Timeline-based process discovery

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. • This work proposes four layout strategies to align a process model to a linear time axis. • Compared to traditional process models, timeline-based process models can more effectively convey the temporal perspective of the model both in its entirety and local fragments, supporting the finding of possible bottlenecks. • Timeline-based process models face a challenge in balancing the trade-off between keeping a high model abstraction and a consistent model-timeline alignment.