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A Perspective on Data Mining Integration with Business Intelligence

A Perspective on Data Mining Integration with Business Intelligence

Ana Azevedo, Manuel Filipe Santos
Copyright: © 2011 |Pages: 21
ISBN13: 9781609600679|ISBN10: 1609600673|EISBN13: 9781609600693
DOI: 10.4018/978-1-60960-067-9.ch006
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MLA

Azevedo, Ana, and Manuel Filipe Santos. "A Perspective on Data Mining Integration with Business Intelligence." Knowledge Discovery Practices and Emerging Applications of Data Mining: Trends and New Domains, edited by A.V. Senthil Kumar, IGI Global, 2011, pp. 109-129. https://doi.org/10.4018/978-1-60960-067-9.ch006

APA

Azevedo, A. & Santos, M. F. (2011). A Perspective on Data Mining Integration with Business Intelligence. In A. Kumar (Ed.), Knowledge Discovery Practices and Emerging Applications of Data Mining: Trends and New Domains (pp. 109-129). IGI Global. https://doi.org/10.4018/978-1-60960-067-9.ch006

Chicago

Azevedo, Ana, and Manuel Filipe Santos. "A Perspective on Data Mining Integration with Business Intelligence." In Knowledge Discovery Practices and Emerging Applications of Data Mining: Trends and New Domains, edited by A.V. Senthil Kumar, 109-129. Hershey, PA: IGI Global, 2011. https://doi.org/10.4018/978-1-60960-067-9.ch006

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Abstract

Business Intelligence (BI) is an emergent area of the Decision Support Systems (DSS) discipline. Over the past years, the evolution in this area has been considerable. Similarly, in the last years, there has been a huge growth and consolidation of the Data Mining (DM) field. DM is being used with success in BI systems, but a truly DM integration with BI is lacking. The purpose of this chapter is to discuss the relevance of DM integration with BI, and its importance to business users. From the literature review, it was observed that the definition of an underlying structure for BI is missing, and therefore a framework is presented. It was also observed that some efforts are being done that seek the establishment of standards in the DM field, both by academics and by people in the industry. Supported by those findings, this chapter introduces an architecture that can conduct to an effective usage of DM in BI. This architecture includes a DM language that is iterative and interactive in nature. This chapter suggests that the effective usage of DM in BI can be achieved by making DM models accessible to business users, through the use of the presented DM language.

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