A novel framework for design and implementation of adaptive stream mining systems

With the increasing need for accurate mining and classification from multimedia data content, and the growth of such multimedia applications in mobile and distributed architectures, stream mining systems require increasing amounts of flexibility, extensibility, and adaptivity for effective deployment. To address this challenge, we propose a novel approach that rigorously integrates foundations of dataflow modeling for high level signal processing system design, and adaptive stream mining based on dynamic topologies of classifiers. In particular, we introduce a new design environment, called the lightweight dataflow for dynamic data driven application systems (LiD4E) environment. LiD4E provides formal semantics, rooted in dataflow principles, for design and implementation of a broad class of multimedia stream mining topologies. We demonstrate the capabilities of LiD4E using a face detection application that systematically adapts the type of classifier used based on dynamically changing application constraints.