Probabilistic Data Structures and Algorithms for Big Data Applications
(Sprache: Russisch)
Probabilistic data structures is a common name for data structures based mostly on different hashing techniques. Unlike regular (or deterministic) data structures, they always provide approximated answers but with reliable ways to estimate possible errors....
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Probabilistic data structures is a common name for data structures based mostly on different hashing techniques. Unlike regular (or deterministic) data structures, they always provide approximated answers but with reliable ways to estimate possible errors. Fortunately, the potential losses and errors are fully compensated for by extremely low memory requirements, constant query time, and scaling, the factors that become essential in Big Data applications.
Autoren-Porträt von Andrii Gakhov
Andrii Gakhov is a mathematician and software engineer holding a Ph.D. in mathematical modeling and numerical methods. He has been a teacher in the School of Computer Science at V. Karazin Kharkiv National University in Ukraine for a number of years and currently works as a software practitioner for ferret go GmbH, the leading community moderation, automation, and analytics company in Germany. His fields of interests include machine learning, stream mining, and data analysis.
Bibliographische Angaben
- Autor: Andrii Gakhov
- 2022, 228 Seiten, Maße: 15,5 x 22 cm, Kartoniert (TB), Russisch
- Verlag: tredition
- ISBN-10: 334754322X
- ISBN-13: 9783347543225
Sprache:
Russisch
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