Automated process discovery from event logs is a key component of process mining, allowing companies to acquire meaningful insights into their business processes. Despite significant research, present methods struggle to balance important quality dimensions: fitness, precision, generalization, and complexity, but is limited when dealing with complex loop structures. This paper introduces Bonita Miner, a novel approach to process model discovery that generates behaviorally accurate Business Process Model and Notation (BPMN) diagrams. Bonita Miner incorporates an advanced filtering mechanism for Directly Follows Graphs (DFGs) alongside innovative algorithms designed to capture concurrency, splits, and loops, effectively addressing limitations of balancing as much as possible these four metrics, either there exists a loop, which challenge in existing works. Our approach produces models that are simpler and more reflective of the behavior of real-world processes, including complex loop dynamics. Empirical evaluations using real-world event logs demonstrate that Bonita Miner outperforms existing methods in fitness, precision, and generalization, while maintaining low model complexity.

翻译：从事件日志中自动发现过程是过程挖掘的关键组成部分，它使企业能够获取对其业务流程的有意义洞察。尽管已有大量研究，现有方法仍难以在关键质量维度之间取得平衡：拟合度、精确度、泛化性和复杂性，且在处理复杂循环结构时存在局限。本文介绍Bonita Miner，这是一种生成行为准确的业务流程模型与标记法（BPMN）图的过程模型发现新方法。Bonita Miner结合了直接跟随图（DFG）的高级过滤机制与旨在捕获并发、分支和循环的创新算法，有效解决了现有工作中面临的平衡这四个指标（尤其当存在循环时）的挑战。我们的方法生成的模型更简洁，更能反映实际过程的行为，包括复杂的循环动态。使用真实事件日志进行的实证评估表明，Bonita Miner在拟合度、精确度和泛化性方面优于现有方法，同时保持较低的模型复杂性。