An Experiment on Using Bayesian Networks for Process Mining

TL;DR

This paper introduces a novel process mining approach using Bayesian Networks to model and analyze business processes under uncertainty, allowing probabilistic inference of task sequences from incomplete event logs.

Contribution

It proposes a new Bayesian Network-based method for process mining that accounts for uncertainty and can learn probabilities automatically from data.

Findings

Bayesian Networks effectively model process uncertainty.

The approach enables probabilistic querying of business processes.

Experimental results show the method's adequacy in real case studies.

Abstract

Process mining is a technique that performs an automatic analysis of business processes from a log of events with the promise of understanding how processes are executed in an organisation. Several models have been proposed to address this problem, however, here we propose a different approach to deal with uncertainty. By uncertainty, we mean estimating the probability of some sequence of tasks occurring in a business process, given that only a subset of tasks may be observable. In this sense, this work proposes a new approach to perform process mining using Bayesian Networks. These structures can take into account the probability of a task being present or absent in the business process. Moreover, Bayesian Networks are able to automatically learn these probabilities through mechanisms such as the maximum likelihood estimate and EM clustering. Experiments made over a Loan Application Case study suggest that Bayesian Networks are adequate structures for process mining and enable a deep analysis of the business process model that can be used to answer queries about that process.