Abstract
Process mining allows organizations to obtain relevant insights into the execution of their processes. However, the starting point of any process mining analysis is an event log, which is typically not readily available in practice. The extraction of event logs from the relevant databases is a manual and highly time-consuming task, and often a hurdle for the application of process mining altogether. Available support for event log extraction comes with different assumptions and requirements and only provides limited automated support. In this paper, we therefore take a novel angle at supporting event log extraction. The core idea of our paper is to use an existing process model as a starting point and automatically identify to which database tables the activities of the considered process model relate to. Based on the resulting mapping, an event log can then be extracted in an automated fashion. We use this paper to define a first approach that is able to identify such a mapping between a process model and a database. We evaluate our approach using three real-world databases and five process models from the purchase-to-pay domain. The results of our evaluation show that our approach has the potential to successfully support event log extraction based on matching.
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Part of this research was funded by NWO (Netherlands Organisation for Scientific Research) project number 16672.
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Stein Dani, V., Leopold, H., van der Werf, J.M.E.M., Reijers, H.A. (2023). Supporting Event Log Extraction Based on Matching. In: Cabanillas, C., Garmann-Johnsen, N.F., Koschmider, A. (eds) Business Process Management Workshops. BPM 2022. Lecture Notes in Business Information Processing, vol 460. Springer, Cham. https://doi.org/10.1007/978-3-031-25383-6_24
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DOI: https://doi.org/10.1007/978-3-031-25383-6_24
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