Editors:
- Presents the recent advances in the fields of Big Data Analysis, as well as the techniques and tools used to analyze it
- Emphasis is on theoretical advances of Big Data and it’s practices in parallel, cloud, and grid environment
- Interesting and novel collection useful for researchers and students
- Includes supplementary material: sn.pub/extras
Part of the book series: Studies in Big Data (SBD, volume 17)
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Table of contents (9 chapters)
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Front Matter
About this book
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.
Editors and Affiliations
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School of Computer, KIIT University, Bhubaneswar, India
Bhabani Shankar Prasad Mishra
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Fakir Mohan University, Department of Information and Communicat, Odisha, India
Satchidananda Dehuri
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Department of Systems Engineering, Ajou University, Suwon, Korea (Republic of)
Euiwhan Kim
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Department of Industrial Engineerin, Ajou University, Suwon, Korea (Republic of)
Gi-Name Wang
Bibliographic Information
Book Title: Techniques and Environments for Big Data Analysis
Book Subtitle: Parallel, Cloud, and Grid Computing
Editors: Bhabani Shankar Prasad Mishra, Satchidananda Dehuri, Euiwhan Kim, Gi-Name Wang
Series Title: Studies in Big Data
DOI: https://doi.org/10.1007/978-3-319-27520-8
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer International Publishing Switzerland 2016
Hardcover ISBN: 978-3-319-27518-5Published: 16 February 2016
Softcover ISBN: 978-3-319-80160-5Published: 30 March 2018
eBook ISBN: 978-3-319-27520-8Published: 05 February 2016
Series ISSN: 2197-6503
Series E-ISSN: 2197-6511
Edition Number: 1
Number of Pages: XI, 191
Number of Illustrations: 27 b/w illustrations, 76 illustrations in colour
Topics: Computational Intelligence, Data Mining and Knowledge Discovery, Artificial Intelligence