  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.
