Scalable Continuous Query Processing in Location - Aware Database Servers

Mohamed F. Mokbel. Ph.D., Purdue University, August, 2005. Scalable Continuous Query Processing in Location-aware Database Servers. Major Professors: Walid G. Aref. The wide spread use of cellular phones, handheld devices, and GPS-like technology enables location-aware environments where virtually all objects are aware of their locations. Location-aware environments and location-aware services are characterized by the large number of moving objects and large number of continuously moving queries (also known as spatio-temporal queries). Such environments call for new query processing techniques that deal with the continuous movement and frequent updates of both spatio-temporal objects and spatio-temporal queries. This dissertation, presents novel paradigms and algorithms for efficient processing and scalable execution of continuous spatio-temporal queries in location-aware database servers. We introduce a disk-based framework that exploits shared execution and incremental evaluation paradigms. With shared execution, the problem of evaluating a set of concurrent continuous queries is abstracted to a spatial join between the set of moving objects and the set of moving queries. With the incremental evaluation, rather than performing a repetitive evaluation of continuous queries, we produce only the updates of the recently reported answer. For streaming environments, we introduce a generic class of spatio-temporal operators that can be tuned with a set of parameters and methods to act as various continuous spatio-temporal queries (e.g., range queries and k-nearest-neighbor queries). The spatio-temporal operators can be combined with other traditional operators (e.g., join, distinct, and aggregate) to support a wide variety of continuous spatiotemporal queries. To support scalability in steaming environments, we introduce