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Incremental Discovery of Process Models Using Trace Fragments

  • Conference paper
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Business Process Management (BPM 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14159))

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Abstract

Process discovery learns process models from event data and is a crucial discipline within process mining. Most existing approaches are fully automated, i.e., event data is provided, and a process model is returned. Thus, process analysts cannot interact and intervene besides parameter settings. In contrast, Incremental Process Discovery (IPD) enables users to actively participate in the discovery phase by gradually selecting process behavior to be incorporated into a process model. Further, most discovery approaches assume process executions, also termed traces, recorded in event data to be complete—complete traces span the actual process from start to completion. Incomplete traces are usually removed in the event data preparation as most discovery algorithms cannot handle them respectively treat them simply as full traces. This paper presents a novel IPD approach that can incorporate process behavior recorded in trace fragments, thus supporting incomplete data. Our experiments show promising results indicating that using trace fragments within IPD leads to high-quality process models.

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Notes

  1. 1.

    https://cortado.fit.fraunhofer.de (from version 1.10.0).

  2. 2.

    BPI Ch. 2020–Request for Payment (DOI: 10.4121/uuid:52fb97d4-4588-43c9-9d04-3604d4613b51)

    Road Traffic Fine Management (DOI: 10.4121/uuid:270fd440-1057-4fb9-89a9-b699b47990f)

    Receipt log (DOI: 10.4121/uuid:a07386a5-7be3-4367-9535-70bc9e77dbe6).

  3. 3.

    https://github.com/fit-daniel-schuster/trace-fragment-supporting-incremental-process-discovery.

References

  1. Adriansyah, A.: Aligning observed and modeled behavior. Ph.D. thesis, Eindhoven University of Technology (2014). https://doi.org/10.6100/IR770080

  2. Armas Cervantes, A., van Beest, N.R.T.P., La Rosa, M., Dumas, M., García-Bañuelos, L.: Interactive and incremental business process model repair. In: Panetto, H., et al. (eds.) OTM 2017. LNCS, vol. 10573, pp. 53–74. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69462-7_5

    Chapter  Google Scholar 

  3. Augusto, A., et al.: Automated discovery of process models from event logs: review and benchmark. IEEE TKDE 31(4), 686–705 (2019). https://doi.org/10.1109/TKDE.2018.2841877

    Article  Google Scholar 

  4. Beerepoot, I., et al.: The biggest business process management problems to solve before we die. Comput. Ind. 146, 103837 (2023). https://doi.org/10.1016/j.compind.2022.103837

    Article  Google Scholar 

  5. Bergenthum, R., Desel, J., Lorenz, R., Mauser, S.: Process mining based on regions of languages. In: Alonso, G., Dadam, P., Rosemann, M. (eds.) BPM 2007. LNCS, vol. 4714, pp. 375–383. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-75183-0_27

    Chapter  Google Scholar 

  6. Bernard, G., Andritsos, P.: Truncated trace classifier. Removal of incomplete traces from event logs. In: Nurcan, S., Reinhartz-Berger, I., Soffer, P., Zdravkovic, J. (eds.) BPMDS/EMMSAD -2020. LNBIP, vol. 387, pp. 150–165. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-49418-6_10

    Chapter  Google Scholar 

  7. Bezerra, F., Wainer, J., van der Aalst, W.M.P.: Anomaly detection using process mining. In: Halpin, T., et al. (eds.) BPMDS/EMMSAD -2009. LNBIP, vol. 29, pp. 149–161. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-01862-6_13

    Chapter  Google Scholar 

  8. Buijs, J., van Dongen, B.F., van der Aalst, W.M.P.: A genetic algorithm for discovering process trees. In: Congress on Evolutionary Computation. IEEE (2012)

    Google Scholar 

  9. Carmona, J., van Dongen, B.F., Solti, A., Weidlich, M.: Conformance Checking. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-99414-7

    Book  Google Scholar 

  10. Dixit, P.M., Buijs, J.C.A.M., van der Aalst, W.M.P.: Prodigy: human-in-the-loop process discovery. In: 12th International Conference on Research Challenges in Information Science (RCIS). IEEE (2018). https://doi.org/10.1109/RCIS.2018.8406657

  11. Fahland, D., van der Aalst, W.M.P.: Repairing process models to reflect reality. In: Barros, A., Gal, A., Kindler, E. (eds.) BPM 2012. LNCS, vol. 7481, pp. 229–245. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32885-5_19

    Chapter  Google Scholar 

  12. Greco, G., Guzzo, A., Lupia, F., Pontieri, L.: Process discovery under precedence constraints. ACM Trans. Knowl. Discov. Data 9(4), 1–39 (2015)

