Translating Workflow Nets into the Partially Ordered Workflow Language

2025-03-26

AI Summary

This paper addresses the practical deployment barrier of POWL—its lack of interoperability with industrial modeling standards such as WF-net and BPMN. We propose the first formally verifiable, scalable, and complete automated translation algorithm from WF-net to POWL. Our method recursively identifies structural patterns in safe and well-structured WF-nets to produce semantically equivalent POWL models. We formally prove behavioral language equivalence between the input WF-net and the output POWL, and demonstrate completeness for all safe WF-net subclasses expressible in POWL. Experimental evaluation confirms efficient conversion of large-scale models. This work bridges, for the first time, the semantic gap between the theoretical expressiveness of POWL and the industrial prevalence of WF-net and BPMN, thereby establishing a foundational basis for POWL’s real-world adoption.

Technology Category

• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Planning, Routing, and Scheduling: Planning with Language Models
• Knowledge Representation and Reasoning: Description Logics

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

The Partially Ordered Workflow Language (POWL) has recently emerged as a process modeling notation, offering strong quality guarantees and high expressiveness. However, its adoption is hindered by the prevalence of standard notations like workflow nets (WF-nets) and BPMN in practice. 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.

Problem

• Translate Workflow Nets to POWL models
• Ensure correctness and scalability of transformation
• Bridge gap between POWL and standard notations

Innovation

• Algorithm transforms WF-nets to POWL models
• Recursively identifies and translates structural patterns
• Proven correctness and high scalability demonstrated