A Probabilistic Approach to Event-Case Correlation for Process Mining

Dina Sayed Bayomie Sobh, Claudio Di Ciccio, Marcello La Rosa, Jan Mendling

Publication: Chapter in book/Conference proceedingContribution to conference proceedings

Abstract

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.
Original languageEnglish
Title of host publicationConceptual Modeling - 38th International Conference, ER 2019
Editors Laender A., Pernici B., Lim E-P., Palazzo de Oliveira J.
Place of PublicationSalvador, Brazil
PublisherSpringer
Pages136 - 152
ISBN (Print)978-3-030-33223-5
Publication statusPublished - 2019

Austrian Classification of Fields of Science and Technology (ÖFOS)

  • 102022 Software development
  • 102
  • 102001 Artificial intelligence
  • 502
  • 502050 Business informatics

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