Entropic relevance: A mechanism for measuring stochastic process models discovered from event data

Abstract

There are many fields of computing in which having access to large volumes of data allows very precise models to be developed. For example, machine learning employs a range of algorithms that deliver important insights based on analysis of data resources. Similarly, process mining develops algorithms that use event data induced by real-world processes to support the modeling of – and hence understanding and long-term improvement of – those processes. In process mining, the quality of the learned process models is assessed using conformance checking techniques, which measure how well the models represent and generalize the data. This article presents the entropic relevance measure for confor..