Analysis: The Importance of Time in Automatic Process Discovery

In the field of automatic process discovery, one key aspect that has often been overlooked is the representation of time. Waiting times, in particular, play a crucial role in understanding the performance of business processes. However, current techniques for automatic process discovery tend to generate models that focus solely on the sequence of activities, without explicitly representing the time axis.

This paper presents an innovative approach to address this limitation by automatically constructing process models that align with a time axis. The authors demonstrate their approach using directly-follows graphs, which are commonly used in process discovery.

The benefits of representing the time axis in process models are highlighted through an evaluation using both public and proprietary datasets. The use of two BPIC datasets ensures that the findings are validated against real-world scenarios. This evaluation serves as a strong argument for the adoption of this new representation technique.

By explicitly representing the time axis, this approach enhances the visual representation of process models. It enables analysts and decision-makers to gain valuable insights into waiting times and other time-related performance metrics. This, in turn, facilitates the identification of bottlenecks and opportunities for process optimization.

The importance of time in process discovery cannot be overstated. By incorporating a time axis into process models, organizations can gain a deeper understanding of their processes and drive more informed decision-making. This approach has the potential to revolutionize the field of automatic process discovery and unlock new opportunities for improving process efficiency and effectiveness.

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