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The Burrows-Wheeler Transform:

Data Compression, Suffix Arrays, and Pattern Matching

By: Donald Adjeroh, Timothy Bell, Amar Mukherjee

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Hardcover | 25 July 2008

At a Glance

Hardcover
364 Pages

Dimensions(cm)
23.5 x 15.88 x 1.91

Hardcover

$169.00

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The Burrows-Wheeler Transform is a text transformation scheme that has found applications in different aspects of the data explosion problem, from data compression to index structures and search. The BWT belongs to a new class of compression algorithms, distinguished by its ability to perform compression by sorted contexts. More recently, the BWT has also found various applications in addition to text data compression, such as in lossless and lossy image compression, tree-source identification, bioinformatics, machine translation, shape matching, and test data compression.

This book will serve as a reference for seasoned professionals or researchers in the area, while providing a gentle introduction, making it accessible for senior undergraduate students or first year graduate students embarking upon research in compression, pattern matching, full text retrieval, compressed index structures, or other areas related to the BWT.

Key Features

Comprehensive resource for information related to different aspects of the Burrows-Wheeler Transform including:

Gentle introduction to the BWT

History of the development of the BWT

Detailed theoretical analysis of algorithmic issues and performance limits

Searching on BWT compressed data

Hardware architectures for the BWT

Explores non-traditional applications of the BWT in areas such as:

Bioinformatics

Joint source-channel coding

Modern information retrieval

Machine translation

Test data compression for systems-on-chip

Teaching materials ideal for classroom use on courses in:

Data Compression and Source Coding

Modern Information Retrieval

Information Science

Digital Libraries

Industry Reviews

From the reviews:

"The focus of the book, the Burrows-Wheeler transform (BWT), is an algorithm ... used in data compression and pattern matching. ... A major strength of the book is the clear writing style and the easy-to-follow flow. ... On one hand, a student or faculty will discover theoretical topics related to information theory and pattern recognition. On the other, computational scientists will be attracted to the many applications of the BWT transform. All in all, it is a worthwhile lecture." (Stefan Robila, ACM Computing Reviews, November, 2008)

"This book exposes the reader to many applications of the Burrows-Wheeler Transform ... . This book invites the reader to explore a new topic. ... I can recommend this text to a wide variety of readers. This book is well suited for a researcher ... who seeks to understand the Burrows-Wheeler Transform and its many applications to existing framework of algorithms. ... This would be a wonderful course textbook since its coverage is comprehensive ... . Many computer scientists can gain from reading this book." (Shoshana Neuburger, SIGACT News, Vol. 41 (1), 2010)

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You Can Find This Book In

Non-Fiction Computing & I.T.Computer Programming & Software Development Algorithms & Data Structures Graphical & Digital Media Applications 3D Graphics & Modelling Engineering & Technology Biochemical Engineering Databases Data Warehousing Computer Science Artificial Intelligence

