Perera has vast experience in computing and technology as he is a member of the Commonwealth Scientific and Industrial Research Organization alongside publishing numerous journals. Georgakopoulos is the Director of Information Engineering Laboratory. He has published over 100 journals on issues related to science and technology (Big Data).
Cohen, J., Dolan, B., Dunlap, M., Hellerstein, J.M., & Welton, C. (2009). MAD Skills: New
Analysis Practices for Big Data. Proc. VLDB Endow., 2(2), 1481 -- 1492.
The article focuses on the Magnetic, Agile, Deep (MAD) that proves more effective in data analysis than the traditional Enterprise Data Warehouse and Business Intelligence. The strength of their article lies on the fact that, it presents the philosophy behind the design of the technology, techniques, and positive experiences associated with the use of the MAD technology of data analysis. The authors provide more insight into the benefits of the technology in contributing to the realization of the objectives of the Big Data that, the system supports agility of data analysis and provides sophisticated data statistical techniques for data analysis. The most contribution to the study is the approaches presented used by the technology as presented by the authors. The study also is significant as it provides directions for future directions of the MAD technology and recommendations applicable in improving the future outcomes of data storage and analysis.
Author Notes: The authors of this article have a variety of professional backgrounds. Cohen has vast experience in programming; Dunlap works with the Evergreen Technologies, and Hellerstein works with the Berkeley University. Welton works with the Greenplum as Senior Director of Programming Services.
Herodotou, H., Lim, H., Luo, G., Borisov, N., Dong, L., Cetin, F.B., & Babu, S. (2011). Starfish?: A Self-tuning System for Big Data Analytics. CIDR, 11, 261 -- 272. doi:10.1.1.222.6934
The authors of this article introduce the Starfish, one of the self-tunings systems that ensure the effectiveness and efficiency of the big data. The starfish provides automation of activities without the need for manual tuning of knobs in the Hadoop. The starfish combines the principles of agility, depth, and magnetism that constitute the previously analyzed MAD to ensure effectiveness of the big data in information storage and analysis. The authors strengthened their...
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