    Article  Google Scholar 

  13. Leemans, S.J.J.: Robust Process Mining with Guarantees. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-96655-3

    Book  Google Scholar 

  14. 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. 25(4), 1–60 (2017). https://doi.org/10.1145/2980764

    Article  Google Scholar 

  15. Reichert, M., Weber, B.: Enabling Flexibility in Process-Aware Information Systems. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-30409-5

    Book  MATH  Google Scholar 

  16. Schuster, D., Föcking, N., van Zelst, S.J., van der Aalst, W.M.P.: Conformance checking for trace fragments using infix and postfix alignments. In: Sellami, M., Ceravolo, P., Reijers, H.A., Gaaloul, W., Panetto, H. (eds.) CoopIS 2022. LNCS, vol. 13591, pp. 299–310. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-17834-4_18

  17. Schuster, D., van Zelst, S.J., van der Aalst, W.M.P.: Incremental discovery of hierarchical process models. In: Dalpiaz, F., Zdravkovic, J., Loucopoulos, P. (eds.) RCIS 2020. LNBIP, vol. 385, pp. 417–433. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50316-1_25

    Chapter  Google Scholar 

  18. Schuster, D., van Zelst, S.J., van der Aalst, W.M.P.: Freezing sub-models during incremental process discovery. In: Ghose, A., Horkoff, J., Silva Souza, V.E., Parsons, J., Evermann, J. (eds.) ER 2021. LNCS, vol. 13011, pp. 14–24. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-89022-3_2

    Chapter  Google Scholar 

  19. Schuster, D., van Zelst, S.J., van der Aalst, W.M.P.: Utilizing domain knowledge in data-driven process discovery: a literature review. Comput. Ind. 137, 103612 (2022). https://doi.org/10.1016/j.compind.2022.103612

    Article  Google Scholar 

  20. Schuster, D., van Zelst, S.J., van der Aalst, W.M.P.: Cortado: a dedicated process mining tool for interactive process discovery. SoftwareX 22, 101373 (2023). https://doi.org/10.1016/j.softx.2023.101373

    Article  Google Scholar 

  21. Solé, M., Carmona, J.: Incremental process discovery. In: Jensen, K., Donatelli, S., Kleijn, J. (eds.) Transactions on Petri Nets and Other Models of Concurrency V. LNCS, vol. 6900, pp. 221–242. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-29072-5_10

    Chapter  Google Scholar 

  22. van Dongen, B.F., Alves de Medeiros, A.K., Wen, L.: Process mining: overview and outlook of petri net discovery algorithms. In: Jensen, K., van der Aalst, W.M.P. (eds.) Transactions on Petri Nets and Other Models of Concurrency II. LNCS, vol. 5460, pp. 225–242. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-00899-3_13

    Chapter  Google Scholar 

  23. de Weerdt, J., de Backer, M., Vanthienen, J., Baesens, B.: A multi-dimensional quality assessment of state-of-the-art process discovery algorithms using real-life event logs. Inf. Syst. 37(7), 654–676 (2012). https://doi.org/10.1016/j.is.2012.02.004

    Article  Google Scholar 

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Authors and Affiliations

  1. Fraunhofer Institute for Applied Information Technology FIT, Sankt Augustin, Germany

    Daniel Schuster, Niklas Föcking, Sebastiaan J. van Zelst & Wil M. P. van der Aalst

  2. RWTH Aachen University, Aachen, Germany

    Daniel Schuster, Sebastiaan J. van Zelst & Wil M. P. van der Aalst

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  1. Daniel Schuster
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  2. Niklas Föcking
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  3. Sebastiaan J. van Zelst
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  4. Wil M. P. van der Aalst
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Correspondence to Daniel Schuster .

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Editors and Affiliations

  1. University of Trento, Trento, Italy

    Chiara Di Francescomarino

  2. Technical University of Denmark, Kgs. Lyngby, Denmark

    Andrea Burattin

  3. TU Dortmund University, Dortmund, Germany

    Christian Janiesch

  4. The University of Queensland, Brisbane, QLD, Australia

    Shazia Sadiq

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Schuster, D., Föcking, N., van Zelst, S.J., van der Aalst, W.M.P. (2023). Incremental Discovery of Process Models Using Trace Fragments. In: Di Francescomarino, C., Burattin, A., Janiesch, C., Sadiq, S. (eds) Business Process Management. BPM 2023. Lecture Notes in Computer Science, vol 14159. Springer, Cham. https://doi.org/10.1007/978-3-031-41620-0_4

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  • DOI: https://doi.org/10.1007/978-3-031-41620-0_4

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  • Online ISBN: 978-3-031-41620-0

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