Discovering Process Models from Uncertain Event Data ⋆ Marco Pegoraro [0000−0002−8997−7517] , Merih Seran Uysal [0000−0003−1115−6601] , and Wil M.P. van der Aalst [0000−0002−0955−6940] Process and Data Science Group (PADS) Department of Computer Science, RWTH Aachen University, Aachen, Germany {pegoraro,uysal,wvdaalst}@pads.rwth-aachen.de http://www.pads.rwth-aachen.de/ 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 to- gether 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 expe- rimental results of performing inductive mining over the directly-follows graph to obtain models representing the certain and uncertain part of the process. Keywords: Process Mining · Process Discovery · Uncertain Data. 1 Introduction With the advent of digitalization of business processes and related management tools, Process-Aware Information Systems (PAISs), ranging from ERP/CRM- systems to BPM/WFM-systems, are widely used to support operational admi- nistration of processes. The databases of PAISs containing event data can be queried to obtain event logs, collections of recordings of the execution of activities belonging to the process. The discipline of process mining aims to synthesize knowledge about processes via the extraction and analysis of execution logs. When applying process mining in real-life settings, the need to address anoma- lies in data recording when performing analyses is omnipresent. A number of such anomalies can be modeled by using the notion of uncertainty: uncertain event logs contain, alongside the event data, some attributes that describe a certain level of uncertainty affecting the data. A typical example is the timestamp infor- mation: in many processes, specifically the ones where data is in part manually recorded, the timestamp of events is recorded with low precision (e.g., specifying ⋆ In International Workshop on Business Process Intelligence (BPI 2019). Please do not print this document unless strictly necessary. arXiv:1909.11567v1 [cs.DB] 20 Sep 2019