Abstract
Process mining aims to infer meaningful insights from process-related data and attracted the attention of practitioners, tool-vendors, and researchers in recent years. Traditionally, event logs are assumed to describe the as-is situation. But this is not necessarily the case in environments where logging may be compromised due to manual logging. For example, hospital staff may need to manually enter information regarding the patient’s treatment. As a result, events or timestamps may be missing or incorrect.
In this work, we make use of process knowledge captured in process models, and provide a method to repair missing events in the logs. This way, we facilitate analysis of incomplete logs. We realize the repair by combining stochastic Petri nets, alignments, and Bayesian networks.
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Rogge-Solti, A., Mans, R.S., van der Aalst, W.M.P., Weske, M. (2013). Repairing Event Logs Using Timed Process Models. In: Demey, Y.T., Panetto, H. (eds) On the Move to Meaningful Internet Systems: OTM 2013 Workshops. OTM 2013. Lecture Notes in Computer Science, vol 8186. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41033-8_89
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DOI: https://doi.org/10.1007/978-3-642-41033-8_89
Publisher Name: Springer, Berlin, Heidelberg
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