Skip to main content Skip to main navigation


DALG: The Data Aware Event Log Generator

David Jilg; Joscha Grüger; Tobias Geyer; Ralph Bergmann
In: BPM 2023 Best Dissertation Award, Doctoral Consortium, and Demonstration & Resources Forum. BPM Demo Track (BPMTracks-2023), Ceur-WS, 2023.


Data and process mining techniques can be applied in many areas to gain valuable insights, but accessibility to real-world process data is severely limited. However, research, but especially the development of new methods, depends on a sufficient basis of realistic data. With adequate quality, synthetic data can be a solution to this problem. The SAMPLE \cite{SAMPLE} approach aims to mitigate this problem by generating multi-perspective synthetic event logs that make sense on a semantic level. In this paper, we present the tool DALG: The Data Aware Event Log Generator, which allows users to generate synthetic event logs using the SAMPLE approach.