Table of Contents

1. Prologue; 2. A warm-up!; 3. Random sampling; 4. List ranking; 5. Sorting atomic items; 6. Set intersection; 7. Sorting strings; 8. The dictionary problem; 9. Searching strings by prefix; 10. Searching strings by substring; 11. Integer coding; 12. Statistical coding; 13. Dictionary-based compressors; 14. The burrows-wheeler transform; 15. Compressed data structures; 16. Conclusion.

Promotional Information

This book covers algorithmic problems in big data applications, presenting solutions over hierarchical-memory systems along with pseudocode.

About the Author

Paolo Ferragina is Professor of Algorithms at the University of Pisa, with a post-doc at the Max-Planck Institute for Informatics. He served his university as Vice Rector for ICT (2019–22) and for Applied Research and Innovation (2010–16) and as the Director of the PhD program in Computer Science (2018–20). His research focuses on designing algorithms and data structures for compressing, mining, and retrieving information from big data. The joint recipient of the prestigious 2022 ACM Paris Kanellakis Theory and Practice Award and numerous international awards, Ferragina has previously collaborated with AT&T, Bloomberg, Google, ST microelectronics, Tiscali, and Yahoo. His research has produced several patents and has featured in over 170 papers published in renowned conferences and journals. He has spent research periods at the Max Planck Institute for Informatics, the University of North Texas, the Courant Institute at New York University, the MGH/Harvard Medical School, AT&T, Google, IBM Research, and Yahoo.

Reviews

'When I joined Google in 2000, algorithmic problems came up every day. Even strong engineers didn't have all the background they needed to design efficient algorithms. Paolo Ferragina's well-written and concise book helps fill that void. A strong software engineer who masters this material will be an asset.' Martin Farach-Colton, Rutgers University

'There are plenty of books on Algorithm Design, but few about Algorithm Engineering. This is one of those rare books on algorithms that pays the necessary attention to the more practical aspects of the process, which become crucial when actual performance matters, and which render some theoretically appealing algorithms useless in real life. The author is an authority on this challenging path between theory and practice of algorithms, which aims at both conceptually nontrivial and practically relevant solutions. I hope the readers will find the reading as pleasant and inspiring as I did.' Gonzalo Navarro, University of Chile

'Ferragina combines his skills as a coding engineer, an algorithmic mathematician, and a pedagogic innovator to engineer a string of pearls made up of beautiful algorithms. In this, beauty dovetails with computational efficiency. His data structures of Stringomics hold the promise for a better understanding of population of genomes and the history of humanity. It belongs in the library of anyone interested in the beauty of code and the code of beauty.' Bud Mishra, Courant Institute, New York University

'There are many textbooks on algorithms focusing on big-O notation and general design principles. This book offers a completely unique aspect of taking the design and analyses to the level of predictable practical efficiency. No sacrifices in generality are made, but rather a convenient formalism is developed around external memory efficiency and parallelism provided by modern computers. The benefits of randomization are elegantly used for obtaining simple algorithms, whose insightful analyses provide the reader with useful tools to be applied to other settings. This book will be invaluable in broadening the computer science curriculum with a course on algorithm engineering.' Veli Makinen, University of Helsinki