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String Matching with Metric Trees Using an Approximate Distance

  • Conference paper
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String Processing and Information Retrieval (SPIRE 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2476))

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Abstract

Searching in a large data set those strings that are more similar, according to the edit distance, to a given one is a time-consuming process. In this paper we investigate the performance of metric trees, namely the M-tree, when they are extended using a cheap approximate distance function as a filter to quickly discard irrelevant strings. Using the bag distance as an approximation of the edit distance, we show an improvement in performance up to 90% with respect to the basic case. This, along with the fact that our solution is independent on both the distance used in the pre-test and on the underlying metric index, demonstrates that metric indices are a powerful solution, not only for many modern application areas, as multimedia, data mining and pattern recognition, but also for the string matching problem.

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© 2002 Springer-Verlag Berlin Heidelberg

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Bartolini, I., Ciaccia, P., Patella, M. (2002). String Matching with Metric Trees Using an Approximate Distance. In: Laender, A.H.F., Oliveira, A.L. (eds) String Processing and Information Retrieval. SPIRE 2002. Lecture Notes in Computer Science, vol 2476. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45735-6_24

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  • DOI: https://doi.org/10.1007/3-540-45735-6_24

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44158-8

  • Online ISBN: 978-3-540-45735-0

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