A new parallel spatial query algorithm for distributed spatial databases

The balance of data and the utilization of resources are essential to distributed spatial database system. The paper presents an efficient parallel spatial query algorithm which takes seriously the organization of spatial data into account. The algorithm adopts a balanced spatial data partitioning strategy for distributed spatial databases. According to the characteristics of data partitioning, it builds a packing R-tree as its index. The strategy also considers the problem of computing distribution. By replicating index to every site, each site can access different entry in the same index node at the same time. Based on the organization of spatial data, the algorithm can simultaneously execute query operation at different site in both filtration phase and refinery phase. So it obviously improves spatial query performances. In order to solve multiple paths search problem caused by R-tree index, the algorithm brings in globe stack to buffer temporary index nodes. It settles the difficult problem flexibly in distributed spatial databases. For simplicity, the paper discusses the parallel algorithm in 2-dimensional space. Through the experiments conducting on many real datasets, it shows better performance in various spatial query operations.