Advanced data analytics for business

Big Data Computing

Volumn , Issue , 2013, Pages 373-397

  Akerkar, Rajendra ^a  

^a WESTERN NORWAY RESEARCH INSTITUTE   (Norway)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84988252175     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1201/b16014     Document Type: Chapter

References (12)

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2. Akerkar, R. and Sajja, P. 2012. Intelligent Technologies for Web Applications, Taylor & Francis, USA.
3. Chaudhuri, S., Dayal, U., and Narasayya, V. 2011. An overview of business intelligence technology, Communications of the ACM (54:8), 88–98.
4. Davenport, T. H. and Harris, J. G. 2007, Competing on Analytics: The New Science of Winning, Harvard Business School, Boston, MA.
5. Gelfand, A. 2011/2012. Privacy and biomedical research: Building a trust infrastructure— an exploration of data-driven and process-driven approaches to data privacy, Biomedical Computation Review, Winter, 23–28.
6. Henschen, D. 2011. “Why All the Hadoopla?” Information Week, November 14, pp. 19–26.
7. Mitchell, T. M. 1995. Machine Learning. McGraw-Hill, New York.
8. Patterson, D. A. 2008. Technical perspective: The data center is the computer, Communications of the ACM (51:1), 105.
9. Settles, B. 2011. Algorithms for active learning. In B. Krishnapuram, S. Yu, and R.B. Rao (Eds.), Cost-Sensitive Machine Learning. Chapman & Hall, Boca Raton, FL.
10. Stonebraker, M., Abadi, D., DeWitt, D. J., Madden, S., Pavlo, A., and Rasin, A. 2012. MapReduce and parallel DBMSs: Friends or foes, Communications of the ACM (53:1), 64–71.
11. van der Aalst, W. 2012. Process mining: Overview and opportunities, ACM Transactions on Management Information Systems (3:2), 7:1–7:17.
12. Witten, I. H., Frank, E., and Hall, M. 2011. Data Mining: Practical Machine Learning Tools and Techniques, 3rd ed., Morgan Kaufmann, San Francisco, CA.