Influence sets based on reverse nearest neighbor queries
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
An Index Structure for Efficient Reverse Nearest Neighbor Queries
Proceedings of the 17th International Conference on Data Engineering
High dimensional reverse nearest neighbor queries
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Continuous Reverse Nearest Neighbor Monitoring
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Nearest and reverse nearest neighbor queries for moving objects
The VLDB Journal — The International Journal on Very Large Data Bases
Multidimensional reverse kNN search
The VLDB Journal — The International Journal on Very Large Data Bases
FINCH: evaluating reverse k-Nearest-Neighbor queries on location data
Proceedings of the VLDB Endowment
Lazy updates: an efficient technique to continuously monitoring reverse kNN
Proceedings of the VLDB Endowment
Efficient k-nearest neighbor search on moving object trajectories
The VLDB Journal — The International Journal on Very Large Data Bases
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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.