Toward Grasping the Dynamic Concept of Big Data
Автор: Luis Emilio Alvarez-Dionisi
Журнал: International Journal of Information Technology and Computer Science(IJITCS) @ijitcs
Статья в выпуске: 7 Vol. 8, 2016 года.
Бесплатный доступ
The idea of Big Data represents a growing challenge for companies such as Google, Yahoo, Bing, Amazon, eBay, YouTube, LinkedIn, Facebook, Instagram, and Twitter. However, the challenge goes beyond private companies, government agencies, and many other organizations. It is actually an alarm clock that is ringing everywhere: newspapers, magazines, books, research papers, online, offline, it is all over the world and people are worried about it. Its economic impact and consequences are of unproportioned dimensions. This research outlines the fundamental literature required to understand the concept of Big Data. Additionally, the present work provides a conclusion and recommendations for further research on Big Data. This study is part of an ongoing research that addresses the link between Economic Growth and Big Data.
Database, Data Science, Big Data, Software Engineering, Software Architecture, Business Analytics
Короткий адрес: https://sciup.org/15012504
IDR: 15012504
Список литературы Toward Grasping the Dynamic Concept of Big Data
- L. Einav and J. Levin, “The Data Revolution and Economic Analysis,” National Bureau of Economic Research, pp. 1–24, 2014.
- F. Diebold, “On the Origin(s) and Development of the Term Big Data,” Penn Institute for Economic Research, PIER Working Paper 12-037, Department of Economics, University of Pennsylvania, 2012.
- S. Kaisler, F. Armour, J. A. Espinosa, and W. Money, “Big Data: Issues and Challenges Moving Forward,” in 46th Hawaii International Conference on System Sciences, 2013, pp. 995–1004.
- J. Yan, “Big Data, Bigger Opportunities - Data.gov’s roles: Promote, lead, contribute, and collaborate in the era of big data,” President Management Council Inter-agency Rotation Program, Cohort 2, 2013.
- Big Data Working Group, Big Data Analytics for Security Intelligence, Cloud Security Alliance, pp. 1–22, 2013.
- R. Harris, “ICSU and the Challenges of Big Data in Science,” Research Trends: Special Issue on Big Data, pp. 11–12, 2012.
- B. Purcell, “Emergence of "Big Data" Technology and Analytics,” Journal of Technology Research, vol. 4, pp. 1–7, July 2013.
- BusinessDictionary. Internet: http://www.businessdictionary.com/definition/risk.html, April 07, 2015.
- F. Provost and T. Fawcett, “Data Science and its relationship to Big Data and Data-driven Decision Making,” Big Data, vol. 1, pp. 51–59, March 2013.
- T. Hey, S. Tansley, and K. Tolle, The Fourth Paradigm: Data-intensive Scientific Discovery, Microsoft Corporation, 2009.
- M. Herland, T. M. Khoshgoftaar, and R. Wald, “A review of data mining using big data in health informatics,” Journal of Big Data, vol. 1, pp. 1–35, June 2014.
- P. O’Donovan, K. Leahy, K. Bruton, and D. O’Sullivan, “Big data in manufacturing: a systematic mapping study,” Journal of Big Data, vol. 2, pp. 1–22, September 2015.
- A. Kumar, V. Grupcev, M. Berrada, J. C. Fogarty, Y. Tu, X. Zhu, S. A. Pandit, and Y. Xia, “DCMS: A data analytics and management system for molecular simulation,” Journal of Big Data, vol. 2, pp. 1–22, November 2014.
- A. Hatipoglu and S. I. Omurca, “A Turkish Wikipedia Text Summarization System for Mobile Devices,” I. J. Information Technology and Computer Science, vol. 1, pp. 1–10, 2016.
- M. Iqbal, M. M. Abid, M. Ahmad, and F. Khurshid, “Study on the Effectiveness of Spam Detection Technologies,” I. J. Information Technology and Computer Science, vol. 1, pp. 11–21, 2016.
