Based on the Half-space Pruning to Continuously Monitoring Reverse KNN

Abstract

Reverse k Nearest Neighbor (RKNN) query is proposed based on the k Nearest Neighbor (KNN) query, which can be used to evaluate the influence of the query objects. At present RKNN query algorithms are mostly based on static or continuous objects, however, with the widespread of mobile device, algorithms for efficiently answering queries about large populations of moving objects are gaining interest. Given a group of nearby space objects as the query input, the authors propose the continuously monitoring RKNN algorithms based on the half-space pruning, compute the minimal enclosing circle containing the query objects and consider the objects in the circle as a whole. Then, in order to get the query's final RKNN results, we design a filter method based on R-tree and a refinement process to realize continuous RKNN monitoring. Keywords-Half-space Pruning, RKNN, Continuously Monitoring, minimal enclosing circle