Abstract
Several simplification techniques have been proposed in process mining to improve the interpretability of complex processes, such as the structural simplification of the model or the simplification of the log. However, obtaining a comprehensible model explaining the behaviour of unstructured large processes is still an open challenge. In this paper, we present WoSimp, a novel algorithm to simplify processes by abstracting the infrequent behaviour from the logs, allowing to discover a simpler process model. This algorithm has been validated with more than 10 complex real processes, most of them from Business Process Management Challenges. Experiments show that WoSimp simplifies the process log and allows to discover a better process model than the state of the art techniques.
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Notes
- 1.
The algorithm, datasets and results can be downloaded from http://tec.citius.usc.es/processmining/WoSimp/.
- 2.
Using plugin Matrix Filter in ProM with Mean as the Threshold adjusting Method.
- 3.
Using the plugin Activity Filter: Indirect Entropy optimized with Greedy Search in ProM [17].
- 4.
Activity Filter takes more than 24 h to converge in datasets with more than 300 activities, thus, no results of this technique are shown in those datasets.
References
van der Aalst, W.M.P.: Process Mining - Data Science in Action, 2nd edn. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-662-49851-4
Polyvyanyy, A., García-Bañuelos, L., Dumas, M.: Structuring acyclic process models. Inf. Syst. 37(6), 518–538 (2012)
Fahland, D., van der Aalst, W.M.P.: Simplifying discovered process models in a controlled manner. Inf. Syst. 38(4), 585–605 (2013)
de San Pedro, J., Carmona, J., Cortadella, J.: Log-based simplification of process models. In: Motahari-Nezhad, H.R., Recker, J., Weidlich, M. (eds.) BPM 2015. LNCS, vol. 9253, pp. 457–474. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-23063-4_30
Sani, M.F., van Zelst, S.J., van der Aalst, W.M.P.: Improving process discovery results by filtering outliers using conditional behavioural probabilities. In: Teniente, E., Weidlich, M. (eds.) BPM 2017. LNBIP, vol. 308, pp. 216–229. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-74030-0_16
Tax, N., Sidorova, N., van der Aalst, W.M.P.: Discovering more precise process models from event logs by filtering out chaotic activities. J. Intell. Inf. Syst. 52(1), 107–139 (2019)
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., Tax, N.: Unsupervised event abstraction using pattern abstraction and local process models. In: Gulden, J., Nurcan, S., et al. (eds.) BPMDS 2017. CEUR Workshop Proceedings, vol. 1859, pp. 55–63. CEUR-WS.org (2017)
Tax, N., Sidorova, N., Haakma, R., van der Aalst, W.M.P.: Mining local process models. CoRR abs/1606.06066 (2016)
Chapela-Campa, D., Mucientes, M., Lama, M.: Mining frequent patterns in process models. Inf. Sci. 472, 235–257 (2019)
Desel, J., Reisig, W.: Place/transition Petri Nets. In: Reisig, W., Rozenberg, G. (eds.) ACPN 1996. LNCS, vol. 1491, pp. 122–173. Springer, Heidelberg (1998). https://doi.org/10.1007/3-540-65306-6_15
Mannhardt, F. (Felix): Sepsis cases - event log (2016)
Van Dongen, B.: Real-life event logs - hospital log (2011)
Van Dongen, B.: BPI Challenge 2012 (2012)
Steeman, W.: BPI Challenge 2013 (2013)
Van Dongen, B.: BPI Challenge 2015 (2015)
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
Leemans, S.J.J., Fahland, D., van der Aalst, W.M.P.: Discovering block-structured process models from event logs - a constructive approach. In: Colom, J.-M., Desel, J. (eds.) PETRI NETS 2013. LNCS, vol. 7927, pp. 311–329. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38697-8_17
Leemans, S.J.J., Fahland, D., van der Aalst, W.M.P.: Discovering block-structured process models from event logs containing infrequent behaviour. In: Lohmann, N., Song, M., Wohed, P. (eds.) BPM 2013. LNBIP, vol. 171, pp. 66–78. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-06257-0_6
Adriansyah, A., van Dongen, B.F., van der Aalst, W.M.P.: Conformance checking using cost-based fitness analysis. In: EDOC 2011, pp. 55–64. IEEE Computer Society (2011)
vanden Broucke, S.K.L.M., Weerdt, J.D., Vanthienen, J., Baesens, B.: Determining process model precision and generalization with weighted artificial negative events. IEEE Trans. Knowl. Data Eng. 26(8), 1877–1889 (2014)
vanden Broucke, S.K.L.M., Weerdt, J.D., Vanthienen, J., Baesens, B.: A comprehensive benchmarking framework (CoBeFra) for conformance analysis between procedural process models and event logs in ProM. In: IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2013, pp. 254–261. IEEE (2013)
Acknowledgments
This research was funded by the Spanish Ministry of Economy and Competitiveness under grant TIN2017-84796-C2-1-R, and the Galician Ministry of Education, Culture and Universities under grant ED431G/08. These grants are co-funded by the European Regional Development Fund (ERDF/FEDER program). D. Chapela-Campa is supported by the Spanish Ministry of Education, under the FPU national plan (FPU16/04428).
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Chapela-Campa, D., Mucientes, M., Lama, M. (2019). Simplification of Complex Process Models by Abstracting Infrequent Behaviour. In: Yangui, S., Bouassida Rodriguez, I., Drira, K., Tari, Z. (eds) Service-Oriented Computing. ICSOC 2019. Lecture Notes in Computer Science(), vol 11895. Springer, Cham. https://doi.org/10.1007/978-3-030-33702-5_32
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