  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.
