Supervised intentional process models discovery using Hidden Markov models

Abstract

Since several decades, discovering process models is a subject of interest in the Information System (IS) community. Approaches have been proposed to recover process models, based on the recorded sequential tasks (traces) done by IS's actors. However, these approaches only focused on activities and the process models identified are, in consequence, activity-oriented. Intentional process models focus on the intentions underlying activities rather than activities, in order to offer a better guidance through the processes. Unfortunately, the existing process-mining approaches do not take into account the hidden aspect of the intentions behind the recorded user activities. We think that we can discover the intentional process models underlying user activities by using Intention mining techniques. The aim of this paper is to propose the use of probabilistic models to evaluate the most likely intentions behind traces of activities, namely Hidden Markov Models (HMMs). We focus on this paper on a supervised approach that allows discovering the intentions behind the user activities traces and to compare them to the prescribed intentional process model.