# Conformance Checking with Uncertainty via SMT (Extended Version)

arXiv: https://arxiv.org/abs/2206.07461 · HTML: https://mlsanity.com/paper/2206.07461/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 24 references that are in the map.
- First posted on arXiv 15 Jun 2022.

## Builds on (most cited first, 5 of 5)

- [Conformance Checking over Uncertain Event Data](https://mlsanity.com/paper/2009.14452/) (2020)
- [Probabilistic Trace Alignment](https://mlsanity.com/paper/2107.03997/) (2021)
- [Optimization Modulo Theories with Linear Rational Costs](https://mlsanity.com/paper/1410.6039/) (2014)
- [CoCoMoT: Conformance Checking of Multi-Perspective Processes via SMT (Extended Version)](https://mlsanity.com/paper/2103.10507/) (2021)
- [Process Mining on Uncertain Event Data](https://mlsanity.com/paper/2204.04148/) (2022)

## Built on by (most cited first, 4 of 4)

- [Conformance Checking of Fuzzy Logs against Declarative Temporal Specifications](https://mlsanity.com/paper/2406.12078/) (2024)
- [Alignment-based conformance checking over probabilistic events](https://mlsanity.com/paper/2209.04309/) (2022)
- [Object-Centric Conformance Alignments with Synchronization (Extended Version)](https://mlsanity.com/paper/2312.08537/) (2023)
- [Data Petri Nets meet Probabilistic Programming (Extended version)](https://mlsanity.com/paper/2406.11883/) (2024)

## Abstract (arXiv)

Logs of real-life processes often feature uncertainty pertaining the recorded timestamps, data values, and/or events. We consider the problem of checking conformance of uncertain logs against data-aware reference processes. Specifically, we show how to solve it via SMT encodings, lifting previous work on data-aware SMT-based conformance checking to this more sophisticated setting. Our approach is modular, in that it homogeneously accommodates for different types of uncertainty. Moreover, using appropriate cost functions, different conformance checking tasks can be addressed. We show the correctness of our approach and witness feasibility through a proof-of-concept implementation.