Abstract
Process mining is based on event logs. Traditionally, an event log is a sequence of events. Yet, there is a growing amount of work in the literature that suggests we should extend the notion of an event log and use partially ordered logs as a basis for process mining. Thus, the need for algorithms able to handle these partially ordered logs will grow in the upcoming years. In this paper, we adapt an existing, classical process discovery algorithm to be able to handle partially ordered logs. We use the ILP Miner [1] as a basis and replace its region theory part by compact tokenflow regions [2] to introduce the ILP2 Miner. This ILP2 Miner handles sequential event logs just like the ILP Miner but, in addition, is able to directly process partially ordered logs. We prove that the ILP2 Miner provides the same guarantees regarding structural and behavioral properties of the discovered process models as the ILP Miner. We implement the ILP2 Miner and show experimental results of its runtime using three well-known example log files from the process mining community literature.
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van Zelst, S.J., van Dongen, B.F., van der Aalst, W.M.P., Verbeek, H.M.W: Discovering Workflow Nets Using Integer Linear Programming. In: Computing 100, pp. 529–556. Springer (2018). https://doi.org/10.1007/s00607-017-0582-5
Bergenthum, R.: Synthesizing Petri Nets from Hasse Diagrams. In: Carmona, J., Engels, G., Kumar, A. (eds.) BPM 2017. LNCS, vol. 10445, pp. 22–39. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-65000-5_2
van der Aalst, W.M.P.: Process Mining: Discovery, Conformance and Enhancement of Business Processes. Springer (2011)
van der Aalst, W., et al.: Process Mining Manifesto. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds.) BPM 2011. LNBIP, vol. 99, pp. 169–194. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-28108-2_19
van der Aalst, W. M. P., Carmona, J.: Process Mining Handbook. Springer (2022)
Dumas, M., García-Bañuelos, L.: Process Mining Reloaded: Event Structures as a Unified Representation of Process Models and Event Logs. In: Devillers, R., Valmari, A. (eds.) PETRI NETS 2015. LNCS, vol. 9115, pp. 33–48. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19488-2_2
Leemans, S. J. J., van Zelst, S. J., Lu, X.: Partial-order-based process mining: a survey and outlook. Knowledge and Information Systems, vol. 65, pp. 1–29. Springer (2023).
Bergenthum, R.: Prime Miner - Process Discovery using Prime Event Structures. ICPM 2019, pp. 41–48 (2019)
Reisig, W.: Understanding Petri Nets - Modeling Techniques, Analysis Methods. Springer, Case Studies (2013)
Desel, J., Juhás, G.: What is a Petri Net? In: Ehrig, H., Juhás, G., Padberg, J., Rozenberg, G. (eds.) Unifying Petri Nets, Advances in Petri Nets, LNCS 2128, pp. 1–25. Springer (2001). https://doi.org/10.1007/978-0-387-09766-4_134
Peterson, J.L.: Petri Net Theory and the Modeling of Systems. Prentice-Hall, Englewood Cliffs (1981)
van der Werf, J. M. E. M., van Dongen, B. F., Hurkens, C. A. J., Serebrenik, A.: Process discovery using integer linear programming. Fundamenta Informaticae, vol. 94 no. 3–4, pp. 387–412. IOS Press (2009)
ProM Tools Documentation. https://www.promtools.org/doku/docs/index.html. Accessed 22 Dec 2022
ProM Tools example log files. https://www.promtools.org/doku/prom6/downloads/example-logs.zip. Accessed 22 Dec 2022
van der Aalst, W.M.P.: Process Mining: Data Science in Action. Springer (2016). https://doi.org/10.1007/978-3-662-49851-4
Event logs and models used in Process Mining book. https://processmining.org/old-version/event-book.html. Accessed 22 Dec 2022
Armas-Cervantes, A., Dumas, M., La Rosa, M., Maaradji, A.: Local concurrency detection in business process event logs. In: ACM Transactions on Internet Technology, vol. 19, no. 1, pp. 1–23 (2019)
ILP Miner module repository. https://github.com/ILPN/ILPN-Module-ILP-miner. Accessed 22 Dec 2022
ILP2 Miner module repository. https://github.com/ILPN/ILPN-Module-ILP2-miner. Accessed 22 Dec 2022
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Folz-Weinstein, S., Bergenthum, R., Desel, J., Kovář, J. (2023). ILP2 Miner – Process Discovery for Partially Ordered Event Logs Using Integer Linear Programming. In: Gomes, L., Lorenz, R. (eds) Application and Theory of Petri Nets and Concurrency. PETRI NETS 2023. Lecture Notes in Computer Science, vol 13929. Springer, Cham. https://doi.org/10.1007/978-3-031-33620-1_4
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DOI: https://doi.org/10.1007/978-3-031-33620-1_4
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