Please use this identifier to cite or link to this item: http://hdl.handle.net/1783.1/2274

An efficient cost model for optimization of nearest neighbor search in low and medium dimensional spaces

Authors Tao, YF
Zhang, J
Papadias, D
Mamoulis, N
Issue Date 2004
Source IEEE transactions on knowledge and data engineering, v. 16, (10), 2004, October, p. 1169-1184
Summary Existing models for nearest neighbor search in multidimensional spaces are not appropriate for query optimization because they either lead to erroneous estimation or involve complex equations that are expensive to evaluate in real-time. This paper proposes an alternative method that captures the performance of nearest neighbor queries using approximation. For uniform data, our model involves closed formulae that are very efficient to compute and accurate for up to 10 dimensions. Further, the proposed equations can be applied on nonuniform data with the aid of histograms. We demonstrate the effectiveness of the model by using it to solve several optimization problems related to nearest neighbor search.
Subjects
ISSN 1041-4347
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Language English
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