Discovering partially ordered workflow models

Information Systems, vol. 128, pp. 102493

Abstract

In many real-world scenarios, processes naturally define partial orders over their constituent tasks. Partially ordered representations can be exploited in process discovery as they facilitate modeling such processes. The Partially Ordered Workflow Language (POWL) extends partially ordered representations with control-flow operators to support modeling common process constructs such as choice and loop structures. POWL integrates the hierarchical nature of process trees with the flexibility of partially ordered representations, opening up significant opportunities in process discovery. This paper presents and compares various approaches for the automated discovery of POWL models. We investigate the effects of applying varying validity criteria to partial orders, and we propose methods for incorporating frequency information to improve the quality of the discovered models. Additionally, we propose alternative visualizations for POWL models, offering different approaches that may be useful in various contexts. The discovery approaches are evaluated using various real-life data sets, demonstrating the ability of POWL models to capture complex process structures. • Employing different validity requirements in the discovery of POWL models. • Incorporating frequency-based filtering in the discovery of POWL models. • Enhancing the visualization of the discovered models. • Proving the soundness of the discovered models.

Authors 4

1. Humam Kourani corresponding Aachen

   Fraunhofer Institute for Applied Information Technology · RWTH Aachen University

   Affiliation as printed

   Fraunhofer Institute for Applied Information Technology FIT, Data Science and Artificial Intelligence, Schloss Birlinghoven, Sankt Augustin, 53757, Germany

   RWTH Aachen University, Process and Data Science, Ahornstraße 55, Aachen, 52074, Germany

2. Sebastiaan J. van Zelst
   Affiliation as printed

   Celonis Labs GmbH, Theresienstraße 6, Munich, 80333, Germany

3. Daniel Schuster Aachen

   Fraunhofer Institute for Applied Information Technology · RWTH Aachen University

   Affiliation as printed

   Fraunhofer Institute for Applied Information Technology FIT, Data Science and Artificial Intelligence, Schloss Birlinghoven, Sankt Augustin, 53757, Germany

   RWTH Aachen University, Process and Data Science, Ahornstraße 55, Aachen, 52074, Germany

4. Wil M. P. van der Aalst Aachen

   Fraunhofer Institute for Applied Information Technology · RWTH Aachen University

   Affiliation as printed

   Fraunhofer Institute for Applied Information Technology FIT, Data Science and Artificial Intelligence, Schloss Birlinghoven, Sankt Augustin, 53757, Germany

   RWTH Aachen University, Process and Data Science, Ahornstraße 55, Aachen, 52074, Germany