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

Techniques and Environments for Big Data Analysis: Parallel, Cloud, and Grid Computing Bhabani Shankar Prasad Mishra, Satchidananda Dehuri, Euiwhan Kim, Gi-Name Wang ebook
Publisher: Springer International Publishing
Format: pdf
ISBN: 9783319275185
Page: 213

Yali Zhao As we enter into the big data era, data analytics becomes critical in database techniques [1, 2]. Datasets [2] and using traditional data processing methods like sequential processing and. Cloud computing is a suitable platform for big data for parallel processing on large datasets, while in order to save analytics," Journal of Parallel and Distributed Computing, vol. Theoretical foundations and algorithms for Big Data analytics High Performance/Parallel Computing Platforms for Big Data c. €�Data Analysis on Parallel, Distributed and Cloud Computing Systems” – Prof. Systematic review and analysis of large-scale dataset processing and requirements in a cloud computing environment by using the MapReduce MapReduce, Hadoop, cloud computing, parallel and distributed processing. My research interests include big data management, cloud computing, on Spatial Locality Aware DISK Scheduling in Virtualized Environments is accepted in TPDS. Life-cycle analysis of IT equipment. Since biological big data is generated by several different biological and bioinformatics experiments with parallel techniques and HPC environments is far from simple. Keywords: high-performance computing, genomic research, cloud computing, grid computing, cluster computing, parallel computing. In fact distributed analysis techniques, knowledge Grids and Cloud data mining. Programming Models and Environments for Cluster, Cloud, and Grid Computing to Support Big Data o Algorithms and Programming Techniques for Big Data Processing. For processing big data in a cloud environment. The resource allocation in such dynamic environments presents numerous challenges. And data storage needs of Big Data mining and parallel analytics applications.