CSR: Small: Dynamic Construction and Configuration of Classifier Topologies for Real-time Stream Mining Systems

该项目主要研究以下三个关于实时流系统中知识提取和数据挖掘的研究问题：流挖掘系统的新框架：该项目正在研究如何通过定义适当的局部和端到端目标函数，以及指导不同优化和自适应算法的资源和延迟约束，来形式化从大容量流中大规模分布式知识提取的问题。拓扑构造：该项目正在研究将分类器组织成映射到分布式基础结构上的连接拓扑的方法。该研究从研究分类器的线性链开始，然后寻求将工作扩展到并行工作的多链和分类器树。基于交互式多智能体学习的分散解决方案：对于分类器分布在多个节点上的大规模流挖掘系统，该项目正在开发用于联合拓扑构建和局部分类器配置的分散决策框架和分布式算法。在这种分布式场景中，优化端到端性能需要在每个站点部署交互式多代理解决方案，以便确定每个分类器的决策对其他分类器的影响。

This project is investigating the following three research problems concerning knowledge extraction and data mining from real-time stream systems:Novel Framework for Stream Mining Systems: The project is studying how to formalize the problem of large scale distributed knowledge extraction from high volume streams by defining appropriate local and end-to-end objective functions, along with resource and delay constraints that will guide the different optimization and adaptation algorithms. Topology construction: The project is studying methods to organize classifiers into a connected topology mapped onto a distributed infrastructure. The research starts by studying linear chains of classifiers and then seeks to extend the work to multiple chains working in parallel, and to classifier trees.Decentralized Solutions based on Interactive Multi-Agent Learning: For large scale stream mining systems, where the classifiers are distributed across multiple nodes, the project is developing a decentralized decision framework and distributed algorithms for joint topology construction and local classifier configuration. In such distributed scenarios, optimizing the end-to-end performance requires interactive, multi-agent solutions to be deployed at each site in order to determine the effect of each classifier's decisions on the other classifiers.