Computing Alignments of Event Data and Process Models

The aim of conformance checking is to assess whether a process model and event data, recorded in an event log, conform to each other. In recent years, alignments have proven extremely useful for calculating conformance statistics. Computing optimal alignments is equivalent to solving a shortest path problem on the state space of the synchronous product net of a process model and event data. State-of-the-art alignment based conformance checking implementations exploit the \(A^* \)-algorithm, a heuristic search method for shortest path problems, and include a wide range of parameters that likely influence their performance. In previous work, we presented a preliminary and exploratory analysis of the effect of these parameters. This paper extends the aforementioned work by means of large-scale statistically-sound experiments that describe the effects and trends of these parameters for different populations of process models. Our results show that, indeed, there exist parameter configurations that have a significant positive impact on alignment computation efficiency.