Metaheuristic Optimization for Automated Business Process Discovery

The problem of automated discovery of process models from event logs has been intensely investigated in the past two decades, leading to a range of approaches that strike various trade-offs between accuracy, model complexity, and execution time. A few studies have suggested that the accuracy of automated process discovery approaches can be enhanced by using metaheuristic optimization. However, these studies have remained at the level of proposals without validation on real-life logs or they have only considered one metaheuristics in isolation. In this setting, this paper studies the following question: To what extent can the accuracy of automated process discovery approaches be improved by applying different optimization metaheuristics? To address this question, the paper proposes an approach to enhance automated process discovery approaches with metaheuristic optimization. The approach is instantiated to define an extension of a state-of-the-art automated process discovery approach, namely Split Miner. The paper compares the accuracy gains yielded by four optimization metaheuristics relative to each other and relative to state-of-the-art baselines, on a benchmark comprising 20 real-life logs. The results show that metaheuristic optimization improves the accuracy of Split Miner in a majority of cases, at the cost of execution times in the order of minutes, versus seconds for the base algorithm.