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.

本文介绍了一种为声明性流程规范设计概率测量的框架。提出了一种测量事件日志上规范满足度的技术，并使用真实数据进行了评估，证明了它在发现、检查和漂移检测方面的适用性。

基于规则的 LTLf 过程规范的测量：一种基于概率的数据驱动方法

Measuring Rule-based LTLf Process Specifications: A Probabilistic  Data-driven Approach

We address the problem of measuring inconsistency in declarative process
specifications, with an emphasis on linear temporal logic on fixed traces
(LTLff). As we will show, existing inconsistency measures for classical logic
cannot provide a meaningful assessment of inconsistency in LTL in general, as
they cannot adequately handle the temporal operators. We therefore propose a
novel paraconsistent semantics as a framework for inconsistency measurement. We
then present two new inconsistency measures based on these semantics and show
that they satisfy important desirable properties. We show how these measures
can be applied to declarative process models and investigate the computational
complexity of the introduced approach.

这篇研究论文解决了测量申明性进程规范不一致性的问题，重点介绍了固定跟踪（LTLff）的线性时间逻辑的不一致性，提出了一种新的不完全一致语义作为不一致性测量的框架。提出两种基于这些语义的不一致性度量，并研究了引入方法的计算复杂性。