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ICDE
1998
IEEE

High Dimensional Similarity Joins: Algorithms and Performance Evaluation

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High Dimensional Similarity Joins: Algorithms and Performance Evaluation
Current data repositories include a variety of data types, including audio, images and time series. State of the art techniques for indexing such data and doing query processing rely on a transformation of data elements into points in a multidimensional feature space. Indexing and query processing then take place in the feature space. In this paper, we study algorithms for nding relationships among points in multidimensional feature spaces, speci cally algorithms for multidimensional joins. Like joins of conventional relations, correlations between multidimensional feature spaces can offer valuable information about the data sets involved. We present several algorithmic paradigms for solving the multidimensional join problem, and we discuss their features and limitations. We propose a generalization of the Size Separation Spatial Join algorithm, named Multidimensional Spatial Join MSJ, to solve the multidimensional join problem. We evaluate MSJ along with several other speci c algorit...
Nick Koudas, Kenneth C. Sevcik
Added 01 Nov 2009
Updated 01 Nov 2009
Type Conference
Year 1998
Where ICDE
Authors Nick Koudas, Kenneth C. Sevcik
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