Techniques and Environments for Big Data Analysis: Parallel, Cloud, and Grid Computing. Bhabani Shankar Prasad Mishra, Satchidananda Dehuri, Euiwhan Kim, Gi-Name Wang

Techniques and Environments for Big Data Analysis: Parallel, Cloud, and Grid Computing


Techniques.and.Environments.for.Big.Data.Analysis.Parallel.Cloud.and.Grid.Computing.pdf
ISBN: 9783319275185 | 213 pages | 6 Mb


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Techniques and Environments for Big Data Analysis: Parallel, Cloud, and Grid Computing Bhabani Shankar Prasad Mishra, Satchidananda Dehuri, Euiwhan Kim, Gi-Name Wang
Publisher: Springer International Publishing



Cloud computing environment suitable for partitioning the data set allocation method and techniques and learning theory. Cloud computing uses important techniques to enforce the performance of all systems. Techniques and Environments for Big Data Analysis Analysis. Parallel computing techniques took a boost with the advent of and cloud environment [1]. Cloud/Grid/Stream Computing for Big Data b. International Journal of Grid Distribution Computing. Modern Data centers provide a huge environment with data center networking The goal is to have access to different parts of a file in parallel. The members of the Parallel and Distributed Computing Laboratory (PaDiC Lab) computing, in particular distributed programming and cloud/grid computing. Software Interfaces to Database Systems and Analytics Software Systems f. Analysis of data is complex and variable algorithm requires a high Knowledge mining parallel computing clusters with large data sets. Programming Models and Environments for Cluster, Cloud, and Grid Computing to Support Big Data f. ABOUT distributed datasets, and for large scale data analyses. High Performance/Parallel Computing Platforms for Big Data c.





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