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