Discovering Process Models from Uncertain Event Data

• Published in: Business Process Management Workshops pp. 238 - 249
• Main Authors: Pegoraro, Marco, Uysal, Merih Seran, van der Aalst, Wil M. P.
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing
• Series: Lecture Notes in Business Information Processing
• Subjects: Process discovery; Process mining; Uncertain data
• ISBN: 3030374521; 9783030374525
• ISSN: 1865-1348; 1865-1356
• DOI: 10.1007/978-3-030-37453-2_20

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.