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Improving Merging Conditions for Recomposing Conformance Checking

  • Wai Lam Jonathan LeeEmail author
  • Jorge Munoz-Gama
  • H. M. W. Verbeek
  • Wil M. P. van der Aalst
  • Marcos Sepúlveda
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 342)

Abstract

Efficient conformance checking is a hot topic in the field of process mining. Much of the recent work focused on improving the scalability of alignment-based approaches to support the larger and more complex processes. This is needed because process mining is increasingly applied in areas where models and logs are “big”. Decomposition techniques are able to achieve significant performance gains by breaking down a conformance problem into smaller ones. Moreover, recent work showed that the alignment problem can be resolved in an iterative manner by alternating between aligning a set of decomposed sub-components before merging the computed sub-alignments and recomposing sub-components to fix merging issues. Despite experimental results showing the gain of applying recomposition in large scenarios, there is still a need for improving the merging step, where log traces can take numerous recomposition steps before reaching the required merging condition. This paper contributes by defining and structuring the recomposition step, and proposes strategies with significant performance improvement on synthetic and real-life datasets over both the state-of-the-art decomposed and monolithic approaches.

Keywords

Recomposition Conformance checking Process mining 

Notes

Acknowledgments

This work is partially supported by CONICYT-PCHA/ Doctorado Nacional/2017-21170612, FONDECYT Iniciación 11170092, CONICYT Apoyo a la Formación de Redes Internacionales Para Investigadores en Etapa Inicial REDI170136, the Vicerrectoría de Investigación de la Pontificia Universidad Católica de Chile/Concurso Estadías y Pasantías Breves 2016, and the Departamento de Ciencias de la Computación UC/Fond-DCC-2017-0001. The authors would like to thank Alfredo Bolt for his comments on the data generation details.

References

  1. 1.
    van der Aalst, W.M.P.: Decomposing Petri nets for process mining: a generic approach. Distrib. Parallel Databases 31(4), 471–507 (2013)CrossRefGoogle Scholar
  2. 2.
    van der Aalst, W.M.P.: Process Mining - Data Science in Action. Springer, Heidelberg (2016).  https://doi.org/10.1007/978-3-662-49851-4CrossRefGoogle Scholar
  3. 3.
    van der Aalst, W.M.P., Bolt, A., van Zelst, S.J.: RapidProM: mine your processes and not just your data. CoRR abs/1703.03740 (2017)Google Scholar
  4. 4.
    Adriansyah, A.: Aligning Observed and Modeled Behavior. Ph.D. thesis, Technische Universiteit Eindhoven (2014)Google Scholar
  5. 5.
    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).  https://doi.org/10.1007/978-3-319-59536-8_7CrossRefGoogle Scholar
  6. 6.
    Dumas, M., Rosa, M.L., Mendling, J., Reijers, H.A.: Fundamentals of Business Process Management. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-33143-5CrossRefGoogle Scholar
  7. 7.
    García-Bañuelos, L., van Beest, N., Dumas, M., Rosa, M.L., Mertens, W.: Complete and interpretable conformance checking of business processes. IEEE Trans. Softw. Eng. 44(3), 262–290 (2018).  https://doi.org/10.1109/TSE.2017.2668418CrossRefGoogle Scholar
  8. 8.
    Jouck, T., Depaire, B.: PTandLogGenerator: a generator for artificial event data. In: BPM (Demos). CEUR Workshop Proceedings, vol. 1789, pp. 23–27. CEUR-WS.org (2016)Google Scholar
  9. 9.
    Kunze, M., Luebbe, A., Weidlich, M., Weske, M.: Towards understanding process modeling – the case of the BPM academic initiative. In: Dijkman, R., Hofstetter, J., Koehler, J. (eds.) BPMN 2011. LNBIP, vol. 95, pp. 44–58. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-25160-3_4CrossRefGoogle Scholar
  10. 10.
    Lee, W.L.J., Verbeek, H.M.W., Munoz-Gama, J., van der Aalst, W.M.P., Sepúlveda, M.: Replay using recomposition: alignment-based conformance checking in the large. In: Proceedings of the BPM Demo Track and BPM Dissertation Award, Barcelona, Spain, 13 September 2017. CEUR Workshop Proceedings, vol. 1920. CEUR-WS.org (2017)Google Scholar
  11. 11.
    Lee, W.L.J., Verbeek, H., Munoz-Gama, J., van der Aalst, W.M.P., Sepúlveda, M.: Recomposing Conformance: Closing the Circle on Decomposed Alignment-Based Conformance Checking in Process Mining (2017, under review). processmininguc.com/publications
  12. 12.
    Munoz-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
  13. 13.
    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
  14. 14.
    Taymouri, F., Carmona, J.: A recursive paradigm for aligning observed behavior of large structured process models. In: La Rosa, M., Loos, P., Pastor, O. (eds.) BPM 2016. LNCS, vol. 9850, pp. 197–214. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-45348-4_12CrossRefGoogle Scholar
  15. 15.
    Taymouri, F., Carmona, J.: Model and event log reductions to boost the computation of alignments. In: Ceravolo, P., Guetl, C., Rinderle-Ma, S. (eds.) SIMPDA 2016. LNBIP, vol. 307, pp. 1–21. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-74161-1_1CrossRefGoogle Scholar
  16. 16.
    van Dongen, B.F., Borchert, F.: BPI Challenge 2018 (2018)Google Scholar
  17. 17.
    Verbeek, H.M.W.: Decomposed replay using hiding and reduction as abstraction. In: Koutny, M., Kleijn, J., Penczek, W. (eds.) Transactions on Petri Nets and Other Models of Concurrency (ToPNoC) XII. LNCS, vol. 10470, pp. 166–186. Springer, Heidelberg (2017).  https://doi.org/10.1007/978-3-662-55862-1_8CrossRefGoogle Scholar
  18. 18.
    Verbeek, H.M.W., van der Aalst, W.M.P.: Merging alignments for decomposed replay. In: Kordon, F., Moldt, D. (eds.) PETRI NETS 2016. LNCS, vol. 9698, pp. 219–239. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-39086-4_14CrossRefGoogle Scholar
  19. 19.
    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

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Wai Lam Jonathan Lee
    • 1
    Email author
  • Jorge Munoz-Gama
    • 1
  • H. M. W. Verbeek
    • 2
  • Wil M. P. van der Aalst
    • 3
  • Marcos Sepúlveda
    • 1
  1. 1.Pontificia Universidad Católica de ChileSantiagoChile
  2. 2.Eindhoven University of TechnologyEindhovenThe Netherlands
  3. 3.RWTH Aachen UniversityAachenGermany

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