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: Group nearest neighbor queries
Authors: Papadias, Dimitris
Shen, Qiongmao
Tao, Yufei
Mouratidis, Kyriakos
Keywords: Group nearest neighbor
GNN query
Euclidean distance
Issue Date: Apr-2004
Citation: 20th International Conference on Data Engineering, p. 301-312
Abstract: Given two sets of points P and Q, a group nearest neighbor (GNN) query retrieves the point(s) of P with the smallest sum of distances to all points in Q. Consider, for instance, three users at locations q1, q2 and q3 that want to find a meeting point (e.g., a restaurant); the corresponding query returns the data point p that minimizes the sum of Euclidean distances |pqi| for 1≤i≤3. Assuming that Q fits in memory and P is indexed by an R-tree, we propose several algorithms for finding the group nearest neighbors efficiently. As a second step, we extend our techniques for situations where Q cannot fit in memory, covering both indexed and non-indexed query points. An experimental evaluation identifies the best alternative based on the data and query properties.
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
papadias7.pdfpre-published version241KbAdobe PDFView/Open

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