CHMM for Discovering Intentional Process Model From Event Logs by Considering Sequence of Activities

Abstract

An intentional process model is known to analyze processes deeply and provide recommendations for the upcoming processes. Nevertheless, the discovery of intentions is a difficult task because the intentions are not recorded in the event log, but they encourage the executable activities in the event log. Map Miner is the latest algorithm to depict the intentional process model. A disadvantage of this algorithm is the inability to determine strategies that contain same activities with the different sequence with other strategies. This disadvantage leads failure on the intentional process model. This research proposes an algorithm for discovering an intentional process model by considering the sequence of activities and CHMM (Coupled Hidden Markov Model). The probabilities and states of CHMM are utilized for the formation of the intentional process model. The experiment shows that the proposed algorithm with considering the sequence of activities gets an appropriate intentional process model. It also demonstrates that an obtained intentional process model using proposed algorithm gets the better validity than an intentional process model using Map Miner Method.