HKUST Library Institutional Repository Banner

HKUST Institutional Repository >
Computer Science and Engineering >
CSE Conference Papers >

Please use this identifier to cite or link to this item:
Title: All-nearest-neighbors queries in spatial databases
Authors: Zhang, Jun
Papadias, Dimitris
Mamoulis, Nikos
Tao, Yufei
Keywords: All-nearest-neighbors queries
Spatial databases
Multidimensional objects
ANN query processing
Database indexing
Closest-pairs query processing
Issue Date: Jun-2004
Citation: Proceedings of the 16th International Conference on Scientific & Statistical Database Management, v. 16 ( 2004 ), p. 297-306
Abstract: Given two sets A and B of multidimensional objects, the all-nearest-neighbors (ANN) query retrieves for each object in A its nearest neighbor in B. Although this operation is common in several applications, it has not received much attention in the database literature. In this paper we study alternative methods for processing ANN queries depending on whether A and B are indexed. Our algorithms are evaluated through extensive experimentation using synthetic and real datasets. The performance studies show that they are an order of magnitude faster than a previous approach based on closest-pairs query processing.
Rights: © 2004 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
Appears in Collections:CSE Conference Papers

Files in This Item:

File Description SizeFormat
papadias3.pdfpre-published version151KbAdobe PDFView/Open

All items in this Repository are protected by copyright, with all rights reserved.