Super-EGO: fast multi-dimensional similarity join

Super-EGO: fast multi-dimensional similarity join Efficient processing of high-dimensional similarity joins plays an important role for a wide variety of data-driven applications. In this paper, we consider $$\varepsilon $$ -join variant of the problem. Given two $$d$$ -dimensional datasets and parameter $$\varepsilon $$ , the task is to find all pairs of points, one from each dataset that are within $$\varepsilon $$ distance from each other. We propose a new $$\varepsilon $$ -join algorithm, called Super-EGO , which belongs the EGO family of join algorithms. The new algorithm gains its advantage by using novel data-driven dimensionality re-ordering technique, developing a new EGO-strategy that more aggressively avoids unnecessary computation, as well as by developing a parallel version of the algorithm. We study the newly proposed Super-EGO algorithm on large real and synthetic datasets. The empirical study demonstrates significant advantage of the proposed solution over the existing state of the art techniques. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The VLDB Journal Springer Journals

Super-EGO: fast multi-dimensional similarity join

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Publisher
Springer Journals
Copyright
Copyright © 2013 by Springer-Verlag Berlin Heidelberg
Subject
Computer Science; Database Management
ISSN
1066-8888
eISSN
0949-877X
D.O.I.
10.1007/s00778-012-0305-7
Publisher site
See Article on Publisher Site

Abstract

Efficient processing of high-dimensional similarity joins plays an important role for a wide variety of data-driven applications. In this paper, we consider $$\varepsilon $$ -join variant of the problem. Given two $$d$$ -dimensional datasets and parameter $$\varepsilon $$ , the task is to find all pairs of points, one from each dataset that are within $$\varepsilon $$ distance from each other. We propose a new $$\varepsilon $$ -join algorithm, called Super-EGO , which belongs the EGO family of join algorithms. The new algorithm gains its advantage by using novel data-driven dimensionality re-ordering technique, developing a new EGO-strategy that more aggressively avoids unnecessary computation, as well as by developing a parallel version of the algorithm. We study the newly proposed Super-EGO algorithm on large real and synthetic datasets. The empirical study demonstrates significant advantage of the proposed solution over the existing state of the art techniques.

Journal

The VLDB JournalSpringer Journals

Published: Aug 1, 2013

References

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