非块结构的合理自由选择工作流网的发现
Discovering Sound Free-choice Workflow Nets With Non-block Structures
摘要：
过程发现的目的是发现能够解释从信息系统中提取的事件日志行为的模型。虽然已经提出了各种方法，但只有少数方法可以保证理想的属性，例如可靠性和自由选择。利用过程树的代表性偏差来提供保证的最新方法被限制为块结构。这样的构造限制了所发现的模型的表达能力，即，只能发现可靠的自由选择工作流网的子集。为了支持更灵活的结构化表示，我们的目标是发现提供相同保证但也允许非块结构的过程模型。从现有的作品，利用自由选择网理论的合成规则的启发，我们提出了一种自动的方法，增量添加活动到现有的过程模型与预定义的模式。按规则行事可以确保生成的模型始终是合理的和自由选择的。此外，所发现的模型不限于块结构，因此更灵活。该方法已在Python中实现，并使用各种真实事件日志进行了测试。实验表明，我们的方法确实可以发现具有竞争力的质量和更灵活的结构相比，现有的方法的模型。
英文摘要：
Process discovery aims to discover models that can explain the behaviors of event logs extracted from information systems. While various approaches have been proposed, only a few guarantee desirable properties such as soundness and free-choice. State-of-the-art approaches that exploit the representational bias of process trees to provide the guarantees are constrained to be block-structured.Such constructs limit the expressive power of the discovered models, i.e., only a subset of sound free-choice workflow nets can be discovered. To support a more flexible structural representation, we aim to discover process models that provide the same guarantees but also allow for non-block structures. Inspired by existing works that utilize synthesis rules from the free-choice nets theory, we propose an automatic approach that incrementally adds activities to an existing process model with predefined patterns. Playing by the rules ensures that the resulting models are always sound and free-choice. Furthermore, the discovered models are not restricted to block structures and are thus more flexible. The approach has been implemented in Python and tested using various real-life event logs. The experiments show that our approach can indeed discover models with competitive quality and more flexible structures compared to the existing approach.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
