English(EN) GNN4PPM: Multi-Target Predictive Process Monitoring with Relational Graph Convolutional Networks

新的GNN4PPM方法利用R-GCN增强预测性流程监控

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-15 04:00

研究人员开发了GNN4PPM，一种用于预测性流程监控（PPM）的新方法，该方法利用关系图卷积网络（R-GCN）。该方法使用RDF语义将事件日志数据表示为异构知识图，从而能够预测所有下一个事件及其完整数据负载。实验表明，与现有的最先进解决方案相比，GNN4PPM在复杂场景中提供了更高的准确性和适用性。 AI

影响这项研究通过利用更丰富的数据表示，有望在流程监控中实现更准确、更全面的预测。

排序理由该集群包含一篇研究论文，详细介绍了使用图神经网络进行预测性流程监控的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Ana Costa, Johannes M\"akelburg, Luise Pufahl · 2026-09-15 04:00

GNN4PPM：基于关系图卷积网络的多目标预测性流程监控

arXiv:2609.14534v1 Announce Type: new Abstract: 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. Ex…

本文如何被排名

报道来源 [1]

GNN4PPM：基于关系图卷积网络的多目标预测性流程监控

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