Abstract
A k-nearest neighbor (k-NN) query, which retrieves nearest k points from a database is one of the fundamental query types in spatial databases. An all k-nearest neighbor query (AkNN query), a variation of a k-NN query, determines the k-nearest neighbors for each point in the dataset in a query process. In this paper, we propose a method for processing AkNN queries in Hadoop. We decompose the given space into cells and execute a query using the MapReduce framework in a distributed and parallel manner. Using the distribution statistics of the target data points, our method can process given queries efficiently.
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Yokoyama, T., Ishikawa, Y., Suzuki, Y. (2012). Processing All k-Nearest Neighbor Queries in Hadoop. In: Gao, H., Lim, L., Wang, W., Li, C., Chen, L. (eds) Web-Age Information Management. WAIM 2012. Lecture Notes in Computer Science, vol 7418. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32281-5_34
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DOI: https://doi.org/10.1007/978-3-642-32281-5_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32280-8
Online ISBN: 978-3-642-32281-5
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