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Titlebook: Distributed Computing in Big Data Analytics; Concepts, Technologi Sourav Mazumder,Robin Singh Bhadoria,Ganesh Chandr Book 2017 Springer Int

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发表于 2025-3-21 19:43:27 | 显示全部楼层 |阅读模式
书目名称Distributed Computing in Big Data Analytics
副标题Concepts, Technologi
编辑Sourav Mazumder,Robin Singh Bhadoria,Ganesh Chandr
视频video
概述Addresses key concepts and patterns of distributed computing to provide practitioners with insight while designing big data analytics use cases.Details how different big data technologies leverage tho
丛书名称Scalable Computing and Communications
图书封面Titlebook: Distributed Computing in Big Data Analytics; Concepts, Technologi Sourav Mazumder,Robin Singh Bhadoria,Ganesh Chandr Book 2017 Springer Int
描述.Big data technologies are used to achieve any type of analytics in a fast and predictable way, thus enabling better human and machine level decision making. Principles of distributed computing are the keys to big data technologies and analytics. The mechanisms related to data storage, data access, data transfer, visualization and predictive modeling using distributed processing in multiple low cost machines are the key considerations that make big data analytics possible within stipulated cost and time practical for consumption by human and machines. However, the current literature available in big data analytics needs a holistic perspective to highlight the relation between big data analytics and distributed processing for ease of understanding and practitioner use..This book fills the literature gap by addressing key aspects of distributed processing in big data analytics. The chapters tackle the essential concepts and patterns of distributed computing widely used in big data analytics. This book discusses also covers the main technologies which support distributed processing. Finally, this book provides insight into applications of big data analytics, highlighting how principle
出版日期Book 2017
关键词Distributed computing; Cognitive analytics; Internet of things; Social media analytics; Scientific data
版次1
doihttps://doi.org/10.1007/978-3-319-59834-5
isbn_softcover978-3-319-86713-7
isbn_ebook978-3-319-59834-5Series ISSN 2520-8632 Series E-ISSN 2364-9496
issn_series 2520-8632
copyrightSpringer International Publishing AG, part of Springer Nature 2017
The information of publication is updating

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Why Elizabeth Never Left EnglandThose concepts are used in various Big Data Technologies of present time, which are in turn the key building blocks of the Big Data Analytics used today in various businesses and industries. So it is essential for practitioners of Big Data Analytics to understand these fundamental concepts related t
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Time Study and Methods Improvementing models, system architectures, and platforms for the development of distributed systems. Several Big Data frameworks of today implement these concepts of distributed system for data synchronization, message exchange, real time data processing and transaction control in architectures for Big Data
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Wage Payment Plans and Incentivese, nonlinear and non-stationary in their dynamics. Solutions to these problems often necessitate the use of complex mathematical modeling, simulation and analysis which are traditionally achieved by the use of expensive high performance computing, more commonly known as Super-Computers. In this chap
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https://doi.org/10.1007/978-3-030-00163-6 industries such as travel and transport. However, in last year or two this phrase has gone viral across various business industries with experts predicting that the cognitive systems will play a very vital role in next generation of computing in general and especially in Big Data Analytics. In this
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Alberto M. Goldwaser,Eric L. Goldwaserctively use social media today. This abrupt societal transition has led to a dramatic increase in the extent to which a person’s social footprint is documented online in the public domain. While for some social media sites, like Snapchat and Facebook, privacy is a key feature, on sites like Twitter,
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https://doi.org/10.1007/978-3-319-59834-5Distributed computing; Cognitive analytics; Internet of things; Social media analytics; Scientific data
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