Efficient evaluation of all-nearest-neighbor queries

The All Nearest Neighbor (ANN) operation is a commonly used primitive for analyzing large multi-dimensional datasets. Since computing ANN is very expensive, in previous works R*-tree based methods have been proposed to speed up this computation. These traditional index-based methods use a pruning metric called MAXMAXDIST, which allows the algorithms to prune out nodes in the index that need not be traversed during the ANN computation. In this paper we introduce a new pruning metric called the NXNDIST and show that this metric is far more effective than the traditional MAXMAXDIST metric. In this paper we also challenge the common practice of using R*-tree index for speeding up the ANN computation. We propose an enhanced bucket quadtree index structure, called the MBRQT, and using extensive experimental evaluation show that the MBRQT index can significantly speed up the ANN computation. In addition, we also present the MBA algorithm based on a depth-first index traversal and bi-directional node expansion strategy. Furthermore, our method can be easily extended to efficiently answer the more general All-k-Nearest-Neighbor (AkNN) queries.