# Probability Estimation of Uncertain Process Trace Realizations

arXiv: https://arxiv.org/abs/2108.08615 · HTML: https://mlsanity.com/paper/2108.08615/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 11 references that are in the map.
- First posted on arXiv 19 Aug 2021.

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

- [Process Mining for Python (PM4Py): Bridging the Gap Between Process- and Data Science](https://mlsanity.com/paper/1905.06169/) (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)
- [Partial Order Resolution of Event Logs for Process Conformance Checking](https://mlsanity.com/paper/2007.02416/) (2020)

## Built on by (most cited first, 6 of 6)

- [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)
- [Resolving Uncertain Case Identifiers in Interaction Logs: A User Study](https://mlsanity.com/paper/2212.00009/) (2022)
- [Process Modeling and Conformance Checking in Healthcare: A COVID-19 Case Study](https://mlsanity.com/paper/2209.10897/) (2022)
- [Ranking the Top-K Realizations of Stochastically Known Event Logs](https://mlsanity.com/paper/2410.00067/) (2024)

## Abstract (arXiv)

Process mining is a scientific discipline that analyzes event data, often collected in databases called event logs. Recently, uncertain event logs have become of interest, which contain non-deterministic and stochastic event attributes that may represent many possible real-life scenarios. In this paper, we present a method to reliably estimate the probability of each of such scenarios, allowing their analysis. Experiments show that the probabilities calculated with our method closely match the true chances of occurrence of specific outcomes, enabling more trustworthy analyses on uncertain data.