Scalable Discovery of Partially Ordered Workflow Models with Formal Guarantees

International Conference on Process Mining (ICPM), pp. 89–96

Abstract

Many real-life processes naturally define partial orders over the activities they are composed of. Partial orders can be used as a graph-like representation of process behavior, allowing us to model concurrent and sequential dependencies. The Partially Ordered Workflow Language (POWL) combines block-structured modeling notations with partially-ordered graph representations. A POWL model is a hierarchical model where sub-models can be combined into a new model either using a control-flow operator or as a partial order. The application of POWL models in process mining remains a challenge due to a lack of scalable approaches for the discovery of POWL models. In this paper, we address this gap by proposing an approach for the discovery of POWL models that leverages large data sets and ensures high conformity with the input data. Our approach provides formal guarantees on the uniqueness, existence, and quality of discovered partial orders. The evaluation of our approach underscores its scalability with large data sets and its ability to generate high-quality models.

Authors 3

1. Humam Kourani

   Fraunhofer Institute for Applied Information Technology

   Affiliation as printed

   Fraunhofer FIT,Sankt Augustin,Germany

   Fraunhofer FIT, Sankt Augustin, Germany

2. Daniel Schuster

   Fraunhofer Institute for Applied Information Technology

   Affiliation as printed

   Fraunhofer FIT,Sankt Augustin,Germany

   Fraunhofer FIT, Sankt Augustin, Germany

3. Wil M. P. van der Aalst Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Aachen,Germany

   RWTH Aachen University, Aachen, Germany