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A variety of techniques for performing a spatial join are reviewed. Instead of just summarizing the literature and presenting each technique in its entirety, distinct components of the different techniques are described and each is decomposed into an overall framework for performing a spatial join. A typical spatial join technique consists of the following(More)
Similarity join algorithms find pairs of objects that lie within a certain distance ε of each other. Algorithms that are adapted from spatial join techniques are designed primarily for data in a vector space and often employ some form of a multidimensional index. For these algorithms, when the data lies in a metric space, the usual solution is to embed(More)
The Hausdorff distance is commonly used as a similarity measure between two point sets. Using this measure, a set X is considered similar to Y iff every point in X is close to at least one point in Y. Formally, the Hausdorff distance HAUSDIST(X, Y) can be computed as the MAX-MIN distance from X to Y , i.e., find the maximum of the distance from an element(More)
The key issue in performing spatial joins is finding the pairs of intersecting rectangles. For unindexed data sets, this is usually resolved by partitioning the data and then performing a plane sweep on the individual partitions. The resulting join can be viewed as a two-step process where the partition corresponds to a hash-based join while the plane-sweep(More)
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