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arXiv · 1909.11567

Discovering Process Models from Uncertain Event Data

Marco Pegoraro·Merih Seran Uysal·Wil M. P. van der Aalst
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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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Marco Pegoraro, Merih Seran Uysal, Wil M. P. van der Aalst. 2019-09-20. Discovering Process Models from Uncertain Event Data. https://doi.org/10.1007/978-3-030-37453-2_20

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