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

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 A...

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Detalles Bibliográficos
Autores principales: Mishra, Bhabani, Dehuri, Satchidananda, Kim, Euiwhan, Wang, Gi-Name
Lenguaje:eng
Publicado: Springer 2016
Materias:
Engineering
Acceso en línea:https://dx.doi.org/10.1007/978-3-319-27520-8
http://cds.cern.ch/record/2137854
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author Mishra, Bhabani
Dehuri, Satchidananda
Kim, Euiwhan
Wang, Gi-Name
author_facet Mishra, Bhabani
Dehuri, Satchidananda
Kim, Euiwhan
Wang, Gi-Name
author_sort Mishra, Bhabani
collection CERN
description 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.
id cern-2137854
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2016
publisher Springer
record_format invenio
spelling cern-21378542021-04-21T19:46:03Zdoi:10.1007/978-3-319-27520-8http://cds.cern.ch/record/2137854engMishra, BhabaniDehuri, SatchidanandaKim, EuiwhanWang, Gi-NameTechniques and environments for big data analysis: parallel, cloud, and grid computingEngineeringThis 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.Springeroai:cds.cern.ch:21378542016
spellingShingle Engineering
Mishra, Bhabani
Dehuri, Satchidananda
Kim, Euiwhan
Wang, Gi-Name
Techniques and environments for big data analysis: parallel, cloud, and grid computing
title Techniques and environments for big data analysis: parallel, cloud, and grid computing
title_full Techniques and environments for big data analysis: parallel, cloud, and grid computing
title_fullStr Techniques and environments for big data analysis: parallel, cloud, and grid computing
title_full_unstemmed Techniques and environments for big data analysis: parallel, cloud, and grid computing
title_short Techniques and environments for big data analysis: parallel, cloud, and grid computing
title_sort techniques and environments for big data analysis: parallel, cloud, and grid computing
topic Engineering
url https://dx.doi.org/10.1007/978-3-319-27520-8
http://cds.cern.ch/record/2137854
work_keys_str_mv AT mishrabhabani techniquesandenvironmentsforbigdataanalysisparallelcloudandgridcomputing
AT dehurisatchidananda techniquesandenvironmentsforbigdataanalysisparallelcloudandgridcomputing
AT kimeuiwhan techniquesandenvironmentsforbigdataanalysisparallelcloudandgridcomputing
AT wangginame techniquesandenvironmentsforbigdataanalysisparallelcloudandgridcomputing

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