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portada Large Scale and big Data: Processing and Management
Type
Physical Book
Publisher
Author
Language
English
Pages
636
Format
Paperback
ISBN13
9781138033948
Edition No.
1

Large Scale and big Data: Processing and Management

Sherif Sakr (Editor) Mohamed Medhat Gaber (Editor) (Author) · Auerbach Publications · Paperback

Large Scale and big Data: Processing and Management - Sherif Sakr (Editor) Mohamed Medhat Gaber (Editor)

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Synopsis "Large Scale and big Data: Processing and Management"

Large Scale and Big Data: Processing and Management provides readers with a central source of reference on the data management techniques currently available for large-scale data processing. Presenting chapters written by leading researchers, academics, and practitioners, it addresses the fundamental challenges associated with Big Data processing tools and techniques across a range of computing environments. The book begins by discussing the basic concepts and tools of large-scale Big Data processing and cloud computing. It also provides an overview of different programming models and cloud-based deployment models. The book's second section examines the usage of advanced Big Data processing techniques in different domains, including semantic web, graph processing, and stream processing. The third section discusses advanced topics of Big Data processing such as consistency management, privacy, and security. Supplying a comprehensive summary from both the research and applied perspectives, the book covers recent research discoveries and applications, making it an ideal reference for a wide range of audiences, including researchers and academics working on databases, data mining, and web scale data processing. After reading this book, you will gain a fundamental understanding of how to use Big Data-processing tools and techniques effectively across application domains. Coverage includes cloud data management architectures, big data analytics visualization, data management, analytics for vast amounts of unstructured data, clustering, classification, link analysis of big data, scalable data mining, and machine learning techniques.

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