Transition Adjacency Relation Computation Based on Unfolding: Potentials and Challenges

Transition Adjacency Relation (TAR) has provided a useful perspective for process model similarity measurement. Motivated by recent developments of other similarity metrics, this article puts TAR computation in the context of Petri net unfolding. Apart from being significantly faster than existing TAR computation algorithms, unfolding-based TAR computation also provides the potentials of enhancement through combination with other metrics that can be obtained from unfolding, especially the popular Behavior Profiles. We show that TAR computation can generally be reduced to coverability problem and solved using unfolding. However, there are also questions to be answered regarding how to further exploit unfolding information for optimal efficiency and handle silent transitions. In this article, we discuss what has been learned from our research, and also point out the open issues.