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Overview on Data Mining Schemes to Design Business Intelligence Framework for Mobile Technology.

Authors :
Tembhurkar, Manish P.
Tugnayat, R. M.
Nagdive, Ashlesha S.
Source :
International Journal of Advanced Research in Computer Science; Nov/Dec2014, Vol. 5 Issue 8, p128-133, 6p
Publication Year :
2014

Abstract

Mining is such as automated technology which facilitates us to find the relations between various parameters and predicts the future outcomes of business sector. Mining tools (data mining, text mining, and web mining) are used to foreseen the future, profit, investment relations in large databases for BI. In this paper, we review various data mining tools and techniques and their performance in BI sectors. Finally, we shall state some of the important factors to be considered while designing & developing a BI framework using data mining techniques for various sectors. Business Intelligence (BI) has turned out to be a predictable technological advantage in the last couple of years, for the large enterprises (especially Mobile Technology Ventures). Such ventures could afford to buy, implement and maintain BI solutions. Recently, small and medium size enterprises all over the globe have understood the competitive and financial benefits of BI. However, limited IT budgets of small companies and BI's high total cost of ownership have created a gap between large and small enterprises where small enterprises do not become fortunate to avail the virtues of BI because of the affordability factor. This Paper provides the systematic study and analysis of various factors (Data Mining techniques, BI framework and their effects on designing a successful automated decision making system). It presents the strong knowledge foundation so that one can give a proper justice to achieve BI objectives. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
09765697
Volume :
5
Issue :
8
Database :
Complementary Index
Journal :
International Journal of Advanced Research in Computer Science
Publication Type :
Academic Journal
Accession number :
100182796