Compression by Contracting Straight-Line Programs

Abstract

In grammar-based compression a string is represented by a context-free grammar, also called a straight-line program (SLP), that generates only that string. We refine a recent balancing result stating that one can transform an SLP of size g in linear time into an equivalent SLP of size 𝒪(g) so that the height of the unique derivation tree is 𝒪(log N) where N is the length of the represented string (FOCS 2019). We introduce a new class of balanced SLPs, called contracting SLPs, where for every rule A → β₁ … β_k the string length of every variable β_i on the right-hand side is smaller by a constant factor than the string length of A. In particular, the derivation tree of a contracting SLP has the property that every subtree has logarithmic height in its leaf size. We show that a given SLP of size g can be transformed in linear time into an equivalent contracting SLP of size 𝒪(g) with rules of constant length. This result is complemented by a lower bound, proving that converting SLPs into so called α-balanced SLPs or AVL-grammars can incur an increase by a factor of Ω(log N). We present an application to the navigation problem in compressed unranked trees, represented by forest straight-line programs (FSLPs). A linear space data structure by Reh and Sieber (2020) supports navigation steps such as going to the parent, left/right sibling, or to the first/last child in constant time. We extend their solution by the operation of moving to the i-th child in time 𝒪(log d) where d is the degree of the current node. Contracting SLPs are also applied to the finger search problem over SLP-compressed strings where one wants to access positions near to a pre-specified finger position, ideally in 𝒪(log d) time where d is the distance between the accessed position and the finger. We give a linear space solution for the dynamic variant where one can set the finger in 𝒪(log N) time, and then access symbols or move the finger in time 𝒪(log d + log^(t) N) for any constant t where log^(t) N is the t-fold logarithm of N. This improves a previous solution by Bille, Christiansen, Cording, and Gortz (2018) with access/move time 𝒪(log d + log log N).