Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/41597
Title: Progressive high-dimensional similarity join
Authors: Tok, W.H. 
Bressan, S. 
Lee, M.-L. 
Issue Date: 2007
Source: Tok, W.H.,Bressan, S.,Lee, M.-L. (2007). Progressive high-dimensional similarity join. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 4653 LNCS : 233-242. ScholarBank@NUS Repository.
Abstract: The Rate-Based Progressive Join (RPJ) is a non-blocking relational equijoin algorithm. It is an equijoin that can deliver results progressively. In this paper, we first present a naive extension, called neRPJ, to the progressive computation of the similarity join of highdimensional data. We argue that this naive extension is not suitable. We therefore propose an adequate solution in the form of a Result-Rate Progressive Join (RRPJ) for high-dimensional distance similarity joins. Using both synthetic and real-life datasets, we empirically show that RRPJ is effective and efficient, and outperforms the naive extension. © Springer-Verlag Berlin Heidelberg 2007.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/41597
ISBN: 9783540744672
ISSN: 03029743
Appears in Collections:Staff Publications

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