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Intention-oriented process mining is based on the belief that the fundamentalnature of processes is mostly intentional (unlike activity-oriented process)and aims at discovering strategy and intentional process models from event-logsrecorded during the process enactment. In this paper, we present an applicationof intention-oriented process mining for the domain of incident management ofan Information Technology Infrastructure Library (ITIL) process. We apply theMap Miner Method (MMM) on a large real-world dataset for discovering hidden andunobservable user behavior, strategies and intentions. We first discover userstrategies from the given activity sequence data by applying Hidden MarkovModel (HMM) based unsupervised learning technique. We then process the emissionand transition matrices of the discovered HMM to generate a coarse-grained MapProcess Model. We present the first application or study of the new andemerging field of Intention-oriented process mining on an incident managementevent-log dataset and discuss its applicability, effectiveness and challenges.

Intention-Oriented Process Model Discovery from Incident Management Event Logs