Abstract
We address the problem of improving variable-length-to- fixed-length codes (VF codes), which have favourable properties for fast compressed pattern matching but moderate compression ratios. Compression ratio of VF codes depends on the parse tree that is used as a dictionary. We propose a method that trains a parse tree by scanning an input text repeatedly, and we show experimentally that it improves the compression ratio of VF codes rapidly to the level of state-of-the-art compression methods.
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Uemura, T., Yoshida, S., Kida, T., Asai, T., Okamoto, S. (2010). Training Parse Trees for Efficient VF Coding. In: Chavez, E., Lonardi, S. (eds) String Processing and Information Retrieval. SPIRE 2010. Lecture Notes in Computer Science, vol 6393. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16321-0_17
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DOI: https://doi.org/10.1007/978-3-642-16321-0_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16320-3
Online ISBN: 978-3-642-16321-0
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