用图模型融合上下文信息，提升流程下一步活动预测准确率
Leveraging contextual events on structure-aware next activity prediction
- 构建实例图编码上下文环境，通过图神经网络建模
- 在多个真实数据集上提升预测性能，验证上下文信息有效性
- 适合流程挖掘与工业预测场景的从业者参考
预测性流程监控旨在预测运行中流程的各种方面。在各类任务中，下一步活动预测研究最为广泛。然而，现有方法中仅有少数明确编码上下文信息，即流程执行所处的环境条件，通常通过事件日志属性或聚合度量建模。本文引入基于实例图的概念，提出多种编码策略并评估其对预测性能的影响。针对每种编码策略，生成一组前缀-实例图，并作为输入提供给图神经网络完成分类任务。该方法在多个真实世界事件日志上进行评估，实验结果表明，融合上下文过程实例能有效提升预测性能。
原文摘要 · Abstract (English)
Predictive process monitoring aims at forecasting various aspects of running processes. Among the different tasks, next activity prediction represents the most extensively investigated. However, only a limited number of existing approaches explicitly encode contextual information, i.e., the environmental conditions in which the process is executed, typically modeled through event log attributes or aggregated measures. In this paper, an approach based on the concept of Instance Graphs is introduced. To incorporate contextual process instances, several encoding strategies are proposed and evaluated by measuring their impact on prediction performance. For each encoding strategy, a set of prefix-Instance Graphs is generated and subsequently provided as input to a Graph Neural Network for the classification task. The proposed approach is evaluated on multiple real-world event logs, and the experimental results demonstrate that incorporating contextual process instances benefits prediction performance.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。