Uncertain Case Identifiers in Process Mining: A User Study of the Event-Case Correlation Problem on Click Data
This addresses the event-case correlation problem for process mining analysts using click data, but it is incremental as it builds on existing sessionization techniques.
The paper tackled the problem of missing case identifiers in click data for process mining by applying a novel neural network method to aggregate user interactions into sessions as cases, validated through expert interviews.
Among the many sources of event data available today, a prominent one is user interaction data. User activity may be recorded during the use of an application or website, resulting in a type of user interaction data often called click data. An obstacle to the analysis of click data using process mining is the lack of a case identifier in the data. In this paper, we show a case and user study for event-case correlation on click data, in the context of user interaction events from a mobility sharing company. To reconstruct the case notion of the process, we apply a novel method to aggregate user interaction data in separate user sessions-interpreted as cases-based on neural networks. To validate our findings, we qualitatively discuss the impact of process mining analyses on the resulting well-formed event log through interviews with process experts.