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Algorithms and Data Structures for Massive Datasets

Autor Dzejla Medjedovic, Emin Tahirovic Ilustrat de Ines Dedovic
en Limba Engleză Paperback – 5 iul 2022
Data structures and algorithms that are great for traditional software may quickly slow or fail altogether when applied to huge datasets. Algorithmsand Data Structures for Massive Datasets introduces a toolbox of new techniques that are perfect for handling modern big data applications. You'll discover methods for reducing and sketching data so it fits in small memory without losing accuracy, and unlock the algorithms and data structures that form the backbone of a big data system. Filled with fun illustrations and examples from real-world businesses, you'll learn how each of these complex techniques can be practically applied to maximize the accuracy and through put of big data processing and analytics.
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Specificații

ISBN-13: 9781617298035
ISBN-10: 1617298034
Pagini: 304
Dimensiuni: 191 x 237 x 20 mm
Greutate: 0.48 kg
Editura: Manning Publications

Notă biografică

Dzejla Medjedovic earned her PhD in the Applied Algorithms Lab of the computer science department at Stony Brook University, NY in 2014. She has worked on a number of projects in algorithms for massive data, taught algorithms at various levels and also spent some time at Microsoft.

Emin Tahirovic earned his doctorate in biostatistics from UPenn in 2016, and his master's degree in theoretical computer science from Goethe University in Frankfurt in 2008. He has worked for DBahn AG as an IT consultant and he regularly consults on projects for pharma and tech companies.

Ines Dedovic earned her PhD at the Institute for Imaging and Computer Vision of the Department of Electrical Engineering at RWTH Aachen University, Germany. She has worked as a researcher at the Research Center Jülich and is currently employed as a software developer for camera systems at Jonas & Redmann, an automation company.

Cuprins

table of contents
READ IN LIVEBOOK1INTRODUCTION
PART 1: HASH-BASED SKETCHES
READ IN LIVEBOOK2REVIEW OF HASH HABLES AND MODERN HASHING
READ IN LIVEBOOK3APPROXIMATE MEMBERSHIP: BLOOM FILTER AND QUOTIENT FILTER
READ IN LIVEBOOK4FREQUENCY ESTIMATION AND COUNT-MIN SKETCH
READ IN LIVEBOOK5CARDINALITY ESTIMATION AND HYPERLOGLOG
PART 2: REAL-TIME ANALYTICS
READ IN LIVEBOOK6STREAMING DATA: BRINGING EVERYTHING TOGETHER
READ IN LIVEBOOK7SAMPLING FROM DATA STREAMS
READ IN LIVEBOOK8APPROXIMATE QUANTILES ON DATA STREAMS
PART 3: DATA STRUCTURES FOR DATABASES AND EXTERNAL-MEMORY ALGORITHMS
READ IN LIVEBOOK9INTRODUCING THE EXTERNAL-MEMORY MODEL
READ IN LIVEBOOK10DATA STRUCTURES FOR DATABASES: B-TREES, B-TREES, LSM-TREES
READ IN LIVEBOOK11EXTERNAL-MEMORY SORTING