# Resolving Uncertain Case Identifiers in Interaction Logs: A User Study

arXiv: https://arxiv.org/abs/2212.00009 · HTML: https://mlsanity.com/paper/2212.00009/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 47 references that are in the map.
- First posted on arXiv 21 Nov 2022.

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

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [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)
- [Identifying candidate routines for Robotic Process Automation from unsegmented UI logs](https://mlsanity.com/paper/2008.05782/) (2020)
- [Event-Case Correlation for Process Mining using Probabilistic Optimization](https://mlsanity.com/paper/2206.10009/) (2022)
- [Process Mining on Uncertain Event Data](https://mlsanity.com/paper/2204.04148/) (2022)
- [Uncertain Case Identifiers in Process Mining: A User Study of the Event-Case Correlation Problem on Click Data](https://mlsanity.com/paper/2204.04164/) (2022)
- [Removing Operational Friction Using Process Mining: Challenges Provided by the Internet of Production (IoP)](https://mlsanity.com/paper/2107.13066/) (2021)

## Built on by (most cited first, 2 of 2)

- [Performance-Preserving Event Log Sampling for Predictive Monitoring](https://mlsanity.com/paper/2301.07624/) (2023)
- [TGCM: Topic-Guided Consistency Modeling for One-Step Disentanglement of Interleaved APT Technique Sequences](https://mlsanity.com/paper/2606.18651/) (2026)

## Abstract (arXiv)

Modern software systems are able to record vast amounts of user actions, stored for later analysis. One of the main types of such user interaction data is click data: the digital trace of the actions of a user through the graphical elements of an application, website or software. While readily available, click data is often missing a case notion: an attribute linking events from user interactions to a specific process instance in the software. In this paper, we propose a neural network-based technique to determine a case notion for click data, thus enabling process mining and other process analysis techniques on user interaction data. We describe our method, show its scalability to datasets of large dimensions, and we validate its efficacy through a user study based on the segmented event log resulting from interaction data of a mobility sharing company. Interviews with domain experts in the company demonstrate that the case notion obtained by our method can lead to actionable process insights.