用有向网展开计算部分有序对齐，更精准捕捉并发行为
FoldA: Computing Partial-Order Alignments Using Directed Net Unfoldings
- 基于有向佩特里网展开在线生成部分有序对齐
- 相比传统方法减少队列状态数，提升并发建模准确性
- 适合处理高并发、高选择性的真实流程数据
合规性检查是流程挖掘的基础任务，用于量化观测到的流程执行与规范流程模型之间的匹配程度。现有方法通过探索流程模型与轨迹构成的同步积所形成的状态空间来计算对齐，这常导致状态空间爆炸，尤其在模型具有高度选择性和并发性时。此外，由于对齐固有的顺序结构，无法充分表达许多真实流程中的并发行为。为此，本文提出一种新方法FoldA，利用有向佩特里网展开在线计算部分有序对齐。我们在485组合成模型-日志对上评估该技术，并与Astar和Dijkstra对齐方法在13个真实世界模型-日志对及6个基准对上进行比较。结果表明，尽管折叠对齐需要更多计算时间，但通常能显著减少队列状态数，并提供更准确的并发表示。
原文摘要 · Abstract (English)
Conformance checking is a fundamental task of process mining, which quantifies the extent to which the observed process executions match a normative process model. The state-of-the-art approaches compute alignments by exploring the state space formed by the synchronous product of the process model and the trace. This often leads to state space explosion, particularly when the model exhibits a high degree of choice and concurrency. Moreover, as alignments inherently impose a sequential structure, they fail to fully represent the concurrent behavior present in many real-world processes. To address these limitations, this paper proposes a new technique for computing partial-order alignments {on the fly using directed Petri net unfoldings, named FoldA. We evaluate our technique on 485 synthetic model-log pairs and compare it against Astar- and Dijkstra-alignments on 13 real-life model-log pairs and 6 benchmark pairs. The results show that our unfolding alignment, although it requires more computation time, generally reduces the number of queued states and provides a more accurate representation of concurrency.
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