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Please use this identifier to cite or link to this item:
http://hdl.handle.net/1783.1/2273
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| Title: | Reverse kNN search arbitrary dimensionality |
| Authors: | Tao, Yufei
Papadias, Dimitris
Lian, Xiang |
| Keywords: | Reverse nearest neighbors
Query
Algorithms
Arbitrary dimensionality
Dynamic multidimensional datasets |
| Issue Date: | Aug-2004 |
| Citation: | Proceedings of the Very Large Data Bases Conference, Aug. 30-Sept. 3, 2004, Toronto, Canada, p. 744-755 |
| Abstract: | Given a point q, a reverse k nearest neighbor (RkNN) query retrieves all the data points that have q as one of their k nearest neighbors. Existing methods for processing such queries have at least one of the following deficiencies: (i) they do not support arbitrary values of k (ii) they cannot deal efficiently with database updates, (iii) they are applicable only to 2D data (but not to higher dimensionality), and (iv) they retrieve only approximate results. Motivated by these shortcomings, we develop algorithms for exact processing of RkNN with arbitrary values of k on dynamic multidimensional datasets. Our methods utilize a conventional data-partitioning index on the dataset and do not require any pre-computation. In addition to their flexibility, we experimentally verify that the proposed algorithms outperform the existing ones even in their restricted focus. |
| Rights: | The original publication is available at http://www.springerlink.com/ |
| URI: | http://hdl.handle.net/1783.1/2273 |
| Appears in Collections: | CSE Conference Papers
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Files in This Item:
| File |
Description |
Size | Format |
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| VLDB04RNN.pdf | pre-published version | 231Kb | Adobe PDF | View/Open |
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