Efficient processing of high-dimensional similarity joins plays an important role for a wide variety of data-driven applications. In this paper, we consider \documentclass[12pt]{minimal}
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\begin{document}$$\varepsilon $$\end{document}-join variant of the problem. Given two \documentclass[12pt]{minimal}
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\begin{document}$$d$$\end{document}-dimensional datasets and parameter \documentclass[12pt]{minimal}
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\begin{document}$$\varepsilon $$\end{document}, the task is to find all pairs of points, one from each dataset that are within \documentclass[12pt]{minimal}
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\begin{document}$$\varepsilon $$\end{document} distance from each other. We propose a new \documentclass[12pt]{minimal}
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\begin{document}$$\varepsilon $$\end{document}-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.
