TRACING FAST-CHANGING LANDSCAPE OF STUDY ON BIG DATA
Keywords:
TRACINGAbstract
This study traced the fast-changing landscape of study on big data. Three hottest aspects of big data study are: technologies, application to academic study and real value in big data. With the rising of mobile multimedia and social media, more and more data were produced in our daily life. Traditional methods and technologies are inefficient or even powerless facing such large scales of data. Also, academics gained greater abilities to produce much more data than before, but had few good methods of analysing big data. Therefore, new technologies targeting efficient utilization of big data are being proposed, like MapReduce, Hadoop and so on. Methods of applying big data to academic study are also being discussed intensively. Furthermore, the political and business value hidden in big data is very attractive. People believe that although some profits have been made, much more are hidden in big data to be dug out. In this paper, current stage, influential references, outstanding authors, important institutions, top-tier journals and evolution of hot topics are all analysed with help of CiteSpace to map big data study. Finally, forecasting on development of big data study was made to provide help for future study.
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References
C. Snijders, U. Matzat, and U. Reips, “Big data’: Big gaps of knowledge in the field of Internet
science,” International Journal of Internet Science, vol. 1, pp. 1-5, 2012.
J. Manyika et al., Big data: The next frontier for innovation, competition, and productivity, The
McKinsey Global Institute, 2011.
D. Laney, 3D Data Management: Controlling Data Volume, Velocity, and Variety, META Group,
C. Doctorow, “Big data: Welcome to the petacentre,” Nature, vol. 455, no. 7209, pp. 16-21, 2008.
A. Setyono, M. J. Alam, and C. Eswaran, “Study and Development of the Transmission Method for
Large Multimedia File Size Using MMS Technology Study and Development of the Transmission
Method for Large Multimedia File Size Using MMS Technology,” Journal of Mobile Multimedia, vol.
, no. 1, pp. 001-024, 2012.
A. Noman, and C. Adams, “Providing A Data Location Assurance Service for Cloud Storage
Environments,” Journal of Mobile Multimedia, vol. 8, no. 4, pp. 265-286, 2013.
X. Zhang, D. Vogel, and Z. Zhou, “Effects of Information Technologies, Department
Characteristics and Individual Roles on Improving Knowledge Sharing Visibility: A Qualitative Case
Study,” Behaviour & Information Technology, vol. 31, pp. 1117-1131, 2012.
O. Trelles et al., “Big data, but are we ready?,” Nat Rev Genet, vol. 12, no. 3, pp. 224-224, 2011.
X. T. Guo et al., “Chaos Theory as a Lens for Interpreting Blogging,” Journal of Management
Information Systems, vol. 26, no. 1, pp. 101-127, Sum, 2009.
X. Zhang, P. de Pablos, and Y. Zhang, “The Relationship between Incentives, Explicit and Tacit
Knowledge Contribution in Online Engineering Education Project,” International Journal of
Engineering Education, vol. 28, pp. 1341-1346, 2012.
J. Dean, and S. Ghemawat, “MapReduce: Simplified Data Processing on Large Clusters,” To
appear in OSDI, pp. 1, 2004.
T. White, Hadoop: the definitive guide: O'Reilly, 2012.
A. Bialecki et al., “Hadoop: a framework for running applications on large clusters built of
commodity hardware,” Wiki at http://lucene. apache. org/hadoop, vol. 11, 2005.
J. T. Dudley, and A. J. Butte, “In silico research in the era of cloud computing,” Nat Biotech, vol.
, no. 11, pp. 1181-1185, 2010.
A. Jacobs, “The pathologies of big data,” Commun. ACM, vol. 52, no. 8, pp. 36-44, 2009.
C. Lynch, “Big data: How do your data grow?,” Nature, vol. 455, no. 7209, pp. 28-29, 2008.
M. Waldrop, “Big data: Wikiomics,” Nature, vol. 455, no. 7209, pp. 22-25, 2008.
B. Allen et al., “Software as a service for data scientists,” Commun. ACM, vol. 55, no. 2, pp.
-88, 2012.
M. C. Schatz, “CloudBurst: highly sensitive read mapping with MapReduce,” Bioinformatics
(Oxford, England), vol. 25, 2009.
E. E. Schadt et al., “Computational solutions to large-scale data management and analysis,”
Nature Reviews Genetics, vol. 11, no. 9, pp. 647-657, 2010.
D. Howe et al., “Big data: The future of biocuration,” Nature, vol. 455, no. 7209, pp. 47-50, 2008.
A. J. Hey, S. Tansley, and K. M. Tolle, “The fourth paradigm: data-intensive scientific
discovery,” 2009.
T. Kalil. "Big Data is a Big Deal."
B. S. M. News. "How big data analysis helped President Obama defeat Romney in 2012
Elections."
G. Lotan et al., “The revolutions were tweeted: Information flows during the 2011 Tunisian and
Egyptian revolutions,” International Journal of Communication, vol. 5, pp. 1375-1405, 2011.
M. Savage, and R. Burrows, “The coming crisis of empirical sociology,” Sociology, vol. 41, no. 5,
pp. 885-899, 2007.
D. Boyd, and K. Crawford, “Critical Questions for Big Data,” Information, Communication &
Society, vol. 15, no. 5, pp. 662-679, 2012/06/01, 2012.
X. Zhang, P. de Pablos, and Q. Xu, “Knowledge Sharing Visibility in Electronic Knowledge
Management Systems: An Empirical Investigation,” Computers in Human Behavior, vol. 29, pp. 307-
, 2013.
J. Dean, and S. Ghemawat, “MapReduce: simplified data processing on large clusters,”
Communications of the ACM, vol. 51, no. 1, pp. 107-113, 2008.
C. Chen, “Searching for intellectual turning points: Progressive knowledge domain visualization,”
Proceedings of the National Academy of Sciences of the United States of America, vol. 101, no. Suppl
, pp. 5303-5310, 2004.
C. Chen, “CiteSpace II: Detecting and visualizing emerging trends and transient patterns in
scientific literature,” Journal of the American Society for Information Science and Technology, vol. 57,
no. 3, pp. 359-377, 2006.
