Process mining visualizations provide analysts with relevant cues for identifying bottlenecks, waiting times, or other operational inefficiencies. Such visualizations show instances of the process and the corresponding model separately. This spatial separation requires users to mentally integrate detailed and generalized data representations, hindering effective traceability between the two perspectives. This study introduces an interactive visual analytics technique for process mining that seamlessly links the process model view with the corresponding case view through a zoom-based visualization. We first define a two-dimensional coordinate system for plotting individual event sequences. Then, we define semantic layers using contour diagrams to incrementally aggregate nodes and edges into a directly-follows graph. Finally, the resulting Time-Order Map enables non-geometric semantic zooming, allowing for a seamless transition between multiple abstraction levels of a process graph, including its instance representation. We evaluate our technique using realworld datasets to demonstrate its effectiveness in exploring the connection between the process model and the underlying cases. In this way, our approach offers a solution for semantic zooming in process mining, facilitating navigation between process view levels.

A key concern of automatic process discovery is providing insights into business process performance. Process analysts are specifically interested in waiting times and delays for identifying opportunities to speed up processes. Against this backdrop, it is surprising that current techniques for automatic process discovery generate directly-follows graphs and comparable process models without representing the time axis explicitly. This paper presents four layout strategies for automatically constructing process models that explicitly align with a time axis. We exemplify our approaches for directly-follows graphs. We evaluate their effectiveness by applying them to real-world event logs with varying complexities. Our specific focus is on their ability to handle the trade-off between high control-flow abstraction and high consistency of temporal activity order. Our results show that timeline-based layouts provide benefits in terms of an explicit representation of temporal distances. They face challenges for logs with many repeating and concurrent activities.