Approximate String Matching with Lempel-Ziv Compressed Indexes

Abstract

A compressed full-text self-index for a text T is a data structure requiring reduced space and able of searching for patterns P in T. Furthermore, the structure can reproduce any substring of T, thus it actually replaces T. Despite the explosion of interest on self-indexes in recent years, there has not been much progress on search functionalities beyond the basic exact search. In this paper we focus on indexed approximate string matching (ASM), which is of great interest, say, in computational biology applications. We present an ASM algorithm that works on top of a Lempel-Ziv self-index.We consider the so-called hybrid indexes, which are the best in practice for this problem. We show that a Lempel-Ziv index can be seen as an extension of the classical q-samples index. We give new insights on this type of index, which can be of independent interest, and then apply them to the Lempel-Ziv index. We show experimentally that our algorithm has a competitive performance and provides a useful space-time tradeoff compared to classical indexes.