对数据流分类分析的常用方法是集成学习。
Ensemble learning is a general method for classifying data streams.
针对数据流的特征，提出了一种基于速率的抢占式批处理方法。
A preemptive rate-based batching approach is proposed based on the characters of the data stream.
当任何源模型发生了更改，可能需要对数据流规范和目标模型进行相应的调整。
When any of the source models change, data-flow specification and possibly the target model will need to be adjusted.
应用推荐