Abstract
Business process simulation enables analysts to run a process in different scenarios, compare its performances and consequently provide indications on how to improve a business process. Process simulation requires one to provide a simulation model, which should accurately reflect reality to ensure the reliability of the simulation findings. An accurate simulation model passes through a correct stochastic modelling of the activity firings: activities are associated with the probability of each to fire. Literature determines these probabilities by looking at the frequency of the activity occurrences when they are enabled. This is a coarse determination, because this way does not consider the actual process state, which might influence the probabilities themselves (e.g., a thorough loan assessment is more likely for larger loan requests). The process state is as a faithful abstraction of the process instance execution so far, including the process-variable values, the activity firing history, etc. This paper aims to investigate how process states can be leveraged to improve activity firing probabilities. A technique has been put forward and compared with the baseline where basic branching probabilities are employed. Experimental results show that, indeed, business simulation models are more accurate to replicate the real process’ behavior.
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Notes
- 1.
\(\mathbb {B}(X)\) indicates the set of all multisets with the elements in set X.
- 2.
For the minimalisation, we do not count model moves on unlabelled transitions.
- 3.
References
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 Mining Knowl. Discov. 2(2), 182–192 (2012)
Baier, C., Katoen, J.: Principles of Model Checking. MIT Press, Cambridge (2008)
Burke, A., Leemans, S.J.J., Wynn, M.T.: Stochastic process discovery by weight estimation. In: Leemans, S., Leopold, H. (eds.) ICPM 2020. LNBIP, vol. 406, pp. 260–272. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-72693-5_20
Camargo, M., Dumas, M., González, O.: Automated discovery of business process simulation models from event logs. Decis. Support Syst. 134, 113284 (2020). https://doi.org/10.1016/j.dss.2020.113284
Camargo, M., Dumas, M., Rojas, O.G.: SIMOD: a tool for automated discovery of business process simulation models. In: Proceedings of the Dissertation Award, Doctoral Consortium, and Demonstration Track at BPM 2019 co-located with 17th International Conference on Business Process Management, BPM 2019, Vienna, Austria, 1–6 September 2019. CEUR Workshop Proceedings, vol. 2420, pp. 139–143. CEUR-WS.org (2019). https://ceur-ws.org/Vol-2420/paperDT5.pdf
Camargo, M., Dumas, M., Rojas, O.G.: Discovering generative models from event logs: data-driven simulation vs deep learning. PeerJ Comput. Sci. 7, e577 (2021). https://doi.org/10.7717/peerj-cs.577
Camargo, M., Dumas, M., Rojas, O.G.: Learning accurate business process simulation models from event logs via automated process discovery and deep learning. In: Franch, X., Poels, G., Gailly, F., Snoeck, M. (eds.) Advanced Information Systems Engineering - 34th International Conference, CAiSE 2022. LNCS, vol. 13295, pp. 55–71. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-07472-1_4, https://doi.org/10.1007/978-3-031-07472-1_4
Leemans, S.J.J., Syring, A.F., van der Aalst, W.M.P.: Earth movers’ stochastic conformance checking. In: Hildebrandt, T., van Dongen, B., Röglinger, M., Mendling, J. (eds.) Business Process Management Forum - BPM Forum 2019. LNIBP, vol. 360, pp. 127–143. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-26643-1_8
Mannhardt, F.: Multi-perspective process mining. Ph.D. thesis, Mathematics and Computer Science (2018). proefschrift
Mannhardt, F., Leemans, S.J.J., Schwanen, C.T., de Leoni, M.: Modelling data-aware stochastic processes - discovery and conformance checking. In: Gomes, L., Lorenz, R. (eds.) Application and Theory of Petri Nets and Concurrency. PETRI NETS 2023. LNCS, vol. 13929, pp. 77–98. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-33620-1_5
Mannhardt, F., de Leoni, M., Reijers, H.A.: The multi-perspective process explorer. In: Proceedings of the BPM Demo Session 2015 Co-located with the 13th International Conference on Business Process Management (BPM 2015), Innsbruck, Austria, September 2, 2015. CEUR Workshop Proceedings, vol. 1418, pp. 130–134. CEUR-WS.org (2015). http://ceur-ws.org/Vol-1418/paper27.pdf
Pourbafrani, M., van der Aalst, W.M.P.: Interactive process improvement using simulation of enriched process trees. In: Hacid, H., et al. (eds.) Service-Oriented Computing - ICSOC 2021 Workshops, vol. 13236. LNCS, pp. 61–76. Springer International Publishing, Cham (2022). https://doi.org/10.1007/978-3-031-14135-5_5
Pourbafrani, M., Balyan, S., Ahmed, M., Chugh, S., van der Aalst, W.M.P.: GenCPN: automatic CPN model generation of processes. In: Proceedings of the ICPM Doctoral Consortium and Demo Track 2021 co-located with 3rd International Conference on Process Mining, ICPM Doctoral Consortium / Demo Track 2021, Eindhoven, The Netherlands, November 2021. CEUR Workshop Proceedings, vol. 3098, pp. 23–24 (2021). http://ceur-ws.org/Vol-3098/demo_192.pdf
Pourbafrani, M., van Zelst, S.J., van der Aalst, W.M.P.: Supporting automatic system dynamics model generation for simulation in the context of process mining. In: Abramowicz, W., Klein, G. (eds.) Business Information Systems - BIS 2020. LNBIP, vol. 389, pp. 249–263. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-53337-3_19
Rogge-Solti, A., van der Aalst, W.M.P., Weske, M.: Discovering stochastic petri nets with arbitrary delay distributions from event logs. In: Lohmann, N., Song, M., Wohed, P. (eds.) BPM 2013. LNBIP, vol. 171, pp. 15–27. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-06257-0_2
Rogge-Solti, A., Weske, M.: Prediction of business process durations using non-Markovian stochastic petri nets. Inf. Syst. 54, 1–14 (2015). https://doi.org/10.1016/j.is.2015.04.004
Witten, I.H., Frank, E., Hall, M.A.: Data Mining: Practical Machine Learning Tools and Techniques, 3rd edn. Morgan Kaufmann, Elsevier (2011)
Acknowledgment
The research is financially supported by MUR (PNRR) and University of Padua, by the Department of Mathematics of University of Padua, through the BIRD project “Data-driven Business Process Improvement” (code BIRD215924/21), and by the “Smart Journey Mining project” funded by the Research Council of Norway (grant no. 312198).
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de Leoni, M., Vinci, F., Leemans, S.J.J., Mannhardt, F. (2023). Investigating the Influence of Data-Aware Process States on Activity Probabilities in Simulation Models: Does Accuracy Improve?. 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_8
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