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.

通过过程挖掘生成的过程模型描述了过程的原样状态。通过带有诸如活动的频率或持续时间之类的指标的注释，这些模型向流程分析人员提供了常规信息。为了改进与绩效度量相关的业务流程，流程分析师需要流程模型的进一步指导。在这项研究中，我们设计了图相关挖掘器（GRM），这是一种基于图神经网络的技术，用于确定流程活动与绩效指标之间的相关性得分。具有此类相关性分数的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，并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定量评估了我们技术的预测质量，以证明相关性得分的真实性。此外，我们提供了一个案例研究的结果，突出了该技术对组织的实用性。我们的工作对研究和业务应用都具有重要意义，因为基于流程模型的分析具有一些缺点，而这些缺点需要紧急解决，才能在企业级别实现成功的流程挖掘。