Continuous K-Nearest Neighbor processing based on speed and direction of moving objects in a road network

Recent research has focused on Continuous K-Nearest Neighbor (CKNN) query over moving objects in road networks. A CKNN query is to find among all moving objects the K-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 the KNN 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 certain KNN query results are obtained. Comprehensive experiments are conducted and the results verify the effectiveness of the proposed methods.