Performance Skyline: Inferring Process Performance Models from Interval Events

Andrea Maldonado, Janina Sontheim, Florian Richter, Thomas Seidl

Abstract: Performance mining from event logs is a central task in managing and optimizing business processes. Established analysis techniques work with a single timestamp per event only. However, when available, time interval information enables proper analysis of the duration of individual activities as well as the overall execution runtime. Our novel approach, performance skyline, considers extended events, including start and end timestamps in log files, aiming at the discovery of events that are crucial to the overall duration of real process executions. As first contribution, our method gains a geometrical process representation for traces with interval events by using interval-based methods from sequence pattern mining and performance analysis. Secondly, we introduce the performance skyline, which discovers dominating events considering a given heuristic in this case, event duration. As a third contribution, we propose three techniques for statistical analysis of performance skylines and process trace sets, enabling more accurate process discovery, conformance checking, and process enhancement. Experiments on real event logs demonstrate that our contributions are highly suitable for detecting and analyzing the dominant events of a process.