Abstract
Due to the rise of IoT, event data becomes increasingly fine-grained. Faced with such data, process discovery often produces incomprehensible spaghetti-models expressed at a granularity level that doesn’t match the mental model of a business user. One approach is to use event abstraction patterns to transform the event log towards a more coarse-grained level and to discover process models from this transformed log. Recent literature has produced various (partial) implementations of this approach, but insights how these techniques compare against each other is still limited.
This paper focuses on the use of Local Process Models and Combination based Behavioural Pattern Mining to discover event abstraction patterns in combination with the approach of Mannhardt et al. [15] to transform the event log. Experiments are conducted to gain insights into the performance of these techniques. Results are very limited with a general decrease in fitness and precision and only a minimal improvement of complexity. Results also show that the combination of the process discovery algorithm and the event abstraction pattern miner matters. In particular, the combination of Local Process Models with Split Miner seems to improve precision.
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Notes
- 1.
This is done via the R package understandBPMN [13].
- 2.
The event logs were extracted from the 4TU Centre for Research Data in May 2020.
- 3.
Done via the convert BPMN diagram to Petri Net (Control Flow) plug-in in ProM.
- 4.
References
Van der Aalst, W.: Process Mining: Data Science in Action, pp. 3–23. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-662-49851-4_1
van der Aalst, W.M.P., Weijters, T., Maruster, L.: Workflow mining: discovering process models from event logs. IEEE Trans. Knowl. Data Eng. 16(9), 1128–1142 (2004)
Acheli, M., Grigori, D., Weidlich, M.: Efficient discovery of compact maximal behavioral patterns from event logs. In: Giorgini, P., Weber, B. (eds.) CAiSE 2019. LNCS, vol. 11483, pp. 579–594. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-21290-2_36
Adriansyah, A., van Dongen, B.F., van der Aalst, W.M.: Conformance checking using cost-based fitness analysis. In: 2011 IEEE 15th International Enterprise Distributed Object Computing Conference, pp. 55–64. IEEE (2011)
Adriansyah, A., Munoz-Gama, J., Carmona, J., van Dongen, B.F., van der Aalst, W.M.: Measuring precision of modeled behavior. Inf. Syst. e-bus. Manag. 13(1), 37–67 (2015)
Alharbi, A., Bulpitt, A., Johnson, O.A.: Towards Unsupervised Detection of Process Models in Healthcare. Studies in Health Technology and Informatics, pp. 381–385. IOS Press, Netherlands (2018)
Augusto, A., Conforti, R., Dumas, M., La Rosa, M., Polyvyanyy, A.: Split miner: automated discovery of accurate and simple business process models from event logs. Knowl. Inf. Syst. 59(2), 251–284 (2019)
Jagadeesh Chandra Bose, R.P., van der Aalst, W.M.P.: Abstractions in process mining: a taxonomy of patterns. In: Dayal, U., Eder, J., Koehler, J., Reijers, H.A. (eds.) BPM 2009. LNCS, vol. 5701, pp. 159–175. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-03848-8_12
Brunings, M., Fahland, D., van Dongen, B.: Defining meaningful local process models. In: Proceedings of the International Workshop on Algorithms and Theories for the Analysis of Event Data 2020. CEUR-WS.org (2020). http://ceur-ws.org/Vol-2625/paper-01.pdf
Folino, F., Guarascio, M., Pontieri, L.: Mining multi-variant process models from low-level logs. In: Abramowicz, W. (ed.) BIS 2015. LNBIP, vol. 208, pp. 165–177. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19027-3_14
Günther, C.W., Rozinat, A., van der Aalst, W.M.P.: Activity mining by global trace segmentation. In: Rinderle-Ma, S., Sadiq, S., Leymann, F. (eds.) BPM 2009. LNBIP, vol. 43, pp. 128–139. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12186-9_13
