标题： NOMAD - 数据流中最佳模型应用的导航 显示英文标题

标题： NOMAD - Navigating Optimal Model Application to Datastreams

摘要： NOMAD（用于数据流的最优模型应用导航）是一个在数据摄入过程中进行数据增强的智能框架，通过动态构建模型链（即具有不同成本-质量权衡的机器学习模型序列）来优化实时多类分类，这些模型通过基于效用的标准进行选择。 受数据库查询处理中谓词排序技术的启发，NOMAD利用更便宜的模型作为初始过滤器，仅在必要时才使用更昂贵的模型，同时通过正式的链安全机制保证分类质量与指定的基准模型相当。 它采用动态信念更新策略，根据每个事件的预测和变化的数据分布来适应模型选择，并扩展到依赖模型的场景，如早期退出的深度神经网络和堆叠集成。 在多个数据集上的评估表明，与静态和简单的方法相比，NOMAD实现了显著的计算节省，同时保持的分类质量与最准确（通常也是最昂贵）的模型相当。 显示英文摘要

摘要： NOMAD (Navigating Optimal Model Application for Datastreams) is an intelligent framework for data enrichment during ingestion that optimizes realtime multiclass classification by dynamically constructing model chains, i.e ,sequences of machine learning models with varying cost-quality tradeoffs, selected via a utilitybased criterion. Inspired by predicate ordering techniques from database query processing, NOMAD leverages cheaper models as initial filters, proceeding to more expensive models only when necessary, while guaranteeing classification quality remains comparable to a designated role model through a formal chain safety mechanism. It employs a dynamic belief update strategy to adapt model selection based on per event predictions and shifting data distributions, and extends to scenarios with dependent models such as earlyexit DNNs and stacking ensembles. Evaluation across multiple datasets demonstrates that NOMAD achieves significant computational savings compared to static and naive approaches while maintaining classification quality comparable to that achieved by the most accurate (and often the most expensive) model.