On efficient mutual nearest neighbor query processing in spatial databases
Data & Knowledge Engineering
Incremental Reverse Nearest Neighbor Ranking in Vector Spaces
SSTD '09 Proceedings of the 11th International Symposium on Advances in Spatial and Temporal Databases
Reverse ranking query over imprecise spatial data
Proceedings of the 1st International Conference and Exhibition on Computing for Geospatial Research & Application
Efficient RkNN retrieval with arbitrary non-metric similarity measures
Proceedings of the VLDB Endowment
User-Centric Similarity and Proximity Measures for Spatial Personalization
International Journal of Data Warehousing and Mining
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Given a set of data points P and a query point q in a multidimensional space, Reverse Nearest Neighbor (RNN) query finds data points in P whose nearest neighbors are q. Reverse k-Nearest Neighbor (RkNN) query (where k ≥ 1) generalizes RNN query to find data points whose kNNs include q. For RkNN query semantics, q is said to have influence to all those answer data points. The degree of q's influence on a data point p (∈ P) is denoted by κp where q is the κp-th NN of p. We introduce a new variant of RNN query, namely, Ranked Reverse Nearest Neighbor (RRNN) query, that retrieves t data points most influenced by q, i.e., the t data points having the smallest κ's with respect to q. To answer this RRNN query efficiently, we propose two novel algorithms, κ-Counting and κ-Browsing that are applicable to both monochromatic and bichromatic scenarios and are able to deliver results progressively. Through an extensive performance evaluation, we validate that the two proposed RRNN algorithms are superior to solutions derived from algorithms designed for RkNN query.