Quotation Bayomie Sobh, Dina Sayed, Di Ciccio, Claudio, La Rosa, Marcello, Mendling, Jan. 2019. A Probabilistic Approach to Event-Case Correlation for Process Mining. In Conceptual Modeling - 38th International Conference, ER 2019, Hrsg. Laender A., Pernici B., Lim E-P., Palazzo de Oliveira J. 136-152. Salvador, Brazil: Springer.




Process mining aims to understand the actual behavior and performance of business processes from event logs recorded by IT systems. A key requirement is that every event in the log must be associated with a unique case identifier (e.g., the order ID in an order-to-cash process). In reality, however, this case ID may not always be present, especially when logs are acquired from different systems or when such systems have not been explicitly designed to offer process-tracking capabilities. Existing techniques for correlating events have worked with assumptions to make the problem tractable: some assume the generative processes to be acyclic while others require heuristic information or user input. In this paper, we lift these assumptions by presenting a novel technique called EC-SA based on probabilistic optimization. Given as input a sequence of timestamped events (the log without case IDs) and a process model describing the underlying business process, our approach returns an event log in which every event is mapped to a case identifier. The approach minimises the misalignment between the generated log and the input process model, and the variance between activity durations across cases. The experiments conducted on a variety of real-life datasets show the advantages of our approach over the state of the art.


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Publication's profile

Status of publication Published
Affiliation WU
Type of publication Contribution to conference proceedings
Language English
Title A Probabilistic Approach to Event-Case Correlation for Process Mining
Title of whole publication Conceptual Modeling - 38th International Conference, ER 2019
Editor Laender A., Pernici B., Lim E-P., Palazzo de Oliveira J.
Page from 136
Page to 152
Location Salvador, Brazil
Publisher Springer
Year 2019
ISBN 978-3-030-33223-5
URL https://www.springer.com/de/book/9783030332228
Open Access N


Bayomie Sobh, Dina Sayed (Details)
Di Ciccio, Claudio (Former researcher)
Mendling, Jan (Details)
La Rosa, Marcello (University of Melbourne, Australia)
Research areas (Ă–STAT Classification 'Statistik Austria')
1105 Computer software (Details)
1108 Informatics (Details)
1109 Information and data processing (Details)
1122 Artificial intelligence (Details)
5306 Business data processing (Details)
5367 Management information systems (Details)
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