Process discovery algorithms traditionally linearize events, failing to capture the inherent concurrency of real-world processes. While some techniques can handle partially ordered data, they often struggle with scalability on large event logs. We introduce a novel, scalable algorithm that directly leverages partial orders in process discovery. Our approach derives partially ordered traces from event data and aggregates them into a sound-by-construction, perfectly fitting process model. Our hierarchical algorithm preserves inherent concurrency while systematically abstracting exclusive choices and loop patterns, enhancing model compactness and precision. We have implemented our technique and demonstrated its applicability on complex real-life event logs. Our work contributes a scalable solution for a more faithful representation of process behavior, especially when concurrency is prevalent in event data.

翻译：传统的过程发现算法将事件数据线性化，未能捕捉真实业务流程中的固有并发性。尽管某些技术能够处理部分有序数据，但在大规模事件日志上常面临可扩展性问题。我们提出了一种新颖且可扩展的算法，该算法直接利用部分序关系进行过程发现。我们的方法从事件数据中提取部分有序轨迹，并将其聚合成一个结构健全且完美拟合的过程模型。该层次化算法在系统抽象排他性选择与循环模式的同时，保留了固有并发性，提升了模型的紧凑性与精确度。我们实现了该技术，并展示了其在复杂真实事件日志上的适用性。本研究为更忠实地表征过程行为（尤其在事件数据中并发性普遍存在时）提供了一种可扩展的解决方案。