Reverse Nearest Neighbors Search in Ad Hoc Subspaces
IEEE Transactions on Knowledge and Data Engineering
Efficient processing of probabilistic reverse nearest neighbor queries over uncertain data
The VLDB Journal — The International Journal on Very Large Data Bases
On multi-type reverse nearest neighbor search
Data & Knowledge Engineering
On efficient obstructed reverse nearest neighbor query processing
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Reverse nearest neighbor search in peer-to-peer systems
FQAS'06 Proceedings of the 7th international conference on Flexible Query Answering Systems
Probabilistic top-k dominating queries in uncertain databases
Information Sciences: an International Journal
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Given an object q, modeled by a multidimensional point, a reverse nearest neighbors (RNN) query returns the set of objects in the database that have q as their nearest neighbor. In this paper, we study an interesting generalization of the RNN query, where not all dimensions are considered, but only an ad-hoc subset thereof. The rationale is that (i) the dimensionality might be too high for the result of a regular RNN query to be useful, (ii) missing values may implicitly define a meaningful subspace for RNN retrieval, and (iii) analysts may be interested in the query results only for a set of (ad-hoc) problem dimensions (i.e., object attributes). We consider a suitable storage scheme and develop appropriate algorithms for projected RNN queries, without relying on multidimensional indexes. Our methods are experimentally evaluated with real and synthetic data.