Exploiting Spatial Indexes for Semijoin-Based Join Processing in Distributed Spatial Databases

• Authors: Kian-Lee Tan;Beng Chin Ooi;David J. Abel
• Venue: IEEE Transactions on Knowledge and Data Engineering
• Year: 2000

In a distributed spatial database system, a user may issue a query that relates two spatial relations not stored at the same site. Because of the sheer volume and complexity of spatial data, spatial joins between two spatial relations at different sites are expensive in terms of computation and transmission cost. In this paper, we address the problems of processing spatial joins in a distributed environment. We propose a semijoin-like operator, called the spatial semijoin, to prune away objects that will not contribute to the join result. This operator also reduces both the transmission and local processing costs for a later join operation. However, the cost of the elimination process must be taken into account, and we consider approaches to minimize these overheads. We also studied and compared two families of distributed join algorithms that are based on the spatial semijoin operator. The first is based on multidimensional approximations obtained from an index such as the R-tree, and the second is based on single-dimensional approximations obtained from object mapping. We conducted experiments on real data sets and report the results in this paper.