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SIGMOD
2002
ACM

Efficient k-NN search on vertically decomposed data

10 years 7 months ago
Efficient k-NN search on vertically decomposed data
Applications like multimedia retrieval require efficient support for similarity search on large data collections. Yet, nearest neighbor search is a difficult problem in high dimensional spaces, rendering efficient applications hard to realize: index structures degrade rapidly with increasing dimensionality, while sequential search is not an attractive solution for repositories with millions of objects. This paper approaches the problem from a different angle. A solution is sought in an unconventional storage scheme, that opens up a new range of techniques for processing k-NN queries, especially suited for high dimensional spaces. The suggested (physical) database design accommodates well a novel variant of branch-and-bound search, that reduces the high dimensional space quickly to a small candidate set. The paper provides insight in applying this idea to k-NN search using two similarity metrics commonly encountered in image database applications, and discusses techniques for its imple...
Arjen P. de Vries, Nikos Mamoulis, Niels Nes, Mart
Added 08 Dec 2009
Updated 08 Dec 2009
Type Conference
Year 2002
Where SIGMOD
Authors Arjen P. de Vries, Nikos Mamoulis, Niels Nes, Martin L. Kersten
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