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Range nearest-neighbor query
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| Venue: | IEEE Transactions on Knowledge and Data Engineering (TKDE |
| Citations: | 39 - 2 self |
BibTeX
@ARTICLE{Hu_rangenearest-neighbor,
author = {Haibo Hu and Dik Lun Lee},
title = {Range nearest-neighbor query},
journal = {IEEE Transactions on Knowledge and Data Engineering (TKDE},
year = {},
pages = {2006}
}
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Abstract
A range nearest-neighbor (RNN) query retrieves the nearest neighbor (NN) for every point in a range. It is a natural generalization of point and continuous nearest-neighbor queries and has many applications. In this paper, we consider the ranges as (hyper-)rectangles and propose efficient in-memory processing and secondary memory pruning techniques for RNN queries in both 2D and high-dimensional spaces. These techniques are generalized for kRNN queries, which return the k nearest neighbors for every point in the range. In addition, we devise an auxiliary solution-based index EXO-tree to speed up any type of NN query. EXO-tree is orthogonal to any existing NN processing algorithm and thus can be transparently integrated. An extensive empirical study was conducted to evaluate the CPU and I/O performance of these techniques, and the study showed that they are efficient and robust under various datasets, query ranges, numbers of nearest neighbors, dimensions, and cache sizes.
Keyphrases
range nearest-neighbor query propose efficient in-memory processing high-dimensional space natural generalization many application nn processing algorithm secondary memory auxiliary solution-based index exo-tree rnn query continuous nearest-neighbor query query range extensive empirical study various datasets range nearest-neighbor nn query krnn query cache size
