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

arXiv: https://arxiv.org/abs/2305.05418 · HTML: https://mlsanity.com/paper/2305.05418/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 81 references that are in the map.
- First posted on arXiv 9 May 2023.

## Builds on (most cited first, 7 of 7)

- [Comprehensive Process Drift Detection with Visual Analytics](https://mlsanity.com/paper/1907.06386/) (2019)
- [Detecting sudden and gradual drifts in business processes from execution traces](https://mlsanity.com/paper/2005.04016/) (2020)
- [A Tutorial on Statistically Sound Pattern Discovery](https://mlsanity.com/paper/1709.03904/) (2017)
- [Evaluating Conformance Measures in Process Mining using Conformance Propositions (Extended version)](https://mlsanity.com/paper/1909.02393/) (2019)
- [LTLf satisfiability checking](https://mlsanity.com/paper/1403.1666/) (2014)
- [Temporal Logics Over Finite Traces with Uncertainty (Technical Report)](https://mlsanity.com/paper/1903.04940/) (2019)
- [Visual Drift Detection for Sequence Data Analysis of Business Processes](https://mlsanity.com/paper/2011.09130/) (2020)

## Abstract (arXiv)

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.