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SIGMOD
2009
ACM

Quality and efficiency in high dimensional nearest neighbor search

9 years 9 months ago
Quality and efficiency in high dimensional nearest neighbor search
Nearest neighbor (NN) search in high dimensional space is an important problem in many applications. Ideally, a practical solution (i) should be implementable in a relational database, and (ii) its query cost should grow sub-linearly with the dataset size, regardless of the data and query distributions. Despite the bulk of NN literature, no solution fulfills both requirements, except locality sensitive hashing (LSH). The existing LSH implementations are either rigorous or adhoc. Rigorous-LSH ensures good quality of query results, but requires expensive space and query cost. Although adhoc-LSH is more efficient, it abandons quality control, i.e., the neighbor it outputs can be arbitrarily bad. As a result, currently no method is able to ensure both quality and efficiency simultaneously in practice. Motivated by this, we propose a new access method called the locality sensitive B-tree (LSB-tree) that enables fast highdimensional NN search with excellent quality. The combination of sever...
Yufei Tao, Ke Yi, Cheng Sheng, Panos Kalnis
Added 05 Dec 2009
Updated 05 Dec 2009
Type Conference
Year 2009
Where SIGMOD
Authors Yufei Tao, Ke Yi, Cheng Sheng, Panos Kalnis
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