Resolving Uncertain Case Identifiers in Interaction Logs: A User Study
This addresses a specific data preprocessing challenge for process mining in user interaction data, with incremental improvements for domain experts in mobility sharing companies.
The paper tackles the problem of missing case identifiers in click data, which prevents process mining, by proposing a neural network-based technique to determine a case notion, validated through a user study with domain experts showing it leads to actionable insights.
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.