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Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
This paper focus on continuous K-nearest neighbor (CKNN for short) query and propose a query method based on R-Tree and Quad Tree (QR-Tree) to support continuous K-nearest neighbor query for moving objects, in which the main idea is to use a QR-Tree to divide the static spatial space for the moving objects. In the interested region, it uses the QR-Tree and hash tables as an index to store the moving object, and then calculates the distances between the query point and the moving objects to get the result. The comprehensive experimentation shows that the performance of the proposed method is better than existing methods in query efficiency and resource consumption.