Fast similarity join for multi-dimensional data

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چکیده

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Fast similarity join for multi-dimensional data

To appear in Information Systems Journal, Elsevier, 2005 The efficient processing of multidimensional similarity joins is important for a large class of applications. The dimensionality of the data for these applications ranges from low to high. Most existing methods have focused on the execution of high-dimensional joins over large amounts of disk-based data. The increasing sizes of main memor...

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SimRank is a well-studied similarity measure between two nodes in a network. However, evaluating SimRank of all nodes in a network is not only time-consuming but also not pragmatic, since users are only interested in the most similar pairs in many real-world applications. This paper focuses on topk similarity join based on SimRank. In this work, we first present an incremental algorithm for com...

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Multidimensional similarity join finds pairs of multidimensional points that are within some small distance of each other. The -k-d-B tree has been proposed as a data structure that scales better as the number of dimensions increases compared to previous data structures. We present a cost model of the -k-d-B tree and use it to optimize the leaf

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ژورنال

عنوان ژورنال: Information Systems

سال: 2007

ISSN: 0306-4379

DOI: 10.1016/j.is.2005.07.002