# Probabilistic and Non-Deterministic Event Data in Process Mining: Embedding Uncertainty in Process Analysis Techniques

arXiv: https://arxiv.org/abs/2205.04827 · HTML: https://mlsanity.com/paper/2205.04827/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 16 references that are in the map.
- First posted on arXiv 10 May 2022.

## Builds on (most cited first, 10 of 10)

- [Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods](https://mlsanity.com/paper/1910.09457/) (2019)
- [Conformance Checking over Uncertain Event Data](https://mlsanity.com/paper/2009.14452/) (2020)
- [Mining Uncertain Event Data in Process Mining](https://mlsanity.com/paper/1910.00089/) (2019)
- [Discovering Process Models from Uncertain Event Data](https://mlsanity.com/paper/1909.11567/) (2019)
- [Uncertain Process Data with Probabilistic Knowledge: Problem Characterization and Challenges](https://mlsanity.com/paper/2106.03324/) (2021)
- [Efficient Construction of Behavior Graphs for Uncertain Event Data](https://mlsanity.com/paper/2002.08225/) (2020)
- [Efficient Time and Space Representation of Uncertain Event Data](https://mlsanity.com/paper/2010.00334/) (2020)
- [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)
- [An XES Extension for Uncertain Event Data](https://mlsanity.com/paper/2204.04135/) (2022)

## Built on by (most cited first, 3 of 3)

- [Process Mining for Unstructured Data: Challenges and Research Directions](https://mlsanity.com/paper/2401.13677/) (2023)
- [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)

Process mining is a subfield of process science that analyzes event data collected in databases called event logs. Recently, novel types of event data have become of interest due to the wide industrial application of process mining analyses. In this paper, we examine uncertain event data. Such data contain meta-attributes describing the amount of imprecision tied with attributes recorded in an event log. We provide examples of uncertain event data, present the state of the art in regard of uncertainty in process mining, and illustrate open challenges related to this research direction.