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b-4 - Aggregate Nearest Neighbor Queries in Spatial...

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Aggregate Nearest Neighbor Queries in Spatial Databases DIMITRIS PAPADIAS Hong Kong University of Science and Technology YUFEI TAO City University of Hong Kong and KYRIAKOS MOURATIDIS and CHUN KIT HUI Hong Kong University of Science and Technology 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 P that minimizes the sum of distances | pq i | for 1 i n that the users have to travel in order to meet there. Similarly, another ANN query may report the point p 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. Categories and Subject Descriptors: H.2 [ Database Management ]; H3.3 [ Information Storage and Retrieval ]: Information Search and Retrieval General Terms: Algorithms, Experimentation Additional Key Words and Phrases: Spatial database, nearest neighbor queries, aggregation This research was supported by the grant HKUST 6180/03E and CityU 1163/04E from Hong Kong RGC. Authors’ addresses: D. Papadias, K. Mouratidis, and C. K. Hui, Department of Computer Science, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China; email: { dimitris,kyriakos,michaelh } @cs.ust.hk; Y. Tao, Department of Computer Science, City University of Hong Kong, Tat Chee Avenue, Hong Kong, China; email: [email protected] Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or direct commercial advantage and that copies show this notice on the first page or initial screen of a display along with the full citation. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, to redistribute to lists, or to use any component of this work in other works requires prior specific permission and/or a fee. Permissions may be requested from Publications Dept., ACM, Inc., 1515 Broadway, New York, NY 10036 USA, fax: + 1 (212) 869-0481, or [email protected]
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