Big Data Annotated Bibliography

Big Data Annotated Bibliography

Bayuk, J. (2010). CyberForensics: Understanding Information Security Investigations. Springer.

Bayuk (2010) in his book presents the computer forensic techniques used to curb cyber insecurity. The author analyzes the security investigations used in the development of counter intelligence system against cybercrime. To support the intelligence systems, the author uses different case studies on cyber crime investigations to develop a computer forensic technology that can be used to eliminate cyber crimes in an organization. The book is important for the study because its provides cyber-forensic concepts applicable in conducting cyber security investigations. It also provides expertise of the pioneers of cyber investigations and explores a number of examples from malware and accounting cyber-forensics.

Author Note: Bayuk is an information technology and security management diligent expert in all aspects of cyber security. She has published four books and two compilations on it governance and Infosec.

Tehan, R. (2008). Data Security Breaches: Context and Incident Summaries. Nova Publishers.

Tehan (2008) analyzes different types of security breaches reported across the U.S.A. And the world at large. Tehan (2008) uses different examples such as breaches into the social security, bank accounts and credit cards to show the magnitude of the security breaches in the current global environment. The book is important for the study as it provides insights to the different security breaches and laws developed to oversee security breaches in the U.S. The findings from the book can be used to create meaning of the Big Data to organizations.

Author Note: Tehan is an information research specialist with the Congressional Research Service.

Chawla, N.V., & Davis, D.A. (2013). Bringing Big Data to Personalized Healthcare: A

Patient- Centered Framework. Journal of General Internal Medicine, 28(3), 660

665. doi:10.1007/s11606-013-2455-8

The Big Data is beneficial to the health care organizations as it ensures proper keeping of patient's information, thereby ensuring cost sustainability and enhanced. The Big Data promotes electronic storage of patient's data, and monitoring of the patient's outcomes hence, promotion of the patient-centered provision of healthcare. According to the authors, the data-driven and network driven frames of thinking promotes the provision of personalized care. The approaches of the Big Data facilitate the creation of the personalized disease risk profile and disease management plan alongside a wellness plan for a patient. The authors strengthen the applicability of the Big Data in the provision of the patient-centered care that, it provides the health care providers with a collaborative filtering technology that facilitates the provision of accurate care to the patient. The authors used different techniques of enhancing personalized care like the APACHE III. The method enhances the comparative analysis of the Big Data in promoting the provision of personalized care to patients. The study is significant as it shows the diversified nature of the uses of the Big Data across different organizations. The approaches used by the authors provide insights to the practicability of the technology in facilitating the provision of patient-centered care.

Author Notes: Chalwa and Davis work with the Department of Computer Science and Engineering, the University of Notre Dame, USA. Little information is provided on their publications on science related issues.

Zaslavsky, a., Perera, C., & Georgakopoulos, D. (2013). Sensing as a Service and Big Data.

arXiv:1301.0159 [cs]. Retrieved from http://arxiv.org/abs/1301.0159

Zaslavsky, Perera, and Georgakopoulos (2013) illuminate light into the benefits of using the Internet of Things (IoT) one of the technologies that facilitate the success of the Big Data. IoT comprises of different devices that promote sensing, communication, compute, and actuate incoming data into the system. The authors support the strengths associated with the IoT by providing some of its features such as regular data collection, sense-compute-actuate (SCA) loops, and intermittent sensing. The authors acknowledge that, the inefficiencies associated with the older techniques of data collection such as capture, storage analysis, search, and virtualization are solvable with the use of the sensing technology (IoT).

They developed strategies such as providing scenarios of different businesses that have adopted the sensing technology of the Big Data to show its benefits. Their most contribution to the study is their approach of explaining the benefits, features, and challenges associated with the sensing technology of the Big Data. While showing the benefits and technology being the sensing technology and model, the also provide insights into its benefits hence, the strength of the article.

Author Notes: The authors of this article appear to have vast knowledge and expertise in sensor technology associated with the Big Data. 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.