Practical Random Access to SLP-Compressed Texts
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.
Cite
@article{arxiv.1910.07145,
title = {Practical Random Access to SLP-Compressed Texts},
author = {Travis Gagie and Tomohiro I and Giovanni Manzini and Gonzalo Navarro and Hiroshi Sakamoto and Louisa Seelbach Benkner and Yoshimasa Takabatake},
journal= {arXiv preprint arXiv:1910.07145},
year = {2020}
}
Comments
Accepted to SPIRE 2020