Metric space similarity joins

Quicker Similarity Joins in Metric Spaces

We consider the join operation in metric spaces. Given two sets A and B of objects drawn from some universe U, we want to compute the set A  B = {(a, b) ∈ A × B | d(a, b) ≤ r} efficiently, where d : U×U → R is a metric distance function and r ∈ R is user supplied query radius. In particular we are interested in the case where we have no index available (nor we can afford to build it) for eithe...

Exploiting Database Similarity Joins for Metric Spaces

Similarity Joins are recognized among the most useful data processing and analysis operations and are extensively used in multiple application domains. They retrieve all data pairs whose distances are smaller than a predefined threshold ε. Multiple Similarity Join algorithms and implementation techniques have been proposed. They range from out-of-database approaches for only in-memory and exter...

Similarity Search - The Metric Space Approach

High-dimensional Similarity Joins

A. Toga. QBISM: a prototype 3-d medical image database system. B. Seeger. The R-tree: an efficient and robust access method for points and rectangles. [7] C. Faloutsos and K.-I. Lin. Fastmap: A fast algorithm for indexing, data-mining and visualization of traditional and multimedia datasets. [8] A. Guttman. R-trees: a dynamic index structure for spatial searching. [12] D. Lomet and B. Salzberg....

High-Dimensional Similarity Joins

Many emerging data mining applications require a similarity join between points in a high-dimensional domain. We present a new algorithm that utilizes a new index structure, called the -kdB tree, for fast spatial similarity joins on high-dimensional points. This index structure reduces the number of neighboring leaf nodes that are considered for the join test, as well as the traversal cost of f...