Continuous Nearest Neighbor Queries over Sliding Windows
Recent research has focused on continuous monitoring of nearest neighbors (NN) in highly dynamic scenarios, where the queries and the data objects move frequently and arbitrarily. All existing methods, however, assume the Euclidean distance metric. In this paper we study k-NN monitoring in road netw...
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sg-smu-ink.sis_research-12032010-09-24T05:42:03Z Continuous Nearest Neighbor Queries over Sliding Windows MOURATIDIS, Kyriakos Papadias, Dimitris Recent research has focused on continuous monitoring of nearest neighbors (NN) in highly dynamic scenarios, where the queries and the data objects move frequently and arbitrarily. All existing methods, however, assume the Euclidean distance metric. In this paper we study k-NN monitoring in road networks, where the distance between a query and a data object is determined by the length of the shortest path connecting them. We propose two methods that can handle arbitrary object and query moving patterns, as well as fluctuations of edge weights. The first one maintains the query results by processing only updates that may invalidate the current NN sets. The second method follows the shared execution paradigm to reduce the processing time. In particular, it groups together the queries that fall in the path between two consecutive intersections in the network, and produces their results by monitoring the NN sets of these intersections. We experimentally verify the applicability of the proposed techniques to continuous monitoring of large data and query sets. 2007-06-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/204 info:doi/10.1109/TKDE.2007.190617 https://ink.library.smu.edu.sg/context/sis_research/article/1203/viewcontent/TKDE07_CNM.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Databases and Information Systems Numerical Analysis and Scientific Computing |
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Databases and Information Systems Numerical Analysis and Scientific Computing MOURATIDIS, Kyriakos Papadias, Dimitris Continuous Nearest Neighbor Queries over Sliding Windows |
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Recent research has focused on continuous monitoring of nearest neighbors (NN) in highly dynamic scenarios, where the queries and the data objects move frequently and arbitrarily. All existing methods, however, assume the Euclidean distance metric. In this paper we study k-NN monitoring in road networks, where the distance between a query and a data object is determined by the length of the shortest path connecting them. We propose two methods that can handle arbitrary object and query moving patterns, as well as fluctuations of edge weights. The first one maintains the query results by processing only updates that may invalidate the current NN sets. The second method follows the shared execution paradigm to reduce the processing time. In particular, it groups together the queries that fall in the path between two consecutive intersections in the network, and produces their results by monitoring the NN sets of these intersections. We experimentally verify the applicability of the proposed techniques to continuous monitoring of large data and query sets. |
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text |
author |
MOURATIDIS, Kyriakos Papadias, Dimitris |
author_facet |
MOURATIDIS, Kyriakos Papadias, Dimitris |
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MOURATIDIS, Kyriakos |
title |
Continuous Nearest Neighbor Queries over Sliding Windows |
title_short |
Continuous Nearest Neighbor Queries over Sliding Windows |
title_full |
Continuous Nearest Neighbor Queries over Sliding Windows |
title_fullStr |
Continuous Nearest Neighbor Queries over Sliding Windows |
title_full_unstemmed |
Continuous Nearest Neighbor Queries over Sliding Windows |
title_sort |
continuous nearest neighbor queries over sliding windows |
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Institutional Knowledge at Singapore Management University |
publishDate |
2007 |
url |
https://ink.library.smu.edu.sg/sis_research/204 https://ink.library.smu.edu.sg/context/sis_research/article/1203/viewcontent/TKDE07_CNM.pdf |
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