An XES Extension for 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] Chair of Process and Data Science (PADS) Department of Computer Science, RWTH Aachen University, Aachen, Germany {pegoraro,uysal,wvdaalst}@pads.rwth-aachen.de http://www.pads.rwth-aachen.de/ Abstract. Event data, often stored in the form of event logs, serve as the starting point for process mining and other evidence-based process improvements. However, event data in logs are often tainted by noise, er- rors, and missing data. Recently, a novel body of research has emerged, with the aim to address and analyze a class of anomalies known as uncer- tainty —imprecisions quantified with meta-information in the event log. This paper illustrates an extension of the XES data standard capable of representing uncertain event data. Such an extension enables input, output, and manipulation of uncertain data, as well as analysis through the process discovery and conformance checking approaches available in literature. Keywords: Event Data · Uncertainty · XES Standard · Process Mining · Business Process Management. 1 Introduction Through the last decades, the increase in the availability of data generated by the execution of processes has enabled the development of the set of disciplines known as process sciences. These fields of science aim to analyze data accounting for the process perspective—the flow of events belonging to a process case. Uncertain event data is a newly-emerging class of anomalous event data. Uncertain data consists of events that have been logged with a quantified mea- sure of uncertainty affecting the recorded information. Sources of uncertainty include noise, human error, or limitations of the information system supporting the process. Such imprecisions affecting the event data are either recorded in an information system with the data itself or reconstructed in a subsequent process- ing step, often with the aid of domain knowledge provided by process experts. Recently, the possible types of uncertain data have been classified in a taxon- omy, and effective process mining algorithms for uncertain event data have been introduced [7,9]. However, the data standards currently in use within the process ⋆ We thank the Alexander von Humboldt (AvH) Stiftung for supporting our research interactions. We thank and acknowledge Fabian Rempfer for his valuable input on writing style, and Majid Rafiei for his contribution to the graphics. arXiv:2204.04135v1 [cs.DB] 8 Apr 2022