Algorithmic Level Stream Mining for Business Intelligence System Architecture Building
Yang Hang, Simon Fong
International Journal of Web Applications, Vol. 3, No. 1, 2011.
DOI: 
Link: http://www.dline.info/ijwa/fulltext/v3n1/4.pdf
Abstract:
Datamining has potential applications in several fields including the Business Intelligence (BI) where in the datamining is useful in for understanding the trends and reacting to events. We advocate that the crucial area in dataming, the stream-mining where continuous data streams arrive into the system and get mined very quickly. It further induces the framework for creating a new type of real-time Business Intelligence architecture. The algorithmic level stream mining and digital information system architecture was addressed and applied separately. We now try to focus a single view on the real-time Business Intelligence system architecture powered by stream-mining. Besides we present a few more possible applications in which the proposed architecture can support are discussed..