Abstract
A reverse k-nearest neighbour (RkNN) query determines the objects from a database that have the query as one of their k-nearest neighbors. Processing such a query has received plenty of attention in research. However, the effect of running multiple RkNN queries at once (join) or within a short time interval (bulk/group query) has only received little attention so far. In this paper, we analyze different types of RkNN joins and discuss possible solutions for solving the non-trivial variants of this problem, including self and mutual pruning strategies. The results indicate that even with a moderate number of query objects (|R| ≈ 0.0007|S|), the performance (CPU) of the state-of-the-art mutual pruning based RkNN-queries deteriorates and hence algorithms based on self pruning without precomputation produce better results. During an extensive performance analysis we provide evaluation results showing the IO and CPU performance of the compared algorithms for a wide range of different setups and suggest appropriate query algorithms for specific scenarios.
Part of this work was supported by the DFG under grant number KR 3358/4-1.
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Emrich, T., Kriegel, HP., Kröger, P., Niedermayer, J., Renz, M., Züfle, A. (2013). Reverse-k-Nearest-Neighbor Join Processing. In: Nascimento, M.A., et al. Advances in Spatial and Temporal Databases. SSTD 2013. Lecture Notes in Computer Science, vol 8098. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40235-7_16
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DOI: https://doi.org/10.1007/978-3-642-40235-7_16
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