manner, the input process model is converted into another minimal automaton, and a minimal error-correcting synchronized product of both automata is calculated using an A* heuristic. The resulting automaton is used to extract alignments between traces of the model and traces of the log, or statements describing behavior observed in the log but not captured in the model. An evaluation on synthetic and real-life models and logs shows that the proposed approach outperforms a state-of-the-art method for complete conformance checking.

Keywords

Conformance checking Process mining Automata Behavioral alignment 

Notes

Acknowledgments

This research is partly funded by the Australian Research Council (grant DP150103356) and the Estonian Research Council (grant IUT20-55).

References

  1. 1.
    Adriansyah, A., Muñoz-Gama, J., Carmona, J., van Dongen, B.F., van der Aalst, W.M.P.: Measuring precision of modeled behavior. IseB 13(1), 37–67 (2015)CrossRefGoogle Scholar
  2. 2.
    Adriansyah, A., van Dongen, B.F., van der Aalst, W.M.P.: Conformance checking using cost-based fitness analysis. In: Proceeding of EDOC, pp. 55–64. IEEE (2011)Google Scholar
  3. 3.
    Adriansyah, A., van Dongen, B.F., van der Aalst, W.M.P.: Memory-efficient alignment of observed and modeled behavior. BPM Center Report (2013)Google Scholar
  4. 4.
    Alves de Medeiros, A.K.: Genetic Process Mining. PhD thesis, TU/e (2006)Google Scholar
  5. 5.
    Armas-Cervantes, A., Baldan, P., Dumas, M., García-Bañuelos, L.: Diagnosing behavioral differences between business process models: An approach based on event structures. Inf. Syst. 56, 304–325 (2016)CrossRefGoogle Scholar
  6. 6.
    Armas-Cervantes, A., Dumas, M., La Rosa, M.: Discovering local concurrency relations in business process event logs. eprint # 102438, QUT (2016)Google Scholar
  7. 7.
    Armas-Cervantes, A., La Rosa, M., Dumas Menjivar, M., García-Bañuelos, L., van Beest, N.R.: Interactive and incremental business process model repair. eprint # 106611, QUT (2017)Google Scholar
  8. 8.
    Augusto, A., Conforti, R., Dumas, M., La Rosa, M., Bruno, G.: Automated discovery of structured process models: discover structured vs. discover and structure. In: Comyn-Wattiau, I., Tanaka, K., Song, I.-Y., Yamamoto, S., Saeki, M. (eds.) ER 2016. LNCS, vol. 9974, pp. 313–329. Springer, Cham (2016). doi: 10.1007/978-3-319-46397-1_25 CrossRefGoogle Scholar
  9. 9.
    Conforti, R., La Rosa, M., ter Hofstede, A.H.M.: Filtering out infrequent behavior from business process event logs. IEEE TKDE 29(2), 300–314 (2016)Google Scholar
  10. 10.
    Curran, T., Keller, G.: SAP R/3 Business Blueprint: Understanding the Business Process Reference Model. Upper Saddle River (1997)Google Scholar
  11. 11.
    Daciuk, J., Mihov, S., Watson, B.W., Watson, R.E.: Incremental construction of minimal acyclic finite-state automata. Comput. Linguist. 26(1), 3–16 (2000)CrossRefzbMATHMathSciNetGoogle Scholar
  12. 12.
    de Leoni, M., Mannhardt, F.: Road traffic fine management process (2015)Google Scholar
  13. 13.
    de Medeiros, A.K.A., van der Aalst, W.M.P., Weijters, A.J.M.M.: Workflow mining: current status and future directions. In: Meersman, R., Tari, Z., Schmidt, D.C. (eds.) OTM 2003. LNCS, vol. 2888, pp. 389–406. Springer, Heidelberg (2003). doi: 10.1007/978-3-540-39964-3_25 CrossRefGoogle Scholar
  14. 14.
    Diller, A.: Z: An Introduction to Formal Methods. Wiley, New York (1990)zbMATHGoogle Scholar
  15. 15.
    García-Bañuelos, L., van Beest, N.R.T.P., Dumas, M., La Rosa, M.: Complete and interpretable conformance checking of business processes. IEEE Trans. Softw. Eng. (2017, to appear). doi: 10.1109/TSE.2017.2668418. IEEE Computer Society
