Efficient Parallelization of Spatial Approximation Trees

This paper describes the parallelization of the Spatial Approximation Tree. This data structure has been shown to be an efficient index structure for solving range queries in high-dimensional metric space databases. We propose a method for load balancing the work performed by the processors. The method is self-tuning and is able to dynamically follow changes in the work-load generated by user queries. Empirical results with different databases show efficient performance in practice. The algorithmic design is based on the use of the bulk-synchronous model of parallel computing.

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