Practical Random Access to SLP-Compressed Texts

TL;DR

This paper improves random access efficiency in grammar-based compressed texts, making it practical for large datasets like genomic databases by introducing a new encoding that offers faster queries without increasing size.

Contribution

It presents a new grammar encoding that achieves faster random access queries while maintaining a size comparable to the state of the art.

Findings

Faster random access queries compared to previous methods

Comparable compression size to existing practical approaches

Enhanced applicability to large-scale datasets like genomics

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.