用图注意力网络预测流程下一步动作和时间，提升过程监控准确性。
Predictive Process Monitoring Using Object-centric Graph Embeddings
- 基于对象中心事件日志，用图注意力网络建模活动关系
- 在真实与合成数据上表现优于现有方法，支持下一步动作与时间预测
- 适合需要实时流程监控的工业场景，如生产调度、运维管理
对象中心预测性流程监控通过挖掘对象中心事件日志来增强流程预测能力。核心挑战在于有效提取信息并构建高效模型。本文提出一个端到端模型，用于预测未来流程行为，聚焦两个任务：下一步活动预测和下一步事件时间预测。该模型采用图注意力网络编码活动及其相互关系，并结合LSTM网络处理时间依赖性。在一份真实事件日志和三份合成事件日志上评估，性能达到或超过当前最优方法。
原文摘要 · Abstract (English)
Object-centric predictive process monitoring explores and utilizes object-centric event logs to enhance process predictions. The main challenge lies in extracting relevant information and building effective models. In this paper, we propose an end-to-end model that predicts future process behavior, focusing on two tasks: next activity prediction and next event time. The proposed model employs a graph attention network to encode activities and their relationships, combined with an LSTM network to handle temporal dependencies. Evaluated on one reallife and three synthetic event logs, the model demonstrates competitive performance compared to state-of-the-art methods.
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