Compact Similarity Joins
Bryan, B.
Eberhardt, F.
Faloutsos, C.
Dept. of Machine Learning, Carnegie Mellon Univ., Pittsburgh, PA;
This paper appears in: Data Engineering, 2008. ICDE 2008. IEEE 24th International Conference on
Publication Date: 7-12 April 2008
On page(s): 346-355
Location: Cancun,
ISBN: 978-1-4244-1836-7
INSPEC Accession Number: 9963790
Digital Object Identifier: 10.1109/ICDE.2008.4497443
Current Version Published: 2008-04-25
Abstract
Similarity joins have attracted significant interest, with applications in geographical information systems, astronomy, marketing analyzes, and anomaly detection. However, all the past algorithms, although highly fine-tuned, suffer an output explosion if the query range is even moderately large relative to the local data density. Under such circumstances, the response time and the search effort are both almost quadratic in the database size, which is often prohibitive. We solve this problem by providing two algorithms that find a compact representation of the similarity join result, while retaining all the information in the standard join. Our algorithms have the following characteristics: (a) they are at least as fast as the standard similarity join algorithm, and typically much faster, (b) they generate significantly smaller output, (c) they provably lose no information, (d) they scale well to large data sets, and (e) they can be applied to any of the standard tree data structures. Experiments on real and realistic point-sets show that our algorithms are up to several orders of magnitude faster.
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