Abstract
We propose an original solution for the general reverse k-nearest neighbor (RkNN) search problem in Euclidean spaces. Compared to the limitations of existing methods for the RkNN search, our approach works on top of Multi-Resolution Aggregate (MRA) versions of any index structures for multidimensional feature spaces where each non-leaf node is additionally associated with aggregate information like the sum of all leaf-entries indexed by that node. Our solution outperforms the state-of-the-art RkNN algorithms in terms of query execution times because it exploits advanced strategies for pruning index entries.
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Kriegel, HP., Kröger, P., Renz, M., Züfle, A., Katzdobler, A. (2009). Reverse k-Nearest Neighbor Search Based on Aggregate Point Access Methods. In: Winslett, M. (eds) Scientific and Statistical Database Management. SSDBM 2009. Lecture Notes in Computer Science, vol 5566. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02279-1_32
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DOI: https://doi.org/10.1007/978-3-642-02279-1_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02278-4
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