可分离工作流网的分层分解
Hierarchical Decomposition of Separable Workflow-Nets
AI总结：
本文提出一种将安全且声音的工作流网转换为等价POWL 2.0模型的算法，利用选择图捕捉通用决策和循环模式，证明了算法的正确性和完备性，展示了其在大规模过程模型中的高可扩展性及实际应用价值。
摘要：
部分有序工作流语言（POWL）最近作为一种过程建模符号出现，提供强质量保证和高表达性。早期版本依赖于严格块结构的运算符进行选择和循环，但语言已演进为POWL 2.0，引入选择图以建模非块结构的决策和循环。为弥合POWL的理论优势与现有符号的兼容性需求之间的差距，需要鲁棒的模型转换。本文提出一种新颖的算法，将安全且声音的工作流网（WF-net）转换为等价的POWL 2.0模型。该算法递归识别WF-net中的结构模式，并将其转换为POWL表示。与之前需要分别检测独占选择和循环的方法不同，我们的新算法利用选择图捕获通用决策和循环模式。我们正式证明了该方法的正确性，显示生成的POWL模型保留输入WF-net的语言。此外，我们证明了该算法在可分离WF-net类上的完备性，这类网络对应于通过状态机和标记图的分层嵌套构造的网络。我们评估了该算法在大规模过程模型上的表现，以展示其高可扩展性。进一步地，为测试其实际表达性，我们将其应用于包含1,493个工业和合成过程模型的基准测试集。我们的算法成功转换了所有模型，表明POWL 2.0的表达能力通常足以捕捉现实世界业务流程中的复杂逻辑。这项工作为POWL在实际过程分析和改进应用中的广泛应用铺平了道路。
英文摘要：
The Partially Ordered Workflow Language (POWL) has recently emerged as a process modeling notation, offering strong quality guarantees and high expressiveness. While early versions of POWL relied on strict block-structured operators for choices and loops, the language has recently evolved into POWL 2.0, introducing choice graphs to enable the modeling of non-block-structured decisions and cycles. To bridge the gap between the theoretical advantages of POWL and the practical need for compatibility with established notations, robust model transformations are required. This paper presents a novel algorithm for transforming safe and sound workflow nets (WF-nets) into equivalent POWL 2.0 models. The algorithm recursively identifies structural patterns within the WF-net and translates them into their POWL representation. Unlike the previous approach that required separate detection strategies for exclusive choices and loops, our new algorithm utilizes choice graphs to capture generalized decision and cyclic patterns. We formally prove the correctness of our approach, showing that the generated POWL model preserves the language of the input WF-net. Furthermore, we prove the completeness of our algorithm on the class of separable WF-nets, which corresponds to nets constructed via the hierarchical nesting of state machines and marked graphs. We evaluate our algorithm on large-scale process models to demonstrate its high scalability. Furthermore, to test its practical expressiveness, we applied it to a benchmark of 1,493 industrial and synthetic process models. Our algorithm successfully transformed all models in this benchmark, suggesting that POWL 2.0's expressive power is generally sufficient to capture the complex logic found in real-world business processes. This work paves the way for broader adoption of POWL in practical process analysis and improvement applications.
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