Publication Type

Conference Paper

Version

Postprint

Publication Date

2005

Abstract

In this paper, we develop a novel index structure to support effcient approximate k-nearest neighbor (KNN) query in high-dimensional databases. In high-dimensional spaces, the computational cost of the distance (e.g., Euclidean distance) between two points contributes a dominant portion of the overall query response time for memory processing. To reduce the distance computation, we first propose a structure (BID) using BIt-Difference to answer approximate KNN query. The BID employs one bit to represent each feature vector of point and the number of bit-difference is used to prune the further points. To facilitate real dataset which is typically skewed, we enhance the BID mechanism with clustering, cluster adapted bitcoder and dimensional weight, named the BID+. Extensive experiments are conducted to show that our proposed method yields signifcant performance advantages over the existing index structures on both real life and synthetic high-dimensional datasets.

Keywords

High-dimensional index structure, approximate KNN query, memory processing, bit difference

Discipline

Databases and Information Systems

Research Areas

Data Management and Analytics

Publication

Proceedings of the 21st ACM International Conference on Multimedia

First Page

947

Last Page

956

City or Country

Newcastle, Australia

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://dl.acm.org/citation.cfm?id=1082240

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