利用上下文事件进行结构感知的下一个活动预测
Leveraging contextual events on structure-aware next activity prediction
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- Polytechnic University of Marche（马尔凯理工大学）
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中文总结 AI 辅助
本文提出基于实例图的方法，通过多种编码策略将上下文过程实例纳入图神经网络，用于下一个活动预测，实验证明可提升预测性能。
中文摘要 AI 辅助
预测性过程监控旨在预测运行中流程的各个方面。在不同任务中，下一个活动预测是研究最广泛的。然而，现有方法中只有少数明确编码了上下文信息，即流程执行的环境条件，通常通过事件日志属性或聚合度量进行建模。本文引入了一种基于实例图概念的方法。为了纳入上下文过程实例，提出了多种编码策略，并通过衡量其对预测性能的影响进行评估。对于每种编码策略，生成一组前缀实例图，并作为输入提供给图神经网络进行分类任务。所提方法在多个真实世界事件日志上进行了评估，实验结果表明，纳入上下文过程实例有利于预测性能。
英文摘要
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.