  16. 16.
    Hart, P.E., Nilsson, N.J., Raphael, B.: A formal basis for the heuristic determination of minimum cost paths. IEEE TSSC 4(2), 100–107 (1968)Google Scholar
  17. 17.
    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). doi: 10.1007/978-3-642-38697-8_17 CrossRefGoogle Scholar
  18. 18.
    Leemans, S.J., Fahland, D., van der Aalst, W.M.: Scalable process discovery and conformance checking. Softw. Syst. Model. 16, 1–33 (2016)Google Scholar
  19. 19.
    Lipton, R.: The reachability problem requires exponential space. Research Report 62, Department of Computer Science, Yale University, New Haven, Connecticut (1976)Google Scholar
  20. 20.
    Mannhardt, F., de Leoni, M., Reijers, H.A., van der Aalst, W.M.P.: Balanced multi-perspective checking of process conformance. Computing 98, 407–437 (2016)CrossRefzbMATHMathSciNetGoogle Scholar
  21. 21.
    Mayr, E.W.: An algorithm for the general petri net reachability problem. SIAM J. Comput. 13(3), 441–460 (1984)CrossRefzbMATHMathSciNetGoogle Scholar
  22. 22.
    Muñoz-Gama, J., Carmona, J.: A fresh look at precision in process conformance. In: Hull, R., Mendling, J., Tai, S. (eds.) BPM 2010. LNCS, vol. 6336, pp. 211–226. Springer, Heidelberg (2010). doi: 10.1007/978-3-642-15618-2_16 CrossRefGoogle Scholar
  23. 23.
    Muñoz-Gama, J., Carmona, J., van der Aalst, W.M.P.: Single-entry single-exit decomposed conformance checking. Inf. Syst. 46, 102–122 (2014)CrossRefGoogle Scholar
  24. 24.
    Murata, T.: Petri nets: Properties, analysis and applications. Proc. IEEE 77(4), 541–580 (1989)CrossRefGoogle Scholar
  25. 25.
    Polyvyanyy, A., Van Der Aalst, W.M.P., Ter Hofstede, A.H.M., Wynn, M.T.: Impact-driven process model repair. ACM Trans. Softw. Eng. Methodol. (TOSEM) 25(4), 28 (2016)CrossRefGoogle Scholar
  26. 26.
    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
  27. 27.
    Song, W., Xia, X., Jacobsen, H.A., Zhang, P., Hu, H.: Efficient alignment between event logs and process models. IEEE Trans. Serv. Comput. 10(1), 136–149 (2017)CrossRefGoogle Scholar
  28. 28.
    Steeman, W.: Bpi challenge 2013, closed problems (2013)Google Scholar
  29. 29.
    van Dongen, B., Carmona, J., Chatain, T., Taymouri, F.: Aligning modeled and observed behavior: a compromise between computation complexity and quality. In: Dubois, E., Pohl, K. (eds.) CAiSE 2017. LNCS, vol. 10253, pp. 94–109. Springer, Cham (2017). doi: 10.1007/978-3-319-59536-8_7 Google Scholar
  30. 30.
    vanden Broucke, S., De Weerdt, J., Vanthienen, J., Baesens, B.: An improved process event log artificial negative event generator. Technical Report KBI_1216, KU Leuven (2012)Google Scholar
  31. 31.
    vanden Broucke, S.K.L.M., De Weerdt, J., Vanthienen, J., Baesens, B.: Determining process model precision and generalization with weighted artificial negative events. IEEE TKDE 26(8), 1877–1889 (2014)Google Scholar
  32. 32.
    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). doi: 10.1007/978-3-662-45563-0_20 Google Scholar
  33. 33.
    Verbeek, H.M.W., Buijs, J.C.A.M., Van Dongen, B.F., Van der Aalst, W.M.P.: Prom 6: The process mining toolkit. Proc. BPM Demonstr. Track 615, 34–39 (2010)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Daniel Reißner
    • 1
    Email author
  • Raffaele Conforti
    • 1
  • Marlon Dumas
    • 2
  • Marcello La Rosa
    • 1
  • Abel Armas-Cervantes
    • 1
  1. 1.Queensland University of TechnologyBrisbaneAustralia
  2. 2.University of TartuTartuEstonia

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