Probabilistic and Non-Deterministic Event Data in Process Mining: Embedding Uncertainty in Process Analysis Techniques
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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.
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
10 of 16 references.
References · 10
- 2019Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methodscited by 913
- 2020Conformance Checking over Uncertain Event Datacited by 13
- 2019Mining Uncertain Event Data in Process Miningcited by 9
- 2019Discovering Process Models from Uncertain Event Datacited by 8
- 2021Uncertain Process Data with Probabilistic Knowledge: Problem Characterization and Challengescited by 7
- 2020Efficient Construction of Behavior Graphs for Uncertain Event Datacited by 6
- 2020Efficient Time and Space Representation of Uncertain Event Datacited by 6
- 2021Probability Estimation of Uncertain Process Trace Realizationscited by 6
- 2021PROVED: A Tool for Graph Representation and Analysis of Uncertain Event Datacited by 5
- 2022An XES Extension for Uncertain Event Datacited by 1
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Citing papers per year: 2023: 1; 2024: 1; 2025: 0; 2026: 1.
3 papers.