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.
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Notes
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For brevity, we directly apply the constraints by giving different prefixes the same probability parameters: there are 10 different splits, but we only define 5 probability distributions. In practice, equality constraints can be added in the final stage, yielding more flexibility in adding and removing specific constraints.
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Because of space limitations, the Bayesian model and resulting posterior distributions have not been included in this paper. Instead, we will look at two example use cases in the following section.
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Note that due to space limitations, we only show the posterior distributions of the variables of interest.
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Janssenswillen, G., Depaire, B., Faes, C. (2020). Enhancing Discovered Process Models Using Bayesian Inference and MCMC. In: Del Río Ortega, A., Leopold, H., Santoro, F.M. (eds) Business Process Management Workshops. BPM 2020. Lecture Notes in Business Information Processing, vol 397. Springer, Cham. https://doi.org/10.1007/978-3-030-66498-5_22
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