Master-client R-trees: a new parallel R-tree architecture

Scientific databases must be able to efficiently run subset retrievals of multidimensional data sets. If the data sets are very large, significant retrieval speedups can be obtained via parallelism. In this paper, we present a new parallel distributed shared-nothing R-tree architecture. We provide experimental results demonstrating actual speedups for several synthetic and real data sets. In addition, we conduct experimental studies to investigate the effect of several declustering strategies and communication parameters.