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Given a set of objects and a query q, a point p is q’s Reverse k Nearest Neighbour (RkNN) if q is one of p’s k-closest objects. RkNN queries have received significant research attention in the past few years. However, we realize that the state-of-the-art algorithm, SLICE, accesses many objects that do not contribute to its RkNN results when running the filtering phase, which deteriorates the query performance. In this paper, we propose a novel RkNN algorithm with pre-computation by partitioning the data space into disjoint rectangular regions and constructing the guardian set for each region R. We guarantee that, for each q that lies in R, its RkNN results are only affected by the objects in R’s guardian set. The advantage of this approach is that the results of a query $$q\in R$$q∈R can be computed by using SLICE on only the objects in its guardian set instead of using the whole dataset. Besides, we raise two new useful variants of RkNN and propose algorithms. Our comprehensive experimental study on synthetic and real the proposed approaches are the most efficient algorithms for RkNN and its variants.

Pre-computed Region Guardian Sets Based Reverse kNN Queries