Reverse Nearest Neighbor Queries for Dynamic Databases

In this paper we propose an algorithm for answering reverse nearest neighbor (RNN) queries, a problem formulated only recently. This class of queries is strongly related to that of nearest neighbor (NN) queries, although the two are not necessarily complementary. Unlike nearest neighbor queries, RNN queries nd the set of database points that have the query point as the nearest neighbor. There is no other proposal we are aware of, that provides an algorithmic approach to answer RNN queries. The earlier approach for RNN queries ((KM99]) is based on the pre-computation of neighborhood information that is organized in terms of auxiliary data structures. It can be argued that the pre-computation of the RNN information for all points in the database can be too restrictive. In the case of dynamic databases, insert and update operations are expensive and can lead to modiications of large parts of the auxiliary data structures. Also, answers to RNN queries for a set of data points depend on the number of dimensions taken in considerations when initializing the data structures. We propose an algorithmic approach that is exible enough to support a larger class of RNN queries, and, in order to support them, we also extend the current method of nearest neighbor search to that of conditional nearest neighbor.