0000−0002−8997−7517] and Wil M.P. van der Aalst [0000−0002−0955−6940] Abstract. 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.
Expressing traces through models is utilized in REF for the calculation of upper and lower bounds for conformance scores of uncertain traces against classic reference models.
Mining Uncertain Event Data in Process Mining
International Conference on Process Mining (ICPM), Aachen, Germany, 2019, pp. 89-96
Computer Science