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派博传思国际中心 »期刊书目

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Titlebook: Big Data 2.0 Processing Systems; A Systems Overview Sherif Sakr Book 2020Latest edition The Editor(s) (if applicable) and The Author(s), un

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发表于 2025-3-21 20:02:08 | 显示全部楼层 |阅读模式
期刊全称Big Data 2.0 Processing Systems
期刊简称A Systems Overview
影响因子2023Sherif Sakr
视频videohttp://file.papertrans.cn/186/185580/185580.mp4
发行地址Provides readers the “big picture” and a comprehensive survey of the domain of big data processing systems and discusses various aspects of research and development.Describes an entire range of engine
图书封面Titlebook: Big Data 2.0 Processing Systems; A Systems Overview Sherif Sakr Book 2020Latest edition The Editor(s) (if applicable) and The Author(s), un
影响因子.This book provides readers the “big picture” and a comprehensive survey of the domain of big data processing systems. For the past decade, the Hadoop framework has dominated the world of big data processing, yet recently academia and industry have started to recognize its limitations in several application domains and thus, it is now gradually being replaced by a collection of engines that are dedicated to specific verticals (e.g. structured data, graph data, and streaming data). The book explores this new wave of systems, which it refers to as Big Data 2.0 processing systems...After Chapter 1 presents the general background of the big data phenomena, Chapter 2 provides an overview of various general-purpose big data processing systems that allow their users to develop various big data processing jobs for different application domains. In turn, Chapter 3 examines various systems that have been introduced to support the SQL flavor on top of the Hadoop infrastructure and provide competing and scalable performance in the processing of large-scale structured data. Chapter 4 discusses several systems that have been designed to tackle the problem of large-scale graph processing, while t
Pindex Book 2020Latest edition
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INCUR
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怒目而视
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Rüdiger Lorenz,Margareta Klinger,Mario Brocklar techniques on harnessing the power of data by achieving powerful analytical features. This chapter focuses on discussing several systems that have been developed to support computationally expensive machine learning and deep learning algorithms on top of big data processing frameworks.
Induction
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https://doi.org/10.1007/978-3-030-44187-6Database Management Systems; Hadoop; Stream Data Management; Graph Databases; Cloud Computing; Data Analy
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Dna262
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Tau and Intracellular Transport in Neurons,a storage, and computation systems. In practice, data generation and consumption is becoming a main part of people’s daily life especially with the pervasive availability and usage of Internet technology and applications. The Big Data term has been coined under the tremendous and explosive growth of
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Contend
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拉开这车床
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K. D. Lerch,D. Schäfer,J. Uelzenetween objects. Graphs have been widely used to represent datasets and encode problems across an already extensive range of application domains. The ever-increasing size of graph-structured data for these applications creates a critical need for scalable and even elastic systems that can process lar
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