# Ranking the Top-K Realizations of Stochastically Known Event Logs

arXiv: https://arxiv.org/abs/2410.00067 · HTML: https://mlsanity.com/paper/2410.00067/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 16 references that are in the map.
- First posted on arXiv 30 Sep 2024.

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

- [Conformance Checking over Uncertain Event Data](https://mlsanity.com/paper/2009.14452/) (2020)
- [Probability Estimation of Uncertain Process Trace Realizations](https://mlsanity.com/paper/2108.08615/) (2021)
- [SKTR: Trace Recovery from Stochastically Known Logs](https://mlsanity.com/paper/2206.12672/) (2022)
- [Process Mining for Unstructured Data: Challenges and Research Directions](https://mlsanity.com/paper/2401.13677/) (2023)
- [Probabilistic and Non-Deterministic Event Data in Process Mining: Embedding Uncertainty in Process Analysis Techniques](https://mlsanity.com/paper/2205.04827/) (2022)

## Abstract (arXiv)

Various kinds of uncertainty can occur in event logs, e.g., due to flawed recording, data quality issues, or the use of probabilistic models for activity recognition. Stochastically known event logs make these uncertainties transparent by encoding multiple possible realizations for events. However, the number of realizations encoded by a stochastically known log grows exponentially with its size, making exhaustive exploration infeasible even for moderately sized event logs. Thus, considering only the top-K most probable realizations has been proposed in the literature. In this paper, we implement an efficient algorithm to calculate a top-K realization ranking of an event log under event independence within O(Kn), where n is the number of uncertain events in the log. This algorithm is used to investigate the benefit of top-K rankings over top-1 interpretations of stochastically known event logs. Specifically, we analyze the usefulness of top-K rankings against different properties of the input data. We show that the benefit of a top-K ranking depends on the length of the input event log and the distribution of the event probabilities. The results highlight the potential of top-K rankings to enhance uncertainty-aware process mining techniques.