Data Mining with Big Data Using HACE Theorem

Pawan P, Trivikram Rao

Abstract


The term Big Data comprises large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity, Big Data are now rapidly expanding in all science and engineering domains, including physical, biological and biomedical sciences. This paper presents a HACE theorem that characterizes the features of the Big Data revolution, and proposes a Big Data processing model, from the data mining perspective. This data-driven model involves demand-driven aggregation of information sources, mining and analysis, user interest modeling, and security and privacy considerations. We analyze the challenging issues in the data-driven model and also in the Big Data revolution.


References


R. Ahmed and G. Karypis, “Algorithms for Mining the Evolution of Conserved Relational States in Dynamic Networks,†Knowledge and Information Systems, vol. 33, no. 3, pp. 603-630, Dec. 2012.

M.H. Alam, J.W. Ha, and S.K. Lee, “Novel Approaches to Crawling Important Pages Early,†Knowledge and Information Systems, vol. 33, no. 3, pp 707-734, Dec. 2012.

S. Aral and D. Walker, “Identifying Influential and Susceptible Members of Social Networks,†Science, vol. 337, pp. 337-341, 2012.

A. Machanavajjhala and J.P. Reiter, “Big Privacy: Protecting Confidentiality in Big Data,†ACM Crossroads, vol. 19, no. 1, pp. 20-23, 2012.

S. Banerjee and N. Agarwal, “Analyzing Collective Behavior from Blogs Using Swarm Intelligence,†Knowledge and Information Systems, vol. 33, no. 3, pp. 523-547, Dec. 2012.

E. Birney, “The Making of ENCODE: Lessons for Big-Data Projects,†Nature, vol. 489, pp. 49-51, 2012.

J. Bollen, H. Mao, and X. Zeng, “Twitter Mood Predicts the Stock Market,†J. Computational Science, vol. 2, no. 1, pp. 1-8, 2011.

S. Borgatti, A. Mehra, D. Brass, and G. Labianca, “Network Analysis in the Social Sciences,†Science, vol. 323, pp. 892-895, 2009.

J. Bughin, M. Chui, and J. Manyika, Clouds, Big Data, and Smart Assets: Ten Tech-Enabled Business Trends to Watch. McKinSey Quarterly, 2010.

D. Centola, “The Spread of Behavior in an Online Social Network Experiment,†Science, vol. 329, pp. 1194-1197, 2010.

E.Y. Chang, H. Bai, and K. Zhu, “Parallel Algorithms for Mining Large-Scale Rich-Media Data,†Proc. 17th ACM Int’l Conf. Multimedia, (MM ’09,) pp. 917-918, 2009.

R. Chen, K. Sivakumar, and H. Kargupta, “Collective Mining of Bayesian Networks from Distributed Heterogeneous Data,†Knowledge and Information Systems, vol. 6, no. 2, pp. 164-187, 2004.

Y.-C. Chen, W.-C. Peng, and S.-Y. Lee, “Efficient Algorithms for Influence Maximization in Social Networks,†Knowledge and Information Systems, vol. 33, no. 3, pp. 577-601, Dec. 2012.

C.T. Chu, S.K. Kim, Y.A. Lin, Y. Yu, G.R. Bradski, A.Y. Ng, and K. Olukotun, “Map-Reduce for Machine Learning on Multicore,†Proc. 20th Ann. Conf. Neural Information Processing Systems (NIPS’06), pp. 281-288, 2006.

G. Cormode and D. Srivastava, “Anonymized Data: Generation, Models, Usage,†Proc. ACM SIGMOD Int’l Conf. Management Data, pp. 1015-1018, 2009.

“IBM What Is Big Data: Bring Big Data to the Enterprise,†http:// www-01.ibm.com/software/data/bigdata/, IBM, 2012.


Full Text: PDF [Full Text]

Refbacks

  • There are currently no refbacks.


Copyright © 2013, All rights reserved.| ijseat.com

Creative Commons License
International Journal of Science Engineering and Advance Technology is licensed under a Creative Commons Attribution 3.0 Unported License.Based on a work at IJSEat , Permissions beyond the scope of this license may be available at http://creativecommons.org/licenses/by/3.0/deed.en_GB.

Â