The Burrows Wheeler Transform

The Burrows-Wheeler Transform is one of the best lossless compression me- ods available. It is an intriguing — even puzzling — approach to squeezing redundancy out of data, it has an interesting history, and it has applications well beyond its original purpose as a compression method. It is a relatively late addition to the compression canon, and hence our motivation to write this book, looking at the method in detail, bringing together the threads that led to its discovery and development, and speculating on what future ideas might grow out of it. The book is aimed at a wide audience, ranging from those interested in learning a little more than the short descriptions of the BWT given in st- dard texts, through to those whose research is building on what we know about compression and pattern matching. The ?rst few chapters are a careful description suitable for readers with an elementary computer science ba- ground (and these chapters have been used in undergraduate courses), but later chapters collect a wide range of detailed developments, some of which are built on advanced concepts from a range of computer science topics (for example, some of the advanced material has been used in a graduate c- puter science course in string algorithms). Some of the later explanations require some mathematical sophistication, but most should be accessible to those with a broad background in computer science.

The Burrows-Wheeler Transform is one of the best lossless compression me- ods available. It is an intriguing — even puzzling — approach to squeezing redundancy out of data, it has an interesting history, and it has applications well beyond its original purpose as a compression method. It is a relatively late addition to the compression canon, and hence our motivation to write this book, looking at the method in detail, bringing together the threads that led to its discovery and development, and speculating on what future ideas might grow out of it. The book is aimed at a wide audience, ranging from those interested in learning a little more than the short descriptions of the BWT given in st- dard texts, through to those whose research is building on what we know about compression and pattern matching. The ?rst few chapters are a careful description suitable for readers with an elementary computer science ba- ground (and these chapters have been used in undergraduate courses), but later chapters collect a wide range of detailed developments, some of which are built on advanced concepts from a range of computer science topics (for example, some of the advanced material has been used in a graduate c- puter science course in string algorithms). Some of the later explanations require some mathematical sophistication, but most should be accessible to those with a broad background in computer science.

This volume contains papers selected for presentation during the 24th Interna tional Symposium on Mathematical Foundations of Computer Science held on September 6-10, 1999 in Szklarska Por^ba, Poland. The symposium, organized alternately in the Czech Republic, Slovakia, and Poland, focuses on theoretical aspects and mathematical foundations of computer science. The scientific program of the symposium consists of five invited talks given by Martin Dyer, Dexter Kozen, Giovanni Manzini, Sergio Rajsbaum, and Mads Tofte, and 37 accepted papers chosen out of 68 submissions. The volume contains all accepted contributed papers, and three invited papers. The contributed papers have been selected for presentation based on their scientific quality, novelty, and interest for the general audience of MFCS par ticipants. Each paper has been reviewed by at least three independent referees — PC members and/or sub-referees appointed by them. The papers were se lected for presentation during a fully electronic virtual meeting of the program committee on May 7, 1999. The virtual PC meeting was supported by software written by Artur Zgoda, Ph.D. student at the University of Wroclaw. The entire communication and access to quite a sensitive database at PC headquarters in Wroclaw was secured by cryptographic protocols based on technology of certificates.

Guided by standard bioscience workflows in high-throughput sequencing analysis, this book for graduate students, researchers, and professionals in bioinformatics and computer science offers a unified presentation of genome-scale algorithms. This new edition covers the use of minimizers and other advanced data structures in pangenomics approaches.

If you want to attract and retain users in the booming mobile services market, you need a quick-loading app that won’t churn through their data plans. The key is to compress multimedia and other data into smaller files, but finding the right method is tricky. This witty book helps you understand how data compression algorithms work—in theory and practice—so you can choose the best solution among all the available compression tools. With tables, diagrams, games, and as little math as possible, authors Colt McAnlis and Aleks Haecky neatly explain the fundamentals. Learn how compressed files are better, cheaper, and faster to distribute and consume, and how they’ll give you a competitive edge. Learn why compression has become crucial as data production continues to skyrocket Know your data, circumstances, and algorithm options when choosing compression tools Explore variable-length codes, statistical compression, arithmetic numerical coding, dictionary encodings, and context modeling Examine tradeoffs between file size and quality when choosing image compressors Learn ways to compress client- and server-generated data objects Meet the inventors and visionaries who created data compression algorithms

This book constitutes the refereed proceedings of the 14th International Symposium on String Processing and Information Retrieval, SPIRE 2007. Coverage in the 27 revised full papers includes dictionary algorithms, text searching, pattern matching, text compression, text mining, natural language processing, sequence driven protein structure prediction, XML, SGML, information retrieval from semi-structured data, text mining and generation of structured data from text.