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2005
Springer

Nearest Neighbor Search on Vertically Partitioned High-Dimensional Data

9 years 7 months ago
Nearest Neighbor Search on Vertically Partitioned High-Dimensional Data
Abstract. In this paper, we present a new approach to indexing multidimensional data that is particularly suitable for the efficient incremental processing of nearest neighbor queries. The basic idea is to use index-striping that vertically splits the data space into multiple low- and medium-dimensional data spaces. The data from each of these lower-dimensional subspaces is organized by using a standard multi-dimensional index structure. In order to perform incremental NN-queries on top of index-striping efficiently, we first develop an algorithm for merging the results received from the underlying indexes. Then, an accurate cost model relying on a power law is presented that determines an appropriate number of indexes. Moreover, we consider the problem of dimension assignment, where each dimension is assigned to a lower-dimensional subspace, such that the cost of nearest neighbor queries is minimized. Our experiments confirm the validity of our cost model and evaluate the performance ...
Evangelos Dellis, Bernhard Seeger, Akrivi Vlachou
Added 26 Jun 2010
Updated 26 Jun 2010
Type Conference
Year 2005
Where DAWAK
Authors Evangelos Dellis, Bernhard Seeger, Akrivi Vlachou
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