研究人员开发了一个名为PetriNetGraph的新框架，用于评估流程挖掘中流程模型的质量。该方法使用节点嵌入和Procrustes对齐来将发现的模型与真实情况进行比较，提供了比传统合规性度量更强大的指标。该框架旨在对事件日志中的噪声和偏差不那么敏感，提供一个统一且有界的流程发现质量指标。实验表明，它比PetriNet2Vec等现有方法更能抵抗日志偏差。 AI
影响 该框架可以提高业务流程管理中流程发现和合规性检查的可靠性。
排序理由 该集群描述了一篇详细介绍流程挖掘新框架的学术论文。
在 Hugging Face Daily Papers 阅读 →
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
完整方法见我们的编辑标准。
arXiv:2610.17702v1 Announce Type: new Abstract: Evaluating the quality of process models discovered from event logs is a fundamental challenge in process mining. Traditional evaluation relies on conformance metrics such as fitness and precision, which are highly sensitive to nois…
Evaluating the quality of process models discovered from event logs is a fundamental challenge in process mining. Traditional evaluation relies on conformance metrics such as fitness and precision, which are highly sensitive to noise and deviations in event logs and therefore oft…