Conformance checking of partially matching processes: An entropy-based approach
AuthorPolyvyanyy, A; Kalenkova, A
Source TitleInformation Systems
AffiliationComputing and Information Systems
Document TypeJournal Article
CitationsPolyvyanyy, A. & Kalenkova, A. (2021). Conformance checking of partially matching processes: An entropy-based approach. Information Systems, pp.101720-101720. https://doi.org/10.1016/j.is.2021.101720.
Access StatusThis item is embargoed and will be available on 2023-01-20
ARC Grant codeARC/DP180102839
Conformance checking is an area of process mining that studies methods for measuring and characterizing commonalities and discrepancies between processes recorded in event logs of IT-systems and designed processes, either captured in explicit process models or implicitly induced by information systems. Applications of conformance checking range from measuring the quality of models automatically discovered from event logs, via regulatory process compliance, to automated process enhancement. Recently, process mining researchers initiated a discussion on the desired properties the conformance measures should possess. This discussion acknowledges that existing measures often do not satisfy the desired properties. Besides, there is a lack of understanding by the process mining community of the desired properties for conformance measures that address partially matching processes, i.e., processes that are not identical but differ in some process steps. In this article, we extend the recently introduced precision and recall conformance measures between an event log and process model that are based on the concept of entropy from information theory to account for partially matching processes. We discuss the properties the presented extended measures inherit from the original measures as well as properties for partially matching processes the new measures satisfy. All the presented conformance measures have been implemented in a publicly available tool. We present qualitative and quantitative evaluations based on our implementation that show the feasibility of using the proposed measures in industrial settings.
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