22nd International Conference on Data Engineering (ICDE'06) (2006)
Apr. 3, 2006 to Apr. 7, 2006
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDE.2006.129
Man Lung Yiu , University of Hong Kong
Nikos Mamoulis , University of Hong Kong
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.
N. Mamoulis and M. L. Yiu, "Reverse Nearest Neighbors Search in Ad-hoc Subspaces," 22nd International Conference on Data Engineering (ICDE'06)(ICDE), Atlanta, Georgia, 2006, pp. 76.