Abstract
Given a point q, a reverse k-nearest neighbor (RkNN) query retrieves all the data points that have q as one of their k-nearest neighbors. RkNN search has received increasing attention recently and has been applied to many applications such as decision support systems, geographic information systems (GIS), and outlier detection. However, most existing methods can deal with low-dimensional or small-scale data, and it is still challenging to handle high-dimensional or large-scale data. In this paper, we propose a GPU-accelerated method of RkNN search for high-dimensional and large-scale data. We divide the process into two parts and implement each on GPU. We experimentally verify that the proposed method outperforms the baseline method implemented on CPU.
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Acknowledgements
This paper was supported by Japan Society for the Promotion of Science (JSPS) KAKENHI under Grant Number JP22H03694 and the New Energy and Industrial Technology Development Organization (NEDO) Grant Number JPNP20006.
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Tsuihiji, K., Amagasa, T. (2022). GPU-Accelerated Reverse K-Nearest Neighbor Search for High-Dimensional Data. In: Barolli, L., Miwa, H., Enokido, T. (eds) Advances in Network-Based Information Systems. NBiS 2022. Lecture Notes in Networks and Systems, vol 526. Springer, Cham. https://doi.org/10.1007/978-3-031-14314-4_28
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DOI: https://doi.org/10.1007/978-3-031-14314-4_28
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