Incremental reverse nearest neighbor ranking in vector spaces

Abstract

In this paper, we formalize the novel concept of incremental reverse nearest neighbor ranking and suggest an original solution for this problem. We propose an efficient approach for reporting the results incrementally without the need to restart the search from scratch. Our approach can be applied to a multi-dimensional feature database which is hierarchically organized by any R-tree like index structure. Our solution does not assume any preprocessing steps which makes it applicable for dynamic environments where updates of the database frequently occur. Experiments show that our approach reports the ranking results with much less page accesses than existing approaches designed for traditional reverse nearest neighbor search applied to the ranking problem. © 2009 Springer Berlin Heidelberg.