  Event sequence data is increasingly available in various application domains,
such as business process management, software engineering, or medical pathways.
Processes in these domains are typically represented as process diagrams or
flow charts. So far, various techniques have been developed for automatically
generating such diagrams from event sequence data. An open challenge is the
visual analysis of drift phenomena when processes change over time. In this
paper, we address this research gap. Our contribution is a system for
fine-granular process drift detection and corresponding visualizations for
event logs of executed business processes. We evaluated our system both on
synthetic and real-world data. On synthetic logs, we achieved an average
F-score of 0.96 and outperformed all the state-of-the-art methods. On
real-world logs, we identified all types of process drifts in a comprehensive
manner. Finally, we conducted a user study highlighting that our visualizations
are easy to use and useful as perceived by process mining experts. In this way,
our work contributes to research on process mining, event sequence analysis,
and visualization of temporal data.
