| The V*-Diagram: a query-dependent approach to moving KNN queries |
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Proceedings of the VLDB Endowment
archive
Volume 1 , Issue 1 (August 2008)
table of contents
SESSION: Spatial and motion data
table of contents
Pages 1095-1106
Year of Publication: 2008
ISSN:2150-8097
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Authors
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Sarana Nutanong
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University of Melbourne, Victoria, Australia and NICTA Victoria Laboratory, Australia
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Rui Zhang
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University of Melbourne, Victoria, Australia
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Egemen Tanin
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University of Melbourne, Victoria, Australia and NICTA Victoria Laboratory, Australia
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Lars Kulik
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University of Melbourne, Victoria, Australia and NICTA Victoria Laboratory, Australia
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Downloads (6 Weeks): 4, Downloads (12 Months): 76, Citation Count: 1
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ABSTRACT
The moving k nearest neighbor (MkNN) query finds the k nearest neighbors of a moving query point continuously. The high potential of reducing the query processing cost as well as the large spectrum of associated applications have attracted considerable attention to this query type from the database community. This paper presents an incremental safe-region-based technique for answering MkNN queries, called the V*-Diagram. In general, a safe region is a set of points where the query point can move without changing the query answer. Traditional safe-region approaches compute a safe region based on the data objects but independent of the query location. Our approach exploits the current knowledge of the query point and the search space in addition to the data objects. As a result, the V*-Diagram has much smaller IO and computation costs than existing methods. The experimental results show that the V*-Diagram outperforms the best existing technique by two orders of magnitude.
REFERENCES
Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.
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[doi> 10.1145/872757.872812]
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CITED BY
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Zaiben Chen , Heng Tao Shen , Xiaofang Zhou , Jeffrey Xu Yu, Monitoring path nearest neighbor in road networks, Proceedings of the 35th SIGMOD international conference on Management of data, June 29-July 02, 2009, Providence, Rhode Island, USA
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