Database Reference
In-Depth Information
￿
Energy Management : the energy consumption of mainframe computing systems
has drawn much attention from both economy and environment perspectives.
With the increase of data volume and analytical demands, the processing, storage,
and transmission of big data will inevitably consume more and more electric
energy. Therefore, system-level power consumption control and management
mechanisms shall be established for big data while expandability and accessi-
bility are both ensured.
￿
Expendability and Scalability : the analytical system of big data must support
present and future datasets. The analytical algorithm must be able to process
increasingly expanding and more complex datasets.
￿
Cooperation : analysis of big data is an interdisciplinary research, which requires
experts in different fields cooperate to harvest the potential of big data. A com-
prehensive big data network architecture must be established to help scientists
and engineers in various fields access different kinds of data and fully utilize
their expertise, so as to cooperate to complete the analytical objectives.
References
1. John Gantz and David Reinsel. Extracting value from chaos. IDC iView , pages 1-12, 2011.
2. Kenneth Cukier. Data, data everywhere: A special report on managing information . Economist
Newspaper, 2010.
3. Drowning in numbers - digital data will flood the planet- and help us understand it better. http://
www.economist.com/blogs/dailychart/2011/11/bigdata-0 , 2011.
4. Steve Lohr. The age of big data. New York Times , 11, 2012.
5. Noguchi Yuki. Following digital breadcrumbs to big data gold. http://www.npr.org/2011/11/
29/142521910/thedigitalbreadcrumbs-that-lead-to-big-data , 2011.
6. Noguchi Yuki. The search for analysts to make sense of big data. http://www.npr.org/2011/11/
30/142893065/the-searchforanalysts-to-make-sense-of-big-data , 2011.
7. Big data. http://www.nature.com/news/specials/bigdata/index.html , 2008.
8. Special online collection: Dealing with big data. http://www.sciencemag.org/site/special/data/ ,
2011.
9. Fact sheet: Big data across the federal government. http://www.whitehouse.gov/sites/default/
files/microsites/ostp/big_data_fact_sheet_3_29_2012.pdf , 2012.
10. O. R. Team. Big Data Now: Current Perspectives from O'Reilly Radar . O'Reilly Media, 2011.
11. M Grobelnik. Big data tutorial. http://videolectures.net/eswc2012grobelnikbigdata/ , 2012.
12. James Manyika, McKinsey Global Institute, Michael Chui, Brad Brown, Jacques Bughin,
Richard Dobbs, Charles Roxburgh, and Angela Hung Byers. Big data: The next frontier for
innovation, competition, and productivity . McKinsey Global Institute, 2011.
13. Viktor Mayer-Schönberger and Kenneth Cukier. Big Data: A Revolution that Will Transform
how We Live, Work, and Think . Eamon Dolan/Houghton Mifflin Harcourt, 2013.
14. Douglas Laney. 3-d data management: Controlling data volume, velocity and variety. META
Group Research Note, February , 6, 2001.
15. Paul Zikopoulos, Chris Eaton, et al. Understanding big data: Analytics for enterprise class
hadoop and streaming data . McGraw-Hill Osborne Media, 2011.
16. Erik Meijer. The world according to linq. Communications of the ACM , 54(10):45-51, 2011.
17. Mark Beyer. Gartner says solving 'big data' challenge involves more than just managing
volumes of data. Gartner . http://www.gartner.com/it/page.jsp , 2011.
Search WWH ::




Custom Search