Abstract
Process-aware information systems (PAISs) can be configured using a reference process model, which is typically obtained via expert interviews. Over time, however, contextual factors and system requirements may cause the operational process to start deviating from this reference model. While a reference model should ideally be updated to remain aligned with such changes, this is a costly and often neglected activity. We present a new process mining technique that automatically improves the reference model on the basis of the observed behavior as recorded in the event logs of a PAIS. We discuss how to balance the four basic quality dimensions for process mining (fitness, precision, simplicity and generalization) and a new dimension, namely the structural similarity between the reference model and the discovered model. We demonstrate the applicability of this technique using a real-life scenario from a Dutch municipality.
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Buijs, J.C.A.M., La Rosa, M., Reijers, H.A., van Dongen, B.F., van der Aalst, W.M.P. (2013). Improving Business Process Models Using Observed Behavior. In: Cudre-Mauroux, P., Ceravolo, P., Gašević, D. (eds) Data-Driven Process Discovery and Analysis. SIMPDA 2012. Lecture Notes in Business Information Processing, vol 162. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40919-6_3
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DOI: https://doi.org/10.1007/978-3-642-40919-6_3
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