Discovering Process Models from Uncertain Event Data
This work addresses the challenge of handling uncertain data in process mining for domains where event logs are incomplete or noisy, but it appears incremental as it builds on existing inductive mining methods.
The paper tackles the problem of discovering process models from event logs that include explicit uncertainty information, resulting in a technique that produces models representing both certain and uncertain parts of the process.
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.