Declarative process specifications define the behavior of processes by means of rules based on Linear Temporal Logic on Finite Traces (LTLf). In a mining context, these specifications are inferred from, and checked on, multi-sets of runs recorded by information systems (namely, event logs). To this end, being able to gauge the degree to which process data comply with a specification is key. However, existing mining and verification techniques analyze the rules in isolation, thereby disregarding their interplay. In this paper, we introduce a framework to devise probabilistic measures for declarative process specifications. Thereupon, we propose a technique that measures the degree of satisfaction of specifications over event logs. To assess our approach, we conduct an evaluation with real-world data, evidencing its applicability in discovery, checking, and drift detection contexts.

翻译：声明式过程规范通过基于有限迹线性时序逻辑（LTLf）的规则定义过程行为。在挖掘场景中，这些规范从信息系统记录的多集运行轨迹（即事件日志）中推断并验证。因此，能够衡量过程数据对规范的符合程度至关重要。然而，现有挖掘与验证技术孤立地分析规则，忽视了规则间的相互作用。本文提出一套框架，用于构建声明式过程规范的概率度量方法，并在此基础上提出一种技术来度量事件日志对规范的满足程度。为评估该方法，我们使用真实数据开展实验，验证了其在过程发现、合规性检查与漂移检测场景中的适用性。