Timeline-based Process Discovery
Harleen Kaur Jan Mendling Christoffer Rubensson Timotheus Kampik
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.