用简单模型+动态选择，高效预测事件日志下一步
Promoting Simple Agents: Ensemble Methods for Event-Log Prediction
- 采用n-gram模型与动态选拔机制，替代复杂神经网络
- 在真实数据上准确率媲美无窗口神经模型，计算成本更低
- 适合资源受限场景下的实时事件预测任务
我们对比了基于轻量级自动机的n-gram模型与神经架构（LSTM、Transformer）在流式事件日志中预测下一个活动的表现。在合成模式和五个真实过程挖掘数据集上的实验表明，合理设置上下文窗口的n-gram模型能达到与神经模型相当的准确率，同时显著降低资源消耗。与窗口化神经架构不稳定的表现不同，n-gram模型具有稳定一致的准确性。尽管传统集成方法如投票可提升n-gram性能，但需并行运行多个代理，增加内存占用和延迟。为此，我们提出一种促进算法，在推理时动态选择两个活跃模型，相比传统投票方案大幅降低开销。在真实数据集上，该集成方法达到或超过非窗口神经模型的准确率，且计算成本更低。
原文摘要 · Abstract (English)
We compare lightweight automata-based models (n-grams) with neural architectures (LSTM, Transformer) for next-activity prediction in streaming event logs. Experiments on synthetic patterns and five real-world process mining datasets show that n-grams with appropriate context windows achieve comparable accuracy to neural models while requiring substantially fewer resources. Unlike windowed neural architectures, which show unstable performance patterns, n-grams provide stable and consistent accuracy. While we demonstrate that classical ensemble methods like voting improve n-gram performance, they require running many agents in parallel during inference, increasing memory consumption and latency. We propose an ensemble method, the promotion algorithm, that dynamically selects between two active models during inference, reducing overhead compared to classical voting schemes. On real-world datasets, these ensembles match or exceed the accuracy of non-windowed neural models with lower computational cost.
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