Hidden Markov Model for Process Mining of Parallel Business Processes
One of all the works on process mining is the process discovery which produces a representation of a parallel business process. This representation is called process model and it consists of sequence and parallel control flow patterns. The parallel control flow patterns contain XOR, AND, and OR relations. Hidden Markov Model is rarely used to represent a process model since XOR, AND and OR relations are not visible. In Hidden Markov Model, the control flow patterns are represented by probabilities of state transitions. This research proposes an algorithm consisting in a process discovery based on Hidden Markov Model. This algorithm contains equations and rules: the equations are used to differentiate XOR, AND, and OR relations, while the rules are used to establish the process model utilizing detected control flow patterns. The experiment results show that the proposed algorithm obtain the right control flow patterns in the process model. The paper demonstrates that the fitness of process models obtained by the proposed algorithm are relatively higher respect to those obtained by Heuristics Miner and Time based Heuristics Miner algorithms. This paper also shows that the validity of process models obtained by the proposed algorithm are better than those obtained by other algorithms.