让日志轨迹匹配更可能的流程路径，避免误判罕见行为。
Stochastic Alignments: Matching an Observed Trace to Stochastic Process Models
- 用启发式算法找与日志最像且概率高的流程路径
- 相比传统方法，能识别出更符合实际发生的流程分支
- 适合流程分析员诊断异常或优化业务流程
流程挖掘通过从IT系统中提取事件数据，为组织的业务流程提供洞察。这些洞察得益于对业务流程中行为频率的显式建模，而这类建模由随机过程模型捕捉。给定一个观测轨迹和一个随机过程模型，传统的基于对齐的合规性检查技术存在根本局限：它们倾向于选择偏离最少的模型路径，但该路径可能并不常见。本文研究如何将观测轨迹与随机过程模型匹配，目标是识别一条与轨迹编辑距离低且概率较高的模型路径。我们将此问题建模为优化问题，并提出一种启发式引导的路径搜索算法求解。开源实现验证了该方法的可行性，表明其能为分析师提供新的、有价值的诊断洞察。
原文摘要 · Abstract (English)
Process mining leverages event data extracted from IT systems to generate insights into the business processes of organizations. Such insights benefit from explicitly considering the frequency of behavior in business processes, which is captured by stochastic process models. Given an observed trace and a stochastic process model, conventional alignment-based conformance checking techniques face a fundamental limitation: They prioritize matching the trace to a model path with minimal deviations, which may, however, lead to selecting an unlikely path. In this paper, we study the problem of matching an observed trace to a stochastic process model by identifying a likely model path with a low edit distance to the trace. We phrase this as an optimization problem and develop a heuristic-guided path-finding algorithm to solve it. Our open-source implementation demonstrates the feasibility of the approach and shows that it can provide new, useful diagnostic insights for analysts.
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