# Mining Uncertain Event Data in Process Mining

arXiv: https://arxiv.org/abs/1910.00089 · HTML: https://mlsanity.com/paper/1910.00089/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2019.

## Built on by (most cited first, 9 of 9)

- [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)
- [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)
- [Inferring Unobserved Events in Systems With Shared Resources and Queues](https://mlsanity.com/paper/2103.00167/) (2021)
- [A Systematic Literature Review on Process-Aware Recommender Systems](https://mlsanity.com/paper/2103.16654/) (2021)

## Abstract (arXiv)

Nowadays, more and more process data are automatically recorded by information systems, and made available in the form of event logs. Process mining techniques enable process-centric analysis of data, including automatically discovering process models and checking if event data conform to a certain model. In this paper we analyze the previously unexplored setting of uncertain event logs: logs where quantified uncertainty is recorded together with the corresponding data. 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 aligning an uncertain trace onto a regular process model.