# Conformance Checking over Uncertain Event Data

arXiv: https://arxiv.org/abs/2009.14452 · HTML: https://mlsanity.com/paper/2009.14452/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Sep 2020.

## Built on by (most cited first, 13 of 13)

- [Uncertain Process Data with Probabilistic Knowledge: Problem Characterization and Challenges](https://mlsanity.com/paper/2106.03324/) (2021)
- [Conformance Checking Over Stochastically Known Logs](https://mlsanity.com/paper/2203.07507/) (2022)
- [Probability Estimation of Uncertain Process Trace Realizations](https://mlsanity.com/paper/2108.08615/) (2021)
- [PROVED: A Tool for Graph Representation and Analysis of Uncertain Event Data](https://mlsanity.com/paper/2103.05564/) (2021)
- [Conformance Checking of Fuzzy Logs against Declarative Temporal Specifications](https://mlsanity.com/paper/2406.12078/) (2024)
- [Conformance Checking with Uncertainty via SMT (Extended Version)](https://mlsanity.com/paper/2206.07461/) (2022)
- [SKTR: Trace Recovery from Stochastically Known Logs](https://mlsanity.com/paper/2206.12672/) (2022)
- [Probabilistic and Non-Deterministic Event Data in Process Mining: Embedding Uncertainty in Process Analysis Techniques](https://mlsanity.com/paper/2205.04827/) (2022)
- [Process Mining on Uncertain Event Data](https://mlsanity.com/paper/2204.04148/) (2022)
- [Uncertain Case Identifiers in Process Mining: A User Study of the Event-Case Correlation Problem on Click Data](https://mlsanity.com/paper/2204.04164/) (2022)
- [Alignment-based conformance checking over probabilistic events](https://mlsanity.com/paper/2209.04309/) (2022)
- [Ranking the Top-K Realizations of Stochastically Known Event Logs](https://mlsanity.com/paper/2410.00067/) (2024)
- [All Eyes on the Workflow: Automated and Efficient Event Discovery from Video Streams](https://mlsanity.com/paper/2604.22476/) (2026)

## Abstract (arXiv)

The strong impulse to digitize processes and operations in companies and enterprises have resulted in the creation and automatic recording of an increasingly large amount of process data in information systems. These are made available in the form of event logs. Process mining techniques enable the process-centric analysis of data, including automatically discovering process models and checking if event data conform to a given model. In this paper, we analyze the previously unexplored setting of uncertain event logs. In such event logs uncertainty is recorded explicitly, i.e., the time, activity and case of an event may be unclear or imprecise. In this work, we define a taxonomy of uncertain event logs and models, and we examine the challenges that uncertainty poses on process discovery and conformance checking. Finally, we show how upper and lower bounds for conformance can be obtained by aligning an uncertain trace onto a regular process model.