Research on Business Intelligence with Data Mining Applications
International Journal of Business and Economics Research
Volume 6, Issue 2, April 2017, Pages: 19-24
Received: Apr. 21, 2017; Published: Apr. 21, 2017
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Authors
Jason C. H. Chen, School of Business Administration, Gonzaga University, Spokane, USA
Napoleone Piani, School of Business Administration, Gonzaga University, Spokane, USA
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Abstract
Business Intelligence (BI) has become an important agenda for many top executives because they have become extremely aware of its value in providing a competitive differentiator at all levels of the organizations. This paper discusses the concepts and technologies of business intelligence, specially, data warehousing and data mining and how these can positively influence and benefit a business. Review on BI frameworks and research models for developing data warehousing and data mining are presented and analyzed. The paper also illustrates a business scenario in which the Rapidminer, a data mining tool can be used to extrapolate relevant data to a small startup ski shop.
Keywords
business intelligence (BI), data mining, rapidminer
To cite this article
Jason C. H. Chen, Napoleone Piani, Research on Business Intelligence with Data Mining Applications, International Journal of Business and Economics Research. Vol. 6, No. 2, 2017, pp. 19-24. doi: 10.11648/j.ijber.20170602.11
References
[1]
Berbel R. L. T. and Gonzalez S. SM (2015). “How to help end users to get better decisions” Personalising OLAP aggregation queries through semantic recommendation of text documents, International Journal of Business Intelligence and Data mining, Vol. 10, No. 1.
[2]
Biere, Mike (2010). The New Era of Enterprise Business Intelligence, IBM Press.
[3]
Chaudhuri, Surajit; Dayal, Umeshwar; Narasayya, Vivek (2011). "An Overview of Business Intelligence Technology", Communications of the ACM 54.8 pp. 88-98.
[4]
Hastie, Trevor; Tibshirani, Robert; Friedman, Jerome (2001). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer-Verlag.
[5]
Hoffer, J. A., Topi, H. and Ramesh, V. (2014). Essentials of Data Management, Pearson.
[6]
Kroenke, D. M. and Boyle, R. J. (2017). Using MIS, Pearson.
[7]
North, Matthew (2012). Data Mining for the Masses, A Global Text Project Book.
[8]
Rahman M. M., Maksud, U. A. and Rahman, S. M. M. (2015). “An open multi-tier architecture for high-performance data mining using SOA,” International Journal of Data Mining, Modelling and Management, Vol. 7, No. 1.
[9]
StatSoft.com (2004), available at https://www.statsoft.com/Portals/0/Customers/Success_Stories/argonauten360.pdf.
[10]
Stock, Tom. (2011). "Using a Data Warehouse to Solve Risk, Performance, Reporting and Compliance-Related Issues," Journal of Securities Operations & Custody 3.4, pp. 305-315.
[11]
Turban, E., Sharda R. and Delen D. (2014). “Business Intelligence: A Managerial Approach”, Prentice-Hall, 3rd Edition.
[12]
Victor, N. and Lopez, D. (2016). “Privacy models for big data: a survey”, International Journal of Big Data Intelligence, Vol. 3, No. 1.
[13]
Wikipedia.a (2017), available at: https://en.wikipedia.org/wiki/Ken_Olsen.
[14]
Wikipedia.b (2017), available at: https://en.wikipedia.org/wiki/Hartsfield%E2%80%93Jackson_Atlanta_International_Airport.
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