Abstract
This paper describes the most important characteristics of cloud computing paradigm in order to be used as powerful arrows towards efficient approaches in teaching data analysis. Also High Performance Computing aspects are considered for solving large complexity data analysis problems. The usage of both Cloud Computing and High Performance Computing will assure not only an excellent framework for research but also a powerful and flexible environment for e-Learning.
The presentation is structured in three sections, being closed with the list of references. The first section considers the resources of cloud computing: SaaS (Software as a Service), IaaS (Infrastructure as a Service), PaaS (Platform as a Service - predefined integrated platform), and Managed Services. For every mentioned resource the most important features which are valuable for e-learning are outlined.
High Performance Computing for data analysis is described in the second section. Also, software tools useful to analyse collections of data having small, medium or large size are presented and compared in order to identify the best tool to be used in a virtual learning framework.
The third section describes both data manipulation techniques and results of experiments. Finally, concluding remarks are presented related to an online course on data analysis based on cloud computing paradigm.
Selected references
1. Abadi D.J., Data Management in the Cloud: Limitations and Opportunities, Bulletin of the IEEE Computer Society Technical Committee on Data Engineering, 2009.
2. Grossman R., Gu Y., Data Mining Using High Performance Data Clouds: Experimental Studies Using Sector and Sphere, http://sector.sourceforge.net/pub/grossman-gu-ncdm-tr-08-04.pdf
3.G Albeanu, L. Serbanescu, Fl. Popentiu-Vladicescu. On teaching data analysis and optimisation using software tools. In Grigore Albanu, Dorin Mircea Popovici , Marin Vlada (eds.), Proceedings of the 2nd International Conference on Virtual Learning, Constata, 26-28 October, Bucharest, ISBN: 973-737-218-2 978-973-737-380-9, Romania. , I, pp. 255-260. 2007. |
