Towards Nearest Collection Search on Spatial Databases

In this paper, for the first time, we present the concept of nearest collection (NC) search. Given a set of spatial data points D and a query point q, a nearest collection search retrieves a certain subset c (|c| = k), called collection from D. We formally define a collection as clustered k objects and the nearest collection search problem. Since the brute-force approach of this problem requires large computational cost, we propose two approaches using database techniques to reduce search space. The first approach is the multiple query method which uses existing method (i.e. k-nearest neighbor query) based on normal R-tree. The second approach is the effective NC query processing based on the branch and bound method using an aggregate R-tree (simply aR-tree). Our experimental results show that the efficiency and effectiveness of our proposed approach.