Big Data Annotated Bibliography essay

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Zaslavsky is the leader of the Semantic Data Management Science Area (SMSA). He has published more than 300 publications on science and technology. 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 argument by providing ways that the starfish can eliminate challenges associated with the traditional MAD like slowed data processing and file-based processing. The article proves applicable for the study because, starfish focuses enhancing optimization of organizational activities using different principles such as the principle of thumb for parameter tuning and dynamic instrumentation that influence the effectiveness of data storage and analysis.

Author Notes: The authors work at the Duke University, Department of Computer Science. They are also senior researchers at Data Management, Exploration and Mining (DMX). Long works as an associate professor at the Baylor University. Borisov has published over 150 studies similar to Luo, Cetin and Babu's.

Grove, R. (2009). Web Based Application Development. Jones & Bartlett Learning.

Grove (2009) provides a detailed analysis of web based theories, concepts, tools, languages, and practices that can be used in the development of systems aiming at eliminating security threats. The author also provides fundamental applications that can be used to put Big Data ideas and other programming related interventions into practice. The book is important for the study as it provides theoretical frameworks applicable in the development of reliable architecture of for handling cyber related insecurities.

Author Notes: Grove teaches SoftWare Engineering in the Department of Computer Science, Madison University.

Peitz, M., & Waldfogel, J. (2012). The Oxford handbook of the digital economy. New York:

Peitz and Waldfogel (2012)'s book provides an up-to-date analysis of the main concerns associated with digital technology having an impact on the global security. The book is important for the study because it provides a pathway for conducting research on issues related to data storage affecting the global environment. It focuses on the key problems facing large industries, businesses, and issues associated with data storage and digital industry.

Author Notes: Peitz works with the University of Manheim as a Professor of Economics. He has published numerous journals and books such as Regulation and Entry into Telecommunications Markets. Waldfogel works with the Carlson School of Management, Minnesota as a professor of Applied Economics. He has published over 50 articles and…[continue]

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