Efficient Constraint Processing for Highly Personalized Location Based Services

With the advances in wireless communications and location positioning technology, the potential for tracking, correlating, and filtering information about moving entities such as automobiles, people, and packages has greatly increased. Potential applications range from location-aware selective information dissemination, personalized route planning, goods tracking, and surveillance of group formation to buddy tracking. The implementation of efficient tracking, correlation, and filtering schemes supporting millions of mobile objects poses significant challenges. The knowledge of spatial, temporal, and causal relationship among moving objects would allow the support of highly personalized and effective location-based service (LBS) for tracking, correlating, and processing object positions, profiles, and trends. All these applications can be broadly classified as location-aware information dissemination tasks that can be well supported by the publish/subscribe paradigm. Moreover, these location-aware applications are supported by two communication styles, orthogonal to the coordination mechanism used—pull-oriented and push-oriented styles.