发表机构
Technical University of Munich（慕尼黑工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
GNN4PPM利用关系图卷积网络和异构知识图谱，实现多目标预测性流程监控，一次性预测所有下一事件及其数据负载，实验证明其准确性和适用性。
AI 中文摘要
预测性流程监控（PPM）旨在运行时尽早预测流程执行的未来状态。常见任务包括预测下一个事件、轨迹完成时间以及结果。现有方法通常从已执行活动及其时间戳和案例标识符的角度来考虑事件。这导致在现实环境中，事件日志中记录的更多信息在执行预测任务时未被捕获或被完全忽略。我们提出GNN4PPM，一种一次性预测所有下一个事件及其完整数据负载的方法。我们将事件信息表示为异构知识图谱，该图谱将事件日志捕获为RDF语义，并使用关系图卷积网络（R-GCN）训练嵌入。与现有解决方案相比，我们的方法具有前景，与最先进解决方案的实验证明了GNN4PPM在复杂设置中的准确性和适用性。
英文摘要
Predictive Process Monitoring (PPM) aims at predicting at runtime and as early as possible the future states of a process execution. Common tasks include predicting the next event, the time to completion of a trace, and outcomes. Existing approaches typically consider an event from the perspective of the executed activities along with their timestamps and case identifiers. This leads to the disadvantage that in real-life settings, there is much more information recorded in the event log that is not captured or completely ignored when performing prediction tasks. We introduce GNN4PPM, an approach that predicts all next events along with their complete data payload at once. We represent event information in a heterogeneous knowledge graph that captures the event log as an RDF semantics, and train the embeddings with a Relational Graph Convolutional Network (R-GCN). Our approach is promising in comparison to existing solutions, and experiments with state-of-the-art solutions prove the accuracy and applicability of GNN4PPM in complex settings.
CommentsAccepted for presentation at the BPM 2026 Forum. The final version will appear in the Lecture Notes in Business Information Processing (LNBIP) post-proceedings
