# Conformance Checking of Fuzzy Logs against Declarative Temporal Specifications

arXiv: https://arxiv.org/abs/2406.12078 · HTML: https://mlsanity.com/paper/2406.12078/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 34 references that are in the map.
- First posted on arXiv 17 Jun 2024.

## Builds on (most cited first, 9 of 9)

- [The AVA-Kinetics Localized Human Actions Video Dataset](https://mlsanity.com/paper/2005.00214/) (2020)
- [Elaborating on Learned Demonstrations with Temporal Logic Specifications](https://mlsanity.com/paper/2002.00784/) (2020)
- [Conformance Checking over Uncertain Event Data](https://mlsanity.com/paper/2009.14452/) (2020)
- [Monitoring Constraints and Metaconstraints with Temporal Logics on Finite Traces](https://mlsanity.com/paper/2004.01859/) (2020)
- [Conformance Checking Over Stochastically Known Logs](https://mlsanity.com/paper/2203.07507/) (2022)
- [Probabilistic Trace Alignment](https://mlsanity.com/paper/2107.03997/) (2021)
- [Shedding Light on Blind Spots: Developing a Reference Architecture to Leverage Video Data for Process Mining](https://mlsanity.com/paper/2010.11289/) (2020)
- [TinyAction Challenge: Recognizing Real-world Low-resolution Activities in Videos](https://mlsanity.com/paper/2107.11494/) (2021)
- [Conformance Checking with Uncertainty via SMT (Extended Version)](https://mlsanity.com/paper/2206.07461/) (2022)

## Built on by (most cited first, 5 of 5)

- [Neuro-Symbolic Predictive Process Monitoring](https://mlsanity.com/paper/2509.00834/) (2025)
- [T-ILR: a Neurosymbolic Integration for LTLf](https://mlsanity.com/paper/2508.15943/) (2025)
- [Grounding LTL Tasks in Sub-Symbolic RL Environments for Zero-Shot Generalization](https://mlsanity.com/paper/2602.09761/) (2026)
- [DIFF-ERO: A Conformance-Aware Loss for Deep Learning in Process Mining](https://mlsanity.com/paper/2606.14283/) (2026)
- [DeepDFA: Injecting Temporal Logic in Deep Learning for Sequential Subsymbolic Applications](https://mlsanity.com/paper/2602.03486/) (2026)

## Abstract (arXiv)

Traditional conformance checking tasks assume that event data provide a faithful and complete representation of the actual process executions. This assumption has been recently questioned: more and more often events are not traced explicitly, but are instead indirectly obtained as the result of event recognition pipelines, and thus inherently come with uncertainty. In this work, differently from the typical probabilistic interpretation of uncertainty, we consider the relevant case where uncertainty refers to which activity is actually conducted, under a fuzzy semantics. In this novel setting, we consider the problem of checking whether fuzzy event data conform with declarative temporal rules specified as Declare patterns or, more generally, as formulae of linear temporal logic over finite traces (LTLf). This requires to relax the assumption that at each instant only one activity is executed, and to correspondingly redefine boolean operators of the logic with a fuzzy semantics. Specifically, we provide a threefold contribution. First, we define a fuzzy counterpart of LTLf tailored to our purpose. Second, we cast conformance checking over fuzzy logs as a verification problem in this logic. Third, we provide a proof-of-concept, efficient implementation based on the PyTorch Python library, suited to check conformance of multiple fuzzy traces at once.