An efficient parallel algorithm for high dimensional similarity join

Multidimensional similarity join finds pairs of multidimensional points that are within some small distance of each other. The ε-k-d-B tree has been proposed as a data structure that scales better as the number of dimensions increases compared to previous data structures. We present a cost model of the ε-k-d-B tree and use it to optimize the leaf size. We present novel parallel algorithms for the similarity join using the ε-k-d-B tree. A load balancing strategy based on equi-depth histograms is shown to work well for uniform or low-skew situations, whereas another based on weighted equi-depth histograms works far better for high-skew datasets. The latter strategy is only slightly slower than the former strategy for low skew datasets. Further its cost is proportional to the overall cost of the similarity join