Abstract
Modern information systems are able to collect event data in the form of event logs. Process mining techniques allow to discover a model from event data, to check the conformance of an event log against a reference model, and to perform further process-centric analyses. In this paper, we consider uncertain event logs, where data is recorded together with explicit uncertainty information. We describe a technique to discover a directly-follows graph from such event data which retains information about the uncertainty in the process. We then present experimental results of performing inductive mining over the directly-follows graph to obtain models representing the certain and uncertain part of the process.
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References
Van der Aalst, W., Weijters, T., Maruster, L.: Workflow mining: discovering process models from event logs. IEEE Trans. Knowl. Data Eng. 16(9), 1128–1142 (2004)
Van der Aalst, W.M.: Process Mining: Data Science in Action. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-662-49851-4
Berti, A., van Zelst, S.J., van der Aalst, W.: Process mining for Python (PM4Py): bridging the gap between process- and data science. In: International Conference on Process Mining - Demo Track. IEEE (2019)
Conforti, R., La Rosa, M., ter Hofstede, A.H.: Filtering out infrequent behavior from business process event logs. IEEE Trans. Knowl. Data Eng. 29(2), 300–314 (2017)
Hornik, K., Grün, B., Hahsler, M.: arules - a computational environment for mining association rules and frequent item sets. J. Stat. Softw. 14(15), 1–25 (2005)
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., Fahland, D., Van der Aalst, W.M.: Scalable process discovery and conformance checking. Softw. Syst. Model. 17(2), 599–631 (2018)
Lu, X., Fahland, D., van den Biggelaar, F.J.H.M., van der Aalst, W.M.P.: Detecting deviating behaviors without models. In: Reichert, M., Reijers, H.A. (eds.) BPM 2015. LNBIP, vol. 256, pp. 126–139. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-42887-1_11
Pegoraro, M., van der Aalst, W.M.: Mining uncertain event data in process mining. In: International Conference on Process Mining. IEEE (2019)
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Pegoraro, M., Uysal, M.S., van der Aalst, W.M.P. (2019). Discovering Process Models from Uncertain Event Data. In: Di Francescomarino, C., Dijkman, R., Zdun, U. (eds) Business Process Management Workshops. BPM 2019. Lecture Notes in Business Information Processing, vol 362. Springer, Cham. https://doi.org/10.1007/978-3-030-37453-2_20
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DOI: https://doi.org/10.1007/978-3-030-37453-2_20
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