Range and region query processing in spatial databases
2017-02-28T23:29:12Z (GMT) by
With the boom of spatial databases, more and more spatial queries, which play a significant role in many academic and industrial areas, are proposed and studied extensively in last decade. One of the most fundamental queries among these is range search which returns all objects of interest within the pre-defined area. Because of the importance of the spatial queries, a mass of researches concentrated on processing various queries in spatial databases, especially, for k nearest neighbors (kNN) queries and its variations. However, as the fundamental query in spatial databases, range search queries have received far less attention. The existing works cannot process range queries efficiently, especially, in non-Euclidean space or on moving objects. Furthermore, the existing works for spatial queries retrieve point object only, none of them can find non-point objects, due to the difficulties of representing and indexing such objects in spatial databases. Motivated by above outstanding problems, we discuss several novel range and region queries and provide efficient solutions in spatial databases in this thesis. The following paragraphs describe our contribution. In the first part, we present several algorithms to process point-expected range queries that retrieve spatial objects within a specific distance from a query point. We are the first to investigate range queries under many different practical constraints. We conduct theoretical analysis to show the precise and effectiveness of our algorithms. The extensive experimental results provide the practical evidence for our theoretical analysis. Then we discuss point-expected range queries in a dynamic circumstance, where the query or the objects of interest are moving continuously. Our experiment results demonstrate that our approach outperforms the existing techniques in most instances. Thereinto, our algorithms of constrained range queries are base-on Voronoi expansion rather than incremental expansion methods, thus the response time and I/O access of range query is much faster than using exiting works. Meanwhile they queries diversify the range queries in spatial databases and solved many novel queries in spatial databases. Our algorithms designed for monitoring range queries involving any moving objects reduce the computation and communication cost significantly comparing with others. In the second part, we propose a new class of range queries, named, region-expected range queries, which find an (some) area(s) according to the location of the given set of objects. Because with the extremely progress of geographic information system, the typical queries in spatial databases cannot fulfill the users' requirements. In this part, we focus on two queries in this class, namely, kNN region queries and optimum region queries. We are the first to study this sort of range queries in spatial databases. We provide two algorithms for each query, and analyze their performances based on abundant theoretical illation and extensive experiment results. We are the first to investigate retrieving non-point objects in spatial databases. This sorts of spatial queries provide rich functionality in industrial and commercial areas, including, geographic information systems, decision support systems and so forth.