Pearls of Algorithm Engineering
Cambridge University Press (Verlag)
978-1-009-12328-0 (ISBN)
There are many textbooks on algorithms focusing on big-O notation and basic design principles. This book offers a unique approach to taking the design and analyses to the level of predictable practical efficiency, discussing core and classic algorithmic problems that arise in the development of big data applications, and presenting elegant solutions of increasing sophistication and efficiency. Solutions are analyzed within the classic RAM model, and the more practically significant external-memory model that allows one to perform I/O-complexity evaluations. Chapters cover various data types, including integers, strings, trees, and graphs, algorithmic tools such as sampling, sorting, data compression, and searching in dictionaries and texts, and lastly, recent developments regarding compressed data structures. Algorithmic solutions are accompanied by detailed pseudocode and many running examples, thus enriching the toolboxes of students, researchers, and professionals interested in effective and efficient processing of big data.
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.
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.
| Erscheinungsdatum | 18.10.2021 |
|---|---|
| Zusatzinfo | Worked examples or Exercises |
| Verlagsort | Cambridge |
| Sprache | englisch |
| Gewicht | 746 g |
| Themenwelt | Informatik ► Theorie / Studium ► Algorithmen |
| ISBN-10 | 1-009-12328-9 / 1009123289 |
| ISBN-13 | 978-1-009-12328-0 / 9781009123280 |
| Zustand | Neuware |
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