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…mehr
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.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Dzejla Medjedovic earned her PhD in the Applied Algorithms Lab of the computer science department atStony 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 RWTHAachen University, Germany. She has worked as a researcher at the ResearchCenter Jülich and is currently employed as a software developer for camera systems at Jonas & Redmann, an automation company.
Inhaltsangabe
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
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
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