Timeline-based Process Discovery
This work addresses the need for better performance insights in business processes, specifically for analysts dealing with waiting times, though it appears incremental as it builds on existing directly-follows graphs.
The paper tackles the problem of automatic process discovery by introducing an approach to construct process models that explicitly align with a time axis, addressing the limitation of current techniques that miss representing waiting times. The evaluation on two BPIC datasets and a proprietary dataset shows benefits over standard layout techniques.
A key concern of automatic process discovery is to provide insights into performance aspects of business processes. Waiting times are of particular importance in this context. For that reason, it is surprising that current techniques for automatic process discovery generate directly-follows graphs and comparable process models, but often miss the opportunity to explicitly represent the time axis. In this paper, we present an approach for automatically constructing process models that explicitly align with a time axis. We exemplify our approach for directly-follows graphs. Our evaluation using two BPIC datasets and a proprietary dataset highlight the benefits of this representation in comparison to standard layout techniques.