Abstract
Process mining techniques provide process analysts with insights into interesting patterns of a business process. Current techniques have focused by and large on the explanation of behavior, partially by help of features that relate to multiple perspectives beyond just pure control flow. However, techniques to provide insights into the connection between data elements of related events have been missing so far. Such connections are relevant for several analysis tasks such as event correlation, resource allocation, or log partitioning. In this paper, we propose a multi-perspective mining technique for discovering data connections. More specifically, we adapt concepts from association rule mining to extract connections between a sequence of events and behavioral attributes of related data objects and contextual features. Our technique was evaluated using real-world events supporting the usefulness of the mined association rules.
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Bayomie, D., Revoredo, K., Mendling, J. (2022). Multi-perspective Process Analysis: Mining the Association Between Control Flow and Data Objects. In: Franch, X., Poels, G., Gailly, F., Snoeck, M. (eds) Advanced Information Systems Engineering. CAiSE 2022. Lecture Notes in Computer Science, vol 13295. Springer, Cham. https://doi.org/10.1007/978-3-031-07472-1_5
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