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Multimedia Big Data Computing for IoT Applications: Concepts, Paradigms and Solutions

By: Tanwar, Sudeep  [Editor] | Tyagi, Sudhanshu  [Editor] | Kumar, Neeraj  [Editor]
Series: Intelligent Systems Reference Library 163Publisher: Singapore: Sprinder, 2020Description: 477 p. DigitalSubject(s): BIG DATA | IOT | COMPUTING | ANALYTICS | MANAGEMENT | ENVIRONMENTS | IIC20DDC classification: Online resources: ►►DOWNLOAD EBOOK / DESCARGAR LIBRO DIGITAL◄◄
Contents:
REFERENCIA FORMATO APA: Tanwar, S., Tyagi, S. & Kumar, N. (2020). Multimedia Big Data Computing for IoT Applications : Concepts, Paradigms and Solutions. Singapore: Springer Singapore Pte. Limited.
Summary: This book considers all aspects of managing the complexity of Multimedia Big Data Computing (MMBD) for IoT applications and develops a comprehensive taxonomy. It also discusses a process model that addresses a number of research challenges associated with MMBD, such as scalability, accessibility, reliability, heterogeneity, and Quality of Service (QoS) requirements, presenting case studies to demonstrate its application. Further, the book examines the layered architecture of MMBD computing and compares the life cycle of both big data and MMBD. Written by leading experts, it also includes numerous solved examples, technical descriptions, scenarios, procedures, and algorithms.
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REFERENCIA FORMATO APA: Tanwar, S., Tyagi, S. & Kumar, N. (2020). Multimedia Big Data Computing for IoT Applications : Concepts, Paradigms and Solutions. Singapore: Springer Singapore Pte. Limited.

This book considers all aspects of managing the complexity of Multimedia Big Data Computing (MMBD) for IoT applications and develops a comprehensive taxonomy. It also discusses a process model that addresses a number of research challenges associated with MMBD, such as scalability, accessibility, reliability, heterogeneity, and Quality of Service (QoS) requirements, presenting case studies to demonstrate its application. Further, the book examines the layered architecture of MMBD computing and compares the life cycle of both big data and MMBD. Written by leading experts, it also includes numerous solved examples, technical descriptions, scenarios, procedures, and algorithms.

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