Process Discovery Using Prior Knowledge

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.