Adaptive data stream processing in heterogeneous distributed computing environments using real-time context

Evolution and proliferation of distributed heterogeneous computing present an exciting opportunity for intelligent data stream analysis. This project aims to develop novel adaptive, cost-efficient and context-aware data stream processing/mining algorithms. The algorithms will be able to incrementally adapt and self-reconfigure while coping with heterogeneity and mobility, operate within a range of varying computational parameters (e.g. battery, processor, memory) and adapt to available resources. The project will make significant contribution to rapidly growing research area of data stream processing as well as to applications such as Intelligent Transportation Systems and monitoring patients for early-detection of critical conditions.