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arXiv · 1910.07145

Practical Random Access to SLP-Compressed Texts

Travis Gagie·Tomohiro I·Giovanni Manzini·Gonzalo Navarro·Hiroshi Sakamoto·Louisa Seelbach Benkner·Yoshimasa Takabatake
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Abstract

Grammar-based compression is a popular and powerful approach to compressing repetitive texts but until recently its relatively poor time-space trade-offs during real-life construction made it impractical for truly massive datasets such as genomic databases. In a recent paper (SPIRE 2019) we showed how simple pre-processing can dramatically improve those trade-offs, and in this paper we turn our attention to one of the features that make grammar-based compression so attractive: the possibility of supporting fast random access. This is an essential primitive in many algorithms that process grammar-compressed texts without decompressing them and so many theoretical bounds have been published about it, but experimentation has lagged behind. We give a new encoding of grammars that is about as small as the practical state of the art (Maruyama et al., SPIRE 2013) but with significantly faster queries.

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Travis Gagie, Tomohiro I, Giovanni Manzini, Gonzalo Navarro, Hiroshi Sakamoto, Louisa Seelbach Benkner, Yoshimasa Takabatake. 2019-10-16. Practical Random Access to SLP-Compressed Texts. https://arxiv.org/abs/1910.07145

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