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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 datais click data: the digital trace of the actions of a user through the graphicalelements of an application, website or software. While readily available, clickdata is often missing a case notion: an attribute linking events from userinteractions to a specific process instance in the software. In this paper, wepropose a neural network-based technique to determine a case notion for clickdata, thus enabling process mining and other process analysis techniques onuser interaction data. We describe our method, show its scalability to datasetsof large dimensions, and we validate its efficacy through a user study based onthe segmented event log resulting from interaction data of a mobility sharingcompany. Interviews with domain experts in the company demonstrate that thecase notion obtained by our method can lead to actionable process insights.

Resolving Uncertain Case Identifiers in Interaction Logs: A User Study