Predictive Process Monitoring focuses on predicting future states of ongoing process executions, such as forecasting the remaining time. Recent developments in Object-Centric Process Mining have enriched event data with objects and their explicit relations between events. To leverage this enriched data, we propose the Heterogeneous Object Event Graph encoding (HOEG), which integrates events and objects into a graph structure with diverse node types. It does so without aggregating object features, thus creating a more nuanced and informative representation. We then adopt a heterogeneous Graph Neural Network architecture, which incorporates these diverse object features in prediction tasks. We evaluate the performance and scalability of HOEG in predicting remaining time, benchmarking it against two established graph-based encodings and two baseline models. Our evaluation uses three Object-Centric Event Logs (OCELs), including one from a real-life process at a major Dutch financial institution. The results indicate that HOEG competes well with existing models and surpasses them when OCELs contain informative object attributes and event-object interactions.

翻译：预测过程监控侧重于预测正在执行的过程的未来状态，例如预测剩余时间。对象中心过程挖掘的最新发展通过对象及其事件之间的显式关系丰富了事件数据。为利用这种丰富的数据，我们提出异构对象事件图编码（HOEG），它将事件和对象集成到具有多种节点类型的图结构中，且无需聚合对象特征，从而创建更具细微差别和信息性的表示。随后，我们采用异构图神经网络架构，在预测任务中整合这些多样化的对象特征。我们评估了HOEG在预测剩余时间方面的性能和可扩展性，将其与两种已有的基于图的编码和两种基线模型进行对比。评估使用了三个对象中心事件日志（OCEL），包括一个来自荷兰某大型金融机构真实流程的日志。结果表明，HOEG与现有模型表现相当，并且在OCEL包含信息性对象属性和事件-对象交互时优于它们。