Problem
Research questions and friction points this paper is trying to address.
Predictive Process Monitoring
event log
heterogeneous knowledge graph
Relational Graph Convolutional Network
R-GCN
GNN4PPM: Multi-Target Predictive Process Monitoring with Relational Graph Convolutional Networks
🤖 AI Summary
该研究提出GNN4PPM方法，利用关系图卷积网络处理事件日志中的丰富信息，以预测流程执行的未来状态，包括所有下一个事件及其完整数据负载。
📝 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. 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
GNN4PPM
Relational Graph Convolutional Networks
Predictive Process Monitoring
Heterogeneous Knowledge Graph
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