Scalable processing of continuous K-nearest neighbor queries with uncertain velocity

被引:0
作者
Lin, Lien-Fa [1 ]
Huang, Yuan-Ko [1 ]
机构
[1] Kao Yuan Univ, Dept Informat Commun, Kaohsiung, Taiwan
关键词
Continuous K-nearest neighbor query; Spatio-temporal queries; K-nearest neighbors; Moving query object; Moving objects;
D O I
10.1016/j.eswa.2011.01.028
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Continuous K-nearest neighbor (CKNN) query is an important type of spatio-temporal queries. Given a time interval [t(s), t(e)] and a moving query object q, a CKNN query is to find the K-nearest neighbors (KNNs) of q at each time instant within [t(s), t(e)]. In this paper, we focus on the issue of scalable processing of CKNN queries over moving objects with uncertain velocity. Due to the large amount of CKNN queries that need to be evaluated concurrently, efficiently processing such queries inevitably becomes more complicated. We propose an index structure, namely the CI-tree, to predetermine and organize the candidates for each query issued by the user from anywhere and anytime. When the CKNN queries are evaluated, their corresponding candidates can be rapidly retrieved by traversing the CI-tree so that the processing time is greatly reduced. A comprehensive set of experiments is performed to demonstrate the effectiveness and the efficiency of the CI-tree. (C) 2011 Elsevier Ltd. All rights reserved.
引用
收藏
页码:9256 / 9265
页数:10
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