Lightweight Data Indexing and Compression in External Memory

TL;DR

This paper introduces lightweight algorithms for computing the Burrows-Wheeler Transform (BWT) and building compressed indexes in external memory, significantly reducing disk space usage and optimizing disk access patterns for efficiency.

Contribution

It presents novel lightweight algorithms that use only linear bits of disk space and rely solely on sequential scans, improving over previous methods that required much more disk space.

Findings

Algorithms use only n bits of disk space

Sequential scans enable faster disk access

New lower bounds on BWT computation complexity

Abstract

In this paper we describe algorithms for computing the BWT and for building (compressed) indexes in external memory. The innovative feature of our algorithms is that they are lightweight in the sense that, for an input of size $n$, they use only ${n}$ bits of disk working space while all previous approaches use $\Th{n \log n}$ bits of disk working space. Moreover, our algorithms access disk data only via sequential scans, thus they take full advantage of modern disk features that make sequential disk accesses much faster than random accesses. We also present a scan-based algorithm for inverting the BWT that uses $\Th{n}$ bits of working space, and a lightweight {\em internal-memory} algorithm for computing the BWT which is the fastest in the literature when the available working space is $\os{n}$ bits. Finally, we prove {\em lower} bounds on the complexity of computing and inverting the BWT via sequential scans in terms of the classic product: internal-memory space $\times$ number of passes over the disk data.