0000−0002−8997−7517] , Merih Seran Uysal [0000−0003−1115−6601] , and Wil M.P. van der Aalst [0000−0002−0955−6940] 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.
A second application for behavior graphs in the domain of process mining over uncertain event data is given in REF .
Discovering Process Models from Uncertain Event Data
International Conference on Business Process Management (BPM), Vienna, Austria, 2019, pp. 238-249
Computer Science