Scalable Processing of Incremental Continuous k-Nearest Neighbor Queries

Abstract

To evaluate the large collection of concurrent CKNN (continuous k-nearest neighbor) queries continuously, a scalable processing of the incremental continuous k-nearest neighbor (SI-CNN) framework is proposed by introducing searching region to filter the visiting TPR-tree (time-parameterized R-tree) nodes. SI-CNN framework exploits the incremental results table to buffer the candidate objects, flushes the objects into query results in bulk, efficiently processes the large number of CKNN queries concurrently, and has a perfect scalability. An incremental SI-CNN query update algorithm is presented, which evaluates incrementally based on the former query answers and supports the insertion or deletion of both query collection and moving objects. Experimental results and analysis show that SI-CNN algorithm based on SI-CNN framework can support a large set of concurrent CKNN queries perfectly, and has a good practical application.