OQ-Quad: An Efficient Query Processing for Continuous K-Nearest Neighbor Based on Quad Tree

With the growing popularity of mobile computing and wireless communications, managing the continuously changing information of the moving objects is becoming feasible, especially in LBS application which is characterized by a large number of moving objects and a large number of continuous queries. In this paper, we focus on continuous k-nearest neighbor (CkNN for short) query and propose a query method based on a quad tree to support continuous k-nearest neighbor query for moving objects, in which the main idea is to use a quad tree to divide the static spatial space for the moving objects. In the interested region, we use the quad tree and hash tables as an index to store the moving objects. Then we calculate the distances between the query point and the moving objects from inside to outside to get the result. Our comprehensive experimentation shows that the performance of the proposed method is better in memory consumption and processing time than the CMP algorithm.