Data-intensive real-time applications, such as transportation management, military surveillance, and network monitoring, need to handle massive amounts of stream data in a timely fashion. It is challenging to support real-time stream data services (RTSDS) due to stringent timing constraints, potentially unbounded continuous stream data, bursty stream data arrivals, and workload variations due to data value changes. This project will develop cost-effective methods and a runtime system for RTSDS. The project will systematically investigate methods and tools to support real-time continuous queries for RTSDS even in the presence of dynamic workloads. Specifically, the project will study a) real-time continuous query modeling, b) new performance metric design c) adaptive query scheduling design, d) tardiness control and load shedding, for both single node and clustered RTSDS. The project will also have prototype implementation and testbed evaluations. The results and findings of this project will advance and seamlessly integrate real-time computing and stream data management.
Real-time stream data services (RTSDS) play an important role in many emerging application including intelligent transportation, green buildings, smart grid management, military surveillance, and network monitoring. Our everyday lives are highly dependent on these applications. RTSDS is a fundamental technology for developing critical data-intensive real-time applications with great socio-economic impacts. The project will develop the scientific foundations and associated engineering principles for building RTSDS.
The project will provide excellent opportunities for undergraduate and graduate students to acquire hands-on experience as well as theoretical backgrounds in RTSDS. The PIs will work closely with students to carry out the research in this project. Through this project, the PIs will train students to become strong in both analytical and implementation skills, and help them solve challenging problems in RTSDS.
