Computation of alignments of business processes through relaxation labeling and local optimal search

A fundamental problem in conformance checking is aligning event data with process models. Unfortunately, existing techniques for this task are either complex, or can only be applicable to restricted classes of models. This in practice means that for large inputs, current techniques often fail to produce a result. In this paper we propose a method to compute alignments for unconstrained process models, which relies on the use of relaxation labeling techniques on top of a partial order representation of the process model. The technique proposed in this paper precomputes information used in the search for alignments, and is able to produce real alignments that may be close to optimal ones by combining the aforementioned techniques with a locally applied A∗ strategy. Remarkably, the implementation on the proposed technique achieves a speed-up of several orders of magnitude with respect to the approaches in the literature (either optimal, sup-optimal or approximate), often with a reasonable trade-off on the cost of the obtained alignment.