ContinuousK-Nearest Neighbor Processing Based on Speed and Direction of Moving Objects in a Road Network.
Recent research has focused on ContinuousK-Nearest Neighbor (CKNN) query over moving objects in road networks. A CKNN query is to find among all moving objects theK-Nearest Neighbors (KNNs) of a moving query point within a given time interval. As the data objects move frequently and arbitrarily in road networks, the frequent updates of object locations make it complicated to process CKNN accurately and efficiently. In this paper, according to the relative moving situation between the moving objects and the query point, a Moving State of Object (MSO) model is presented to indicate the relative moving state of the object to the query point. With the help of this model, we propose a novel Object Candidate Processing (OCP) algorithm to highly reduce the repetitive query cost with pruning phase and refining phase. In the pruning phase, the data objects which cannot be theKNN query results are excluded within the given time interval. In the refining phase, the time subintervals of the given time interval are determined where the certainKNN query results are obtained. Comprehensive experiments are conducted and the results verify the effectiveness of the proposed methods.
School of Computer Science and Technology, Hua Zhong University of Science and Technology, Wuhan, China.
Guohui
G
Li
School of Computer Science and Technology, Hua Zhong University of Science and Technology, Wuhan, China ; School of Computer Science, Xianning University, Hubei,China.
Ping
P
Fan
School of Computer Science and Technology, Hua Zhong University of Science and Technology, Wuhan, China.
Ling
L
Yuan
Continuous K-Nearest Neighbor query; Moving direction; Moving speed; Road network