Timeline-based Process Discovery

TL;DR

This paper introduces a method for process discovery that explicitly incorporates a time axis into process models, enhancing the understanding of waiting times and performance aspects in business processes.

Contribution

It presents a novel approach for constructing process models with an explicit time axis, improving upon standard models by highlighting performance-related information.

Findings

Explicit time axis improves process understanding.

Evaluation shows benefits over standard layout techniques.

Method effective on multiple datasets.

Abstract

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.