This project, acquiring infrastructure for evaluating distributed event stream processing architecture, spatio-temporal data mining algorithms, and scalability of middleware architectures for supporting large scale context aware systems, responds to the increasing demand for applications that exploit the spatio-temporal context information to enrich and augment mobile users' computing environments. This demand is motivated by factors such as the need to provide pertinent resources and information to users based on their physical location context (e.g., yellow pages and location-based services), provide location based public safety services (e.g., enhanced 911 services), and assist seamless mobility of users across different spaces by dynamically adapting the applications on their mobile computing devices based on the location. Challenges in building such context-aware computing environments and applications stem from the high complexity in developing applications due to the need of integration of different kinds of technologies, context-based security and privacy requirements, need to identify required pertinent resources and information, and timely detection and querying of context information. The infrastructure contributes to address these challenges through research in . Programming environments and middleware architectures for context-aware applications; . Spatio-temporal query processing with privacy preserving techniques, . Spatio-temporal data mining, profiling, and prefetching methods, and . Agent-based distributed event data stream processing for context detection. These synergistic projects use . Context information in providing security and privacy to these applications, . Spatio-temporal data mining for context-based adaptations, . Continuous query processing for context-based information retrieval, and . Programming abstractions and middleware architectures for rapid construction of these applications from their high level specifications.
Broader Impacts: The infrastructure advances knowledge and contributes to develop new software infrastructure for building context-aware applications. The project actively seeks and encourages participation of students from under-represented groups, facilitates students' theses, and collaborates with an IGERT initiative.
