Mining Uncertain Event Data in Process Mining
This addresses the need for handling uncertainty in process data for researchers and practitioners in process mining, but it is incremental as it builds on existing techniques.
The paper tackles the problem of analyzing uncertain event logs in process mining, where quantified uncertainty is recorded alongside data, by defining a taxonomy and examining challenges in process discovery and conformance checking, and shows how to obtain upper and lower bounds for conformance by aligning uncertain traces onto regular process models.
Nowadays, more and more process data are automatically recorded by information systems, and made available in the form of event logs. Process mining techniques enable process-centric analysis of data, including automatically discovering process models and checking if event data conform to a certain model. In this paper we analyze the previously unexplored setting of uncertain event logs: logs where quantified uncertainty is recorded together with the corresponding data. We define a taxonomy of uncertain event logs and models, and we examine the challenges that uncertainty poses on process discovery and conformance checking. Finally, we show how upper and lower bounds for conformance can be obtained aligning an uncertain trace onto a regular process model.