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：声明式过程规约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，这些规约从信息系统记录的多重运行集（即事件日志）中推断并校验。为此，衡量过程数据符合规约的程度至关重要。然而，现有挖掘与验证技术孤立地分析规则，忽视了规则间的交互影响。本文提出一种框架，用于构建声明式过程规约的概率度量方法。基于此，我们提出一种技术，可度量事件日志对规约的满足程度。通过使用真实世界数据评估该方法，我们验证了其在过程发现、合规校验及漂移检测场景中的适用性。