# SKTR: Trace Recovery from Stochastically Known Logs

arXiv: https://arxiv.org/abs/2206.12672 · HTML: https://mlsanity.com/paper/2206.12672/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 27 references that are in the map.
- First posted on arXiv 25 Jun 2022.

## Builds on (most cited first, 9 of 9)

- [ASFormer: Transformer for Action Segmentation](https://mlsanity.com/paper/2110.08568/) (2021)
- [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)
- [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)
- [Efficient Construction of Behavior Graphs for Uncertain Event Data](https://mlsanity.com/paper/2002.08225/) (2020)
- [Efficient Time and Space Representation of Uncertain Event Data](https://mlsanity.com/paper/2010.00334/) (2020)
- [Partial Order Resolution of Event Logs for Process Conformance Checking](https://mlsanity.com/paper/2007.02416/) (2020)

## Built on by (most cited first, 4 of 4)

- [Process Mining for Unstructured Data: Challenges and Research Directions](https://mlsanity.com/paper/2401.13677/) (2023)
- [Ranking the Top-K Realizations of Stochastically Known Event Logs](https://mlsanity.com/paper/2410.00067/) (2024)
- [Conformance Checking for Less: Efficient Conformance Checking for Long Event Sequences](https://mlsanity.com/paper/2505.21506/) (2025)
- [DDTR: Diffusion Denoising Trace Recovery](https://mlsanity.com/paper/2510.22553/) (2025)

## Abstract (arXiv)

Developments in machine learning together with the increasing usage of sensor data challenge the reliance on deterministic logs, requiring new process mining solutions for uncertain, and in particular stochastically known, logs. In this work we formulate {trace recovery}, the task of generating a deterministic log from stochastically known logs that is as faithful to reality as possible. An effective trace recovery algorithm would be a powerful aid for maintaining credible process mining tools for uncertain settings. We propose an algorithmic framework for this task that recovers the best alignment between a stochastically known log and a process model, with three innovative features. Our algorithm, SKTR, 1) handles both Markovian and non-Markovian processes; 2) offers a quality-based balance between a process model and a log, depending on the available process information, sensor quality, and machine learning predictiveness power; and 3) offers a novel use of a synchronous product multigraph to create the log. An empirical analysis using five publicly available datasets, three of which use predictive models over standard video capturing benchmarks, shows an average relative accuracy improvement of more than 10 over a common baseline.