Data compression is mandatory to manage massive datasets, indexing is fundamental to query them. However, their goals appear as counterposed: the former aims at minimizing data redundancies, whereas the latter augments the dataset with auxiliary information to speed up the query resolution. In this monograph we introduce solutions that overcome this dichotomy. We start by presenting the use of optimization techniques to improve the compression of classical data compression algorithms, then we move to the design of compressed data structures providing fast random access or efficient pattern matching queries on the compressed dataset. These theoretical studies are supported by experimental evidences of their impact in practical scenarios.

This book constitutes the proceedings of the 24th International Symposium on String Processing and Information Retrieval, SPIRE 2017, held in Palermo, Italy, in September 2017. The 26 papers presented in this volume were carefully reviewed and selected from 71 submissions. They focus on fundamental studies on string processing and information retrieval, as well as on computational biology.