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 Berlin Heidelberg
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

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