Supervised Intentional Process Models Discovery using Hidden Markov Models

Ghazaleh Khodabandelou (1) , Charlotte Hug (1) , Rebecca Deneckere (1) , Camille Salinesi (1)

1 CRI - Centre de Recherche en Informatique de Paris 1

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' actors. However, these approaches only focused on activities and the process models identified are, in consequence, activity-oriented. Intentional process models focuses on intentions rather than activities, in order to offer a better guidance through the processes, based on the reasoning behind the activities. Unfortunately, the existing process-mining approaches do not take into account the hidden aspect of intentions behind the recorded users' 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). This paper focuses on a supervised approach that allows discovering the intentions behind the users' activities traces and to compare them to the prescribed intentional process model.

Keywords: intention mining; process modeling; supervised approach; process discovery

Domains: Other [cs.OH]

Ghazaleh Khodabandelou, Charlotte Hug, Rebecca Deneckere, Camille Salinesi. Supervised Intentional Process Models Discovery using Hidden Markov Models. Seventh International Conference on Research Challenges in Information Science, May 2013, Paris, France. pp.1-11, ⟨10.1109/RCIS.2013.6577711⟩. ⟨hal-00803875⟩