# Conformance Checking Over Stochastically Known Logs

arXiv: https://arxiv.org/abs/2203.07507 · HTML: https://mlsanity.com/paper/2203.07507/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 16 references that are in the map.
- First posted on arXiv 14 Mar 2022.

## Builds on (most cited first, 7 of 7)

- [Unsupervised Learning and Segmentation of Complex Activities from Video](https://mlsanity.com/paper/1803.09490/) (2018)
- [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)
- [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)

## Built on by (most cited first, 7 of 7)

- [Conformance Checking of Fuzzy Logs against Declarative Temporal Specifications](https://mlsanity.com/paper/2406.12078/) (2024)
- [SKTR: Trace Recovery from Stochastically Known Logs](https://mlsanity.com/paper/2206.12672/) (2022)
- [Data-driven project planning: An integrated network learning and constraint relaxation approach in favor of scheduling](https://mlsanity.com/paper/2311.11542/) (2023)
- [Alignment-based conformance checking over probabilistic events](https://mlsanity.com/paper/2209.04309/) (2022)
- [A Scalable and Near-Optimal Conformance Checking Approach for Long Traces](https://mlsanity.com/paper/2406.05439/) (2024)
- [Conformance Checking for Less: Efficient Conformance Checking for Long Event Sequences](https://mlsanity.com/paper/2505.21506/) (2025)
- [Stochastic Alignments: Matching an Observed Trace to Stochastic Process Models](https://mlsanity.com/paper/2507.06472/) (2025)

## Abstract (arXiv)

With the growing number of devices, sensors and digital systems, data logs may become uncertain due to, e.g., sensor reading inaccuracies or incorrect interpretation of readings by processing programs. At times, such uncertainties can be captured stochastically, especially when using probabilistic data classification models. In this work we focus on conformance checking, which compares a process model with an event log, when event logs are stochastically known. Building on existing alignment-based conformance checking fundamentals, we mathematically define a stochastic trace model, a stochastic synchronous product, and a cost function that reflects the uncertainty of events in a log. Then, we search for an optimal alignment over the reachability graph of the stochastic synchronous product for finding an optimal alignment between a model and a stochastic process observation. Via structured experiments with two well-known process mining benchmarks, we explore the behavior of the suggested stochastic conformance checking approach and compare it to a standard alignment-based approach as well as to an approach that creates a lower bound on performance. We envision the proposed stochastic conformance checking approach as a viable process mining component for future analysis of stochastic event logs.