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| Continuous K-Nearest Neighbor Queries for Uncertain Moving Objects |
| YU Yanwei1, QI Jianpeng1, SONG Peng1, ZHANG Yonggang2 |
1.School of Computer and Control Engineering, Yantai University, Yantai 264005
2.Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012 |
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Abstract An urgent problem in location-based services is continuous K-nearest neighbor (KNN) queries for uncertain moving objects. An efficient algorithm for continuous K-nearest neighbor queries for uncertain moving objects is proposed. Firstly, two solutions, MaxMin and Rate, are proposed to predict the possible location range of the moving object in the time interval by utilizing the sampling points with velocities in the recent time window. A closed interval of minimum and maximum distances is employed to represent the distance between the query object and the moving object. Secondly, an optimized ranking method based on vague possibility decision is proposed to quickly find KNNs of the query object. Finally, experimental results on real and synthetic large-scale datasets demonstrate the effectiveness of the proposed algorithm.
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Received: 02 March 2016
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| Fund:Supported by National Natural Science Foundation of China (No.61572419,61403328,61302065), Natural Science Foundation of Shandong Province (No.ZR2014FQ016,ZR2013FM011), Key Program for Research and Development of Shandong Province (No.J2015GSF115009), Open Project of Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education of Jilin University (No.93K172014K13) |
About author:: YU YanweiCorresponding author, born in 1986, Ph.D., lecturer. His research interests include data mining and distributed computing.
QI Jianpeng, born in 1992, master student. His research interests include data mining.
SONG Peng, born in 1983, Ph.D., lecturer. His research interests include machine learning and data mining.
ZHANG Yonggang, born in 1974, Ph.D., associate profe-ssor. His research interests include data mining and knowledge engineering. |
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2016, Vol. 29 
