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Techniques and environments for big ...

Mishra, Bhabani Shankar Prasad.

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Techniques and environments for big data analysis = parallel, cloud, and grid computing /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Techniques and environments for big data analysis/ edited by Bhabani Shankar Prasad Mishra ... [et al.].
Reminder of title:	parallel, cloud, and grid computing /
other author:	Mishra, Bhabani Shankar Prasad.
Published:	Cham :Springer International Publishing : : 2016.,
Description:	xi, 191 p. :ill., digital ;24 cm.
[NT 15003449]:	Introduction to Big Data Analysis -- Parallel Environments -- A Deep Dive into the Hadoop World to Explore its Various Performances -- Natural Language Processing and Machine Learning for Big Data -- Big Data and Cyber Foraging: Future Scope and Challenges -- Parallel GA in Big Data Analysis -- Evolutionary Algorithm Based Techniques to Handle Big Data -- Statistical and Evolutionary Feature Selection Techniques Parallelized using MapReduce Programming Model -- A Data Aware Scheme for Scheduling Big-Data Applications on SAVANNA Hadoop -- The Role of Grid Technologies: A Next Level Combat with Big Data.
Contained By:	Springer eBooks
Subject:	Big data. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-27520-8
ISBN:	9783319275208$q(electronic bk.)

Techniques and environments for big data analysis = parallel, cloud, and grid computing /
Techniques and environments for big data analysisparallel, cloud, and grid computing /[electronic resource] :edited by Bhabani Shankar Prasad Mishra ... [et al.]. - Cham :Springer International Publishing :2016. - xi, 191 p. :ill., digital ;24 cm. - Studies in big data,v.172197-6503 ;. - Studies in big data ;v.1..

Introduction to Big Data Analysis -- Parallel Environments -- A Deep Dive into the Hadoop World to Explore its Various Performances -- Natural Language Processing and Machine Learning for Big Data -- Big Data and Cyber Foraging: Future Scope and Challenges -- Parallel GA in Big Data Analysis -- Evolutionary Algorithm Based Techniques to Handle Big Data -- Statistical and Evolutionary Feature Selection Techniques Parallelized using MapReduce Programming Model -- A Data Aware Scheme for Scheduling Big-Data Applications on SAVANNA Hadoop -- The Role of Grid Technologies: A Next Level Combat with Big Data.

This volume is aiming at a wide range of readers and researchers in the area of Big Data by presenting the recent advances in the fields of Big Data Analysis, as well as the techniques and tools used to analyze it. The book includes 10 distinct chapters providing a concise introduction to Big Data Analysis and recent Techniques and Environments for Big Data Analysis. It gives insight into how the expensive fitness evaluation of evolutionary learning can play a vital role in big data analysis by adopting Parallel, Grid, and Cloud computing environments.

ISBN: 9783319275208$q(electronic bk.)

Standard No.: 10.1007/978-3-319-27520-8doiSubjects--Topical Terms:

2045508
Big data.

LC Class. No.: QA76.9.B45

Dewey Class. No.: 005.7

Techniques and environments for big data analysis = parallel, cloud, and grid computing /
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245 0 0 $a Techniques and environments for big data analysis $h [electronic resource] : $b parallel, cloud, and grid computing / $c edited by Bhabani Shankar Prasad Mishra ... [et al.].
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505 0 $a Introduction to Big Data Analysis -- Parallel Environments -- A Deep Dive into the Hadoop World to Explore its Various Performances -- Natural Language Processing and Machine Learning for Big Data -- Big Data and Cyber Foraging: Future Scope and Challenges -- Parallel GA in Big Data Analysis -- Evolutionary Algorithm Based Techniques to Handle Big Data -- Statistical and Evolutionary Feature Selection Techniques Parallelized using MapReduce Programming Model -- A Data Aware Scheme for Scheduling Big-Data Applications on SAVANNA Hadoop -- The Role of Grid Technologies: A Next Level Combat with Big Data.
520 $a This volume is aiming at a wide range of readers and researchers in the area of Big Data by presenting the recent advances in the fields of Big Data Analysis, as well as the techniques and tools used to analyze it. The book includes 10 distinct chapters providing a concise introduction to Big Data Analysis and recent Techniques and Environments for Big Data Analysis. It gives insight into how the expensive fitness evaluation of evolutionary learning can play a vital role in big data analysis by adopting Parallel, Grid, and Cloud computing environments.
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