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.
CITATION STYLE
Pegoraro, M., Uysal, M. S., & van der Aalst, W. M. P. (2019). Discovering Process Models from Uncertain Event Data. In Lecture Notes in Business Information Processing (Vol. 362 LNBIP, pp. 238–249). Springer. https://doi.org/10.1007/978-3-030-37453-2_20
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