Abstract
We propose a general framework for the process mining problem which encompasses the assumption of workflow schema with local constraints only, for it being applicable to more expressive specification languages, independently of the particular syntax adopted. In fact, we provide an effective technique for process mining based on the rather unexplored concept of clustering workflow executions, in which clusters of executions sharing the same structure and the same unexpected behavior (w.r.t. the local properties) are seen as a witness of the existence of global constraints.
An interesting framework for assessing the similarity between the original model and the discovered one is proposed, as well as some experimental results evidencing the validity of our approach.
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Greco, G., Guzzo, A., Pontieri, L., Saccà, D. (2004). Mining Expressive Process Models by Clustering Workflow Traces. In: Dai, H., Srikant, R., Zhang, C. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2004. Lecture Notes in Computer Science(), vol 3056. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24775-3_8
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DOI: https://doi.org/10.1007/978-3-540-24775-3_8
Publisher Name: Springer, Berlin, Heidelberg
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