This book provides students, researchers and professionals working in big data applications with solutions to core algorithmic problems, analyzed within RAM and external-memory models of computation. Pseudocode and running examples deal with various data types, and algorithmic tools for sampling, sorting, search, and data compression are included.
This book provides students, researchers and professionals working in big data applications with solutions to core algorithmic problems, analyzed within RAM and external-memory models of computation. Pseudocode and running examples deal with various data types, and algorithmic tools for sampling, sorting, search, and data compression are included.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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.
Inhaltsangabe
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.
