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

University Of California-Los Angeles, Los Angeles CA

Investigators

Abstract

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.