Please use this identifier to cite or link to this item: http://hdl.handle.net/1783.1/2388

Aggregate nearest neighbor queries in spatial databases

Authors Papadias, D
Tao, YF
Mouratidis, K
Hui, CK
Issue Date 2005
Source ACM transactions on database systems, v. 30, (2), 2005, June, p. 529-576
Summary Given two spatial datasets P (e.g., facilities) and Q (queries), an aggregate nearest neighbor (ANN) query retrieves the point(s) of P with the smallest aggregate distance(s) to points in Q. Assuming, for example, n users at locations q(1),... q(n), an ANN query outputs the facility p is an element of P that minimizes the sum of distances \textbackslash{}pq(i)\textbackslash{} for 1 <= i <= n that the users have to travel in order to meet there. Similarly, another ANN query may report the point p is an element of P that minimizes the maximum distance that any user has to travel, or the minimum distance from some user to his/her closest facility. If Q fits in memory and P is indexed by an R-tree, we develop algorithms for aggregate nearest neighbors that capture several versions of the problem, including weighted queries and incremental reporting of results. Then, we analyze their performance and propose cost models for query optimization. Finally, we extend our techniques for disk-resident queries and approximate ANN retrieval. The efficiency of the algorithms and the accuracy of the cost models are evaluated through extensive experiments with real and synthetic datasets.
Subjects
ISSN 0362-5915
Rights © ACM, 2005. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM transactions on Database Systems, v. 30, no. 2, June 2005.
Language English
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