Other Editions and Formats

Paperback

Published: 4th November 2010

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Introduction	p. 1
An example of a Burrows-Wheeler Transform	p. 3
Genesis of the Burrows-Wheeler Transform	p. 5
Transformation	p. 8
Permutation	p. 11
Recency	p. 12
Pattern matching	p. 13
Organization of this book	p. 14
Further reading	p. 16
How the Burrows-Wheeler Transform works	p. 19
The forward Burrows-Wheeler Transform	p. 19
The reverse Burrows-Wheeler Transform	p. 23
Special cases	p. 29
Further reading	p. 31
Coders for the Burrows-Wheeler Transform	p. 33
Entropy coding	p. 33
Run-length and arithmetic coder	p. 38
Move-to-front lists	p. 39
Frequency counting methods	p. 42
Inversion Frequencies (IF)	p. 43
Distance coding	p. 44
Wavelet trees	p. 45
Other permutations	p. 46
Block size	p. 47
Further reading	p. 48
Suffix trees and suffix arrays	p. 51
Suffix Trees	p. 51
Basic notations and definitions	p. 52
Construction of a suffix tree	p. 54
Ukkonen's suffix tree algorithm	p. 57
From implicit suffix tree to true suffix tree	p. 64
Farach's recursive construction	p. 66
Generalized suffix trees	p. 73
Implementation issues	p. 74
Suffix arrays	p. 75
Traditional string sorting	p. 76
Suffix arrays via suffix trees	p. 78
Manber-Myers suffix sorting algorithm	p. 78
Linear-time direct suffix sorting	p. 81
Space issues in suffix trees and suffix arrays	p. 85
Further reading	p. 88
Analysis of the Burrows-Wheeler Transform	p. 91
The BWT, suffix trees and suffix arrays	p. 93
Computational complexity	p. 95
BWT first stage - the transform	p. 95
BWT second stage - coding the transformed text	p. 95
BWT context clustering property	p. 97
Context trees	p. 97
Estimation using context trees	p. 100
BWT and context trees	p. 103
Analysis of BWT output	p. 104
Theoretical distribution of BWT output	p. 104
Empirical distribution of BWT output	p. 105
Analysis of BWT compression performance	p. 119
Definitions and notation	p. 120
Performance using recency ranking	p. 123
Performance without LGT	p. 129
Performance using piecewise constant parameters	p. 132
Performance on general sources via empirical entropy	p. 133
Relationship with other compression schemes	p. 135
Context-based schemes	p. 135
Symbol ranking schemes	p. 148
Further reading	p. 149
Variants of the Burrows-Wheeler Transform	p. 153
The sort transform	p. 154
Forward sort transform	p. 154
Inverse sort transform	p. 155
Performance of the sort transform	p. 159
Lexical permutation sorting	p. 163
Sorting permutations	p. 164
Lexical permutation sorting algorithm	p. 167
The extended BWT	p. 168
Sort order between strings	p. 168
Performing the extended BWT	p. 169
Inverting the transform	p. 170
Sort-based context similarity measurement	p. 173
Context similarity measurement and ranking	p. 173
The prefix list data structure	p. 175
Relationship with the Burrows-Wheeler Transform	p. 178
Performance of the prefix list	p. 180
Word-based compression	p. 180
General word-based compression	p. 181
Word-based Burrows-Wheeler Transform	p. 183
Further reading	p. 185
Exact and approximate pattern matching	p. 187
Exact pattern matching algorithms	p. 188
Brute force matching	p. 189
The Knuth-Morris-Pratt Algorithm	p. 190
The Boyer-Moore algorithm	p. 195
The Karp-Rabin algorithm	p. 197
The shift-and method	p. 199
Multiple pattern matching	p. 200
Pattern matching with don't-care characters	p. 204
Pattern matching using the Burrows-Wheeler Transform	p. 207
Boyer-Moore pattern matching using the BWT	p. 209
BWT-based exact pattern matching with binary search	p. 209
BWT-based exact pattern matching with suffix arrays	p. 214
Pattern matching using the FM-index	p. 215
Algorithm improvements with overwritten arrays	p. 220
Performance of BWT-based exact pattern matching	p. 221
Compression performance	p. 222
Search performance	p. 224
Array construction speeds	p. 231
Comparison with LZ-based compressed-domain pattern matching	p. 232
Approximate pattern matching	p. 233
Edit distance: dynamic programming formulation	p. 234
Edit graphs	p. 236
Local similarity	p. 237
The longest common subsequence problem	p. 239
String matching with k differences	p. 244
The k-mismatch problem using the BWT	p. 247
k-approximate matching using the BWT	p. 253
Hardware algorithms for pattern matching	p. 255
An equivalent hardware algorithm	p. 256
A brief review of other hardware algorithms	p. 258
Conclusion	p. 259
Further reading	p. 260
Other applications of the Burrows-Wheeler Transform	p. 265
Compressed suffix trees and compressed suffix arrays	p. 266
Compressed suffix trees	p. 267
Compressed suffix arrays	p. 270
Compressed full-text indexing	p. 275
Full-text indexing using CSTs and CSAs	p. 276
Searching on compressed suffix trees	p. 277
Searching on compressed suffix arrays	p. 278
Bioinformatics and computational biology	p. 278
DNA sequence compression	p. 279
Analysis of repetition structures	p. 280
Whole-genome comparisons	p. 281
Genome annotation	p. 282
Distance measure between sequences and phylogeny	p. 283
Test data compression	p. 284
Nature of test data	p. 285
BWT-based test data compression	p. 286
Image compression, computer vision and machine translation	p. 287
Image compression	p. 287
Shape matching	p. 292
Machine translation	p. 294
Joint source-channel coding	p. 296
General source coding via channel coding	p. 297
BWT-based joint source-channel coding	p. 298
Prediction and entropy estimation	p. 299
Further reading	p. 301
Conclusion	p. 305
Notation	p. 309
Ongoing work on the Burrows-Wheeler Transform	p. 313
BWT-related web sites	p. 313
Ph.D. theses relating to the Burrows-Wheeler Transform	p. 314
References	p. 317
Index	p. 341
Table of Contents provided by Ingram. All Rights Reserved.

The Burrows-Wheeler Transform:

Data Compression, Suffix Arrays, and Pattern Matching

At a Glance

Hardcover

Industry Reviews

Shipping

How to return your order

Defective items

You Can Find This Book In

Other Editions and Formats

Paperback

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