Algorithms for Compressed Inputs
Nathan Brunelle, Gabriel Robins, Abhi A. Shelat · 2013
We study compression-aware algorithms, i.e. algorithms that can exploit regularity in their input data by directly operating on compressed data. While popular with string algorithms, we consider this idea for algorithms operating on numeric sequences and graphs that have been compressed using a variety of schemes including LZ77, grammar-based compression, a graph interpretation of Re-Pair, and a method presented by Boldi and Vigna in The Web Graph Framework. In all cases, we discover algorithms outperforming a trivial approach: to decompress the input and run a standard algorithm. We aim to develop an algorithmic toolkit for basic tasks to operate on a variety of compression inputs.