基于图的神经网络的过程活动相关性得分确定方法
A Technique for Determining Relevance Scores of Process Activities using Graph-based Neural Networks
摘要：
通过流程挖掘生成的流程模型描述了流程的现状。通过使用诸如活动的频率或持续时间之类的度量进行注释，这些模型为流程分析人员提供了通用信息。为了改进业务流程的性能度量，流程分析师需要流程模型的进一步指导。在这项研究中，我们设计了图相关性矿工（GRM），一种基于图神经网络的技术，以确定相关性分数的过程活动的性能指标。用这样的相关性分数来注释流程模型有助于对业务流程进行以问题为中心的分析，将这些问题置于分析的中心。我们使用来自不同领域的四个数据集来定量评估我们的技术的预测质量，以证明相关性分数的可靠性。此外，我们提出了一个案例研究的结果，突出了该技术的实用性组织。我们的工作有重要的影响，无论是研究和业务应用，因为基于流程模型的分析功能的缺点，需要迫切解决，以实现成功的流程挖掘在企业级。
英文摘要：
Process models generated through process mining depict the as-is state of a process. Through annotations with metrics such as the frequency or duration of activities, these models provide generic information to the process analyst. To improve business processes with respect to performance measures, process analysts require further guidance from the process model. In this study, we design Graph Relevance Miner (GRM), a technique based on graph neural networks, to determine the relevance scores for process activities with respect to performance measures. Annotating process models with such relevance scores facilitates a problem-focused analysis of the business process, placing these problems at the centre of the analysis. We quantitatively evaluate the predictive quality of our technique using four datasets from different domains, to demonstrate the faithfulness of the relevance scores. Furthermore, we present the results of a case study, which highlight the utility of the technique for organisations. Our work has important implications both for research and business applications, because process model-based analyses feature shortcomings that need to be urgently addressed to realise successful process mining at an enterprise level.
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