Leemans, S.J.J., Fahland, D., van der Aalst, W.M.P.: Process and deviation exploration with inductive visual miner. In: Limonad, L., Weber, B. (eds.) Business Process Management Demo Sessions (BPMD 2014). CEUR Workshop Proceedings, vol. 1295, pp. 46–50. Eindhoven, The Netherlands. CEUR-WS.org (2014)
Lieben, J., Jouck, T., Depaire, B., Jans, M.: An improved way for measuring simplicity during process discovery. In: Pergl, R., Babkin, E., Lock, R., Malyzhenkov, P., Merunka, V. (eds.) EOMAS 2018. LNBIP, vol. 332, pp. 49–62. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00787-4_4
Mannhardt, F., de Leoni, M., Reijers, H.A., van der Aalst, W.M.P., Toussaint, P.J.: From low-level events to activities - a pattern-based approach. In: La Rosa, M., Loos, P., Pastor, O. (eds.) BPM 2016. LNCS, vol. 9850, pp. 125–141. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-45348-4_8
Mannhardt, F., de Leoni, M., Reijers, H.A., van der Aalst, W.M., Toussaint, P.J.: Guided process discovery-a pattern-based approach. Inf. Syst. 76, 1–18 (2018)
Mannhardt, F., Tax, N.: Unsupervised event abstraction using pattern abstraction and local process models. In: Gulden, J., et al. (eds.) CEUR Workshop Proceedings, vol. 1859, pp. 55–63. CEUR-WS.org (2017)
Mendling, J.: Metrics for Process Models: Empirical Foundations of Verification, Error Prediction, and Guidelines for Correctness, vol. 6. Springer Science & Business Media (2008)
Rehse, J.-R., Fettke, P.: Clustering business process activities for identifying reference model components. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) BPM 2018. LNBIP, vol. 342, pp. 5–17. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11641-5_1
Sánchez-Charles, D., Carmona, J., Muntés-Mulero, V., Solé, M.: Reducing event variability in logs by clustering of word embeddings. In: Teniente, E., Weidlich, M. (eds.) BPM 2017. LNBIP, vol. 308, pp. 191–203. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-74030-0_14
Senderovich, A., Rogge-Solti, A., Gal, A., Mendling, J., Mandelbaum, A.: The ROAD from sensor data to process instances via interaction mining. In: Nurcan, S., Soffer, P., Bajec, M., Eder, J. (eds.) CAiSE 2016. LNCS, vol. 9694, pp. 257–273. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-39696-5_16
Tax, N., Dalmas, B., Sidorova, N., van der Aalst, W.M., Norre, S.: Interest-driven discovery of local process models. Inf. Syst. 77, 105–117 (2018)
Tax, N., Sidorova, N., Haakma, R., van der Aalst, W.M.P.: Event abstraction for process mining using supervised learning techniques. In: Bi, Y., Kapoor, S., Bhatia, R. (eds.) IntelliSys 2016. LNNS, vol. 15, pp. 251–269. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-56994-9_18
Tax, N., Sidorova, N., Haakma, R., van der Aalst, W.M.: Mining local process models. J. Innov. Digit. Ecosyst. 3(2), 183–196 (2016)
van Dongen, B.F., de Medeiros, A.K.A., Verbeek, H.M.W., Weijters, A.J.M.M., van der Aalst, W.M.P.: The ProM framework: a new era in process mining tool support. In: Ciardo, G., Darondeau, P. (eds.) ICATPN 2005. LNCS, vol. 3536, pp. 444–454. Springer, Heidelberg (2005). https://doi.org/10.1007/11494744_25
van Zelst, S.J., Mannhardt, F., de Leoni, M., Koschmider, A.: Event abstraction in process mining: literature review and taxonomy. Granular Comput. 1–18 (2020)
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Van Houdt, G., Depaire, B., Martin, N. (2021). Unsupervised Event Abstraction in a Process Mining Context: A Benchmark Study. In: Leemans, S., Leopold, H. (eds) Process Mining Workshops. ICPM 2020. Lecture Notes in Business Information Processing, vol 406. Springer, Cham. https://doi.org/10.1007/978-3-030-72693-5_7
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