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Multimedia big data computing for Io...

Kumar, Neeraj.

Multimedia big data computing for IoT applicationsconcepts, paradigms and solutions /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Multimedia big data computing for IoT applicationsedited by Sudeep Tanwar, Sudhanshu Tyagi, Neeraj Kumar.
Reminder of title:	concepts, paradigms and solutions /
other author:	Tanwar, Sudeep.
Published:	Singapore :Springer Singapore :2020.
Description:	xiv, 477 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Big data.
Online resource:	https://doi.org/10.1007/978-981-13-8759-3
ISBN:	9789811387593$q(electronic bk.)

Multimedia big data computing for IoT applicationsconcepts, paradigms and solutions /
Multimedia big data computing for IoT applicationsconcepts, paradigms and solutions /[electronic resource] :edited by Sudeep Tanwar, Sudhanshu Tyagi, Neeraj Kumar. - Singapore :Springer Singapore :2020. - xiv, 477 p. :ill., digital ;24 cm. - Intelligent systems reference library,v.1631868-4394 ;. - Intelligent systems reference library ;v.24..

Multimedia Big data computing for IoT -- Energy Conservation in MMBD Computing and IoT - A Challenge -- An Architecture for the Real-Time Data Stream Monitoring in IoT -- Deep learning for Multimedia data in IoT -- Random Forest based Sarcastic Tweet Classification using multiple feature Collection -- Peak Average Power Ratio reduction in FBMC using SLM & PTS techniques -- Intelligent Personality Analysis on Indicators in IoT-MMBD Enabled Environment -- Data Reduction in MMBD Computing -- Large Scale MMBD Management and Retrieval -- Data Reduction Technique for Capsule Endoscopy -- Multimedia Social Big Data: Mining -- Advertisement prediction in social media environment using big data framework.

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.

ISBN: 9789811387593$q(electronic bk.)

Standard No.: 10.1007/978-981-13-8759-3doiSubjects--Topical Terms:

609582
Big data.

LC Class. No.: QA76.9.B45

Dewey Class. No.: 005.7

Multimedia big data computing for IoT applicationsconcepts, paradigms and solutions /
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505 0 $a Multimedia Big data computing for IoT -- Energy Conservation in MMBD Computing and IoT - A Challenge -- An Architecture for the Real-Time Data Stream Monitoring in IoT -- Deep learning for Multimedia data in IoT -- Random Forest based Sarcastic Tweet Classification using multiple feature Collection -- Peak Average Power Ratio reduction in FBMC using SLM & PTS techniques -- Intelligent Personality Analysis on Indicators in IoT-MMBD Enabled Environment -- Data Reduction in MMBD Computing -- Large Scale MMBD Management and Retrieval -- Data Reduction Technique for Capsule Endoscopy -- Multimedia Social Big Data: Mining -- Advertisement prediction in social media environment using big data framework.
520 $a 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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