Abstract
In this paper, we describe a process discovery algorithm that leverages prior knowledge and process execution data to learn a control-flow model. Most process discovery algorithms are not able to exploit prior knowledge supplied by a domain expert. Our algorithm incorporates prior knowledge using ideas from Bayesian statistics. We demonstrate that our algorithm is able to recover a control-flow model in the presence of noisy process execution data, and uncertain prior knowledge. © 2013 Springer-Verlag.
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CITATION STYLE
Rembert, A. J., Omokpo, A., Mazzoleni, P., & Goodwin, R. T. (2013). Process discovery using prior knowledge. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8274 LNCS, pp. 328–342). https://doi.org/10.1007/978-3-642-45005-1_23
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