Translating Workflow Nets into the Partially Ordered Workflow Language

The Partially Ordered Workflow Language (POWL) has recently emerged as a process modeling notation, offering strong quality guarantees and high expressiveness. However, its adoption in practice is hindered by the prevalence of standard notations like workflow nets (WF-nets) and BPMN. This paper presents a novel algorithm for transforming safe and sound WF-net into equivalent POWL models. The algorithm recursively identifies structural patterns within the WF-net and translates them into their POWL representation. We formally prove the correctness of our approach, showing that the generated POWL model preserves the language of the input WF-net. Furthermore, we demonstrate the high scalability of our algorithm, and we show its completeness on a subclass of WF-nets that encompasses equivalent representations for all POWL models. This work bridges the gap between the theoretical advantages of POWL and the practical need for compatibility with established notations, paving the way for broader adoption of POWL in process analysis and improvement applications.