Efficient filter algorithms for reverse k-nearest neighbor query

Reverse k-Nearest Neighbor (RkNN) Queries have got considerable attentions over the recent years. Most state of the art methods use the two-step (filter-refinement) RkNN processing. However, for a large k, the amount of calculation becomes very heavy, especially in the filter step. This is not acceptable for most mobile devices. A new filter strategy called BRC is proposed to deal with the filter step for RkNN queries. There are two pruning heuristics in BRC. The experiments show that the processing time of BRC is still acceptable for most mobile devices when k is large. And we extend the BRC to the continuous RkNN queries.