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High Dimensional Similarity Joins: Algorithms and Performance Evaluation
January/February 2000 (vol. 12 no. 1)
pp. 3-18

Abstract—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 finding relationships among points in multidimensional feature spaces, specifically 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 specific algorithms, comparing their performance for various dimensionalities on both real and synthetic multidimensional data sets. Our experimental results indicate that MSJ, which is based on space filling curves, consistently yields good performance across a wide range of dimensionalities.

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Index Terms:
Spatial join, sort merge joins, multiple-key indexes, data structures.
Citation:
Nick Koudas, Kenneth C. Sevcik, "High Dimensional Similarity Joins: Algorithms and Performance Evaluation," IEEE Transactions on Knowledge and Data Engineering, vol. 12, no. 1, pp. 3-18, Jan.-Feb. 2000, doi:10.1109/69.842246
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