The problem of automatically discovering business process models from event logs has been intensely investigated in the past two decades, leading to a wide 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 means of metaheuristic optimization techniques. However, these studies have remained at the level of proposals without validation on real-life datasets or they have only considered one metaheuristic in isolation. This article presents a metaheuristic optimization framework for automated process discovery. The key idea of the framework is to construct a directly-follows graph (DFG) from the event log, to perturb this DFG so as to generate new candidate solutions, and to apply a DFG-based automated process discovery approach in order to derive a process model from each DFG. The framework can be instantiated by linking it to an automated process discovery approach, an optimization metaheuristic, and the quality measure to be optimized (e.g., fitness, precision, F-score). The article considers several instantiations of the framework corresponding to four optimization metaheuristics, three automated process discovery approaches (Inductive Miner—directly-follows, Fodina, and Split Miner), and one accuracy measure (Markovian F-score). These framework instances are compared using a set of 20 real-life event logs. The evaluation shows that metaheuristic optimization consistently yields visible improvements in F-score for all the three automated process discovery approaches, at the cost of execution times in the order of minutes, versus seconds for the baseline approaches.

在过去的二十年中，从事件日志中自动发现业务流程模型的问题得到了深入的研究，从而导致了各种各样的方法，这些方法在准确性，模型复杂性和执行时间之间进行了各种取舍。一些研究表明，可以通过元启发式优化技术来提高自动化过程发现方法的准确性。但是，这些研究仍处于建议水平，而未在实际数据集上进行验证，或者仅考虑了一种元启发式方法。本文介绍了一种用于自动过程发现的元启发式优化框架。该框架的主要思想是从事件日志中构造一个直接关注图（DFG），以扰动该DFG以便生成新的候选解决方案，并应用基于DFG的自动过程发现方法，以便从每个DFG导出过程模型。可以通过将框架链接到自动化过程发现方法，优化元启发式方法和要优化的质量度量（例如适应性，精度，F评分）来实例化该框架。本文考虑了与四种优化元启发式方法，三种自动化过程发现方法（感应矿工-直接关注，Fodina和Split Miner）相对应的框架的几种实例化，以及一种准确性测度（马尔可夫F评分）。使用一组20个真实事件日志对这些框架实例进行比较。评估显示，对于所有三种自动化过程发现方法，元启发式优化始终在F分数方面产生明显的改进，