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. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Marín, M., & Reyes, N. (2005). Efficient parallelization of Spatial Approximation Trees. In Lecture Notes in Computer Science (Vol. 3514, pp. 1003–1010). Springer Verlag. https://doi.org/10.1007/11428831_125
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