Abstract
Process discovery techniques return process models that are either formal (precisely describing the possible behaviors) or informal (merely a “picture” not allowing for any form of formal reasoning). Formal models are able to classify traces (i.e., sequences of events) as fitting or non-fitting. Most process mining approaches described in the literature produce such models. This is in stark contrast with the over 25 available commercial process mining tools that only discover informal process models that remain deliberately vague on the precise set of possible traces. There are two main reasons why vendors resort to such models: scalability and simplicity. In this paper, we propose to combine the best of both worlds: discovering hybrid process models that have formal and informal elements. As a proof of concept we present a discovery technique based on hybrid Petri nets. These models allow for formal reasoning, but also reveal information that cannot be captured in mainstream formal models. A novel discovery algorithm returning hybrid Petri nets has been implemented in ProM and has been applied to several real-life event logs. The results clearly demonstrate the advantages of remaining “vague” when there is not enough “evidence” in the data or standard modeling constructs do not “fit”. Moreover, the approach is scalable enough to be incorporated in industrial-strength process mining tools.
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Notes
- 1.
Install ProM and the package HybridMiner from http://www.promtools.org.
- 2.
The reader is invited to redo the experiments using the latest version of ProM, the HybridMiner package (promtools.org), and the publicly available data sets used here [16].
References
van der Aalst, W.M.P.: Process Mining: Data Science in Action. Springer, Berlin (2016)
van der Aalst, W.M.P., De Masellis, R., Di Francescomarino, C., Ghidini, C.: Learning Hybrid Process Models From Events: Process Discovery Without Faking Confidence (Experimental Results). ArXiv e-prints 1703.06125 (2017). arXiv.org/abs/1703.06125
van der Aalst, W.M.P., Weijters, A.J.M.M., Maruster, L.: Workflow mining: discovering process models from event logs. IEEE Trans. Knowl. Data Eng. 16(9), 1128–1142 (2004)
Adriansyah, A.: Aligning observed and modeled behavior. Ph.D thesis, Eindhoven University of Technology, April 2014
Adriansyah, A., Munoz-Gama, J., Carmona, J., van Dongen, B.F., van der Aalst, W.M.P.: Measuring precision of modeled behavior. ISeB 13(1), 37–67 (2015)
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). doi:10.1007/978-3-540-75183-0_27
Famelis, M., Salay, R., Chechik, M., Models, P.: Towards modeling and reasoning with uncertainty. In: International Conference on Software Engineering (ICSE 2012), pp. 573–583. IEEE Computer Society (2012)
Günther, C.W., van der Aalst, W.M.P.: Fuzzy mining – adaptive process simplification based on multi-perspective metrics. In: Alonso, G., Dadam, P., Rosemann, M. (eds.) BPM 2007. LNCS, vol. 4714, pp. 328–343. Springer, Heidelberg (2007). doi:10.1007/978-3-540-75183-0_24
Herrmann, T., Hoffmann, M., Loser, K.U., Moysich, K.: Semistructured models are surprisingly useful for user-centered design. In: De Michelis, G., Giboin, A., Karsenty, L., Dieng, R. (eds.) Designing Cooperative Systems (Coop 2000), pp. 159–174. IOS Press, Amsterdam (2000)
Herrmann, T., Loser, K.U.: Vagueness in models of socio-technical systems. Behav. Inf. Technol. 18(5), 313–323 (1999)
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). doi: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). doi:10.1007/978-3-319-06257-0_6
Leemans, S.J.J., Fahland, D., van der Aalst, W.M.P.: Scalable process discovery and conformance checking. Softw. Syst. Model., pp. 1–33 (2016). doi:10.1007/s10270-016-0545-x
Salay, R., Chechik, M., Horkoff, J., Di Sandro, A.: Managing requirements uncertainty with partial models. Requirements Eng. 18(2), 107–128 (2013)
Solé, M., Carmona, J.: Process mining from a basis of state regions. In: Lilius, J., Penczek, W. (eds.) PETRI NETS 2010. LNCS, vol. 6128, pp. 226–245. Springer, Heidelberg (2010). doi:10.1007/978-3-642-13675-7_14
van Dongen, B.F.: BPI Challenges (2011–2017), Real life Event Logs Collection (2017). data.4tu.nl/repository/collection:event_logs
Weijters, A.J.M.M., van der Aalst, W.M.P.: Rediscovering workflow models from event-based data using little thumb. Integr. Comput.-Aided Eng. 10(2), 151–162 (2003)
van der Werf, J.M.E.M., van Dongen, B.F., Hurkens, C.A.J., Serebrenik, A.: Process discovery using integer linear programming. Fundam. Inf. 94, 387–412 (2010)
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van der Aalst, W.M.P., De Masellis, R., Di Francescomarino, C., Ghidini, C. (2017). Learning Hybrid Process Models from Events. In: Carmona, J., Engels, G., Kumar, A. (eds) Business Process Management. BPM 2017. Lecture Notes in Computer Science(), vol 10445. Springer, Cham. https://doi.org/10.1007/978-3-319-65000-5_4
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