# Efficient Time and Space Representation of Uncertain Event Data

arXiv: https://arxiv.org/abs/2010.00334 · HTML: https://mlsanity.com/paper/2010.00334/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2020.

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

- [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)
- [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)

## Abstract (arXiv)

Process mining is a discipline which concerns the analysis of execution data of operational processes, the extraction of models from event data, the measurement of the conformance between event data and normative models, and the enhancement of all aspects of processes. Most approaches assume that event data is accurately capture behavior. However, this is not realistic in many applications: data can contain uncertainty, generated from errors in recording, imprecise measurements, and other factors. Recently, new methods have been developed to analyze event data containing uncertainty; these techniques prominently rely on representing uncertain event data by means of graph-based models explicitly capturing uncertainty. In this paper, we introduce a new approach to efficiently calculate a graph representation of the behavior contained in an uncertain process trace. We present our novel algorithm, prove its asymptotic time complexity, and show experimental results that highlight order-of-magnitude performance improvements for the behavior graph construction.