.png){kind=icon}
.png){kind=icon}
Return
Measuring rule-based LTLf process specifications: A probabilistic data-driven approach
.png){kind=icon}
OA
AI
DOI：10.1016/j.is.2023.102312.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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 for diverse process mining tasks, including discovery, checking, and drift detection.
Keywords:
Linear temporal logic
Declarative process mining
Specification mining
Probabilistic modeling
Statistical estimation
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.9
Papers:
2.8K
Citations:
1.8K
Organization
View more organizations