Enhancing Discovered Process Models Using Bayesian Inference and MCMC

Abstract

Process mining is an innovative research field aimed at extracting useful information about business processes from event data. An important task herein is process discovery. The results of process discovery are mainly non-stochastic process models, which do not convey a notion of probability or uncertainty. In this paper, Bayesian inference and Markov Chain Monte Carlo is used to build a statistical model on top of a process model using event data, which is able to generate probability distributions for choices in a process' control-flow. A generic algorithm to build such a model is presented, and it is shown how the resulting statistical model can be used to test different kinds of hypotheses. The algorithm supports the enhancement of discovered process models by exposing probabilistic dependencies, and allows to compare the quality among different models, each of which provides important advancements in the field of process discovery.