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The Impacts of Data Stream Mining on Real-Time Business Intelligence
Yang, Hang; Fong, Simon
Conference Name2nd international Conference on IT & Business Intelligence
Source PublicationProceedings of 2nd international Conference on IT & Business Intelligence (ITBI-10)
Conference Date2010-11
Conference PlaceNagpur, Tamil Nadu, India
PublisherIMT CASE Journal

Real-time Business Intelligence (rt-BI) is an emerging field for business executives who need to make effective decision in a very short time. This kind of immediate real-time decisions may not necessarily be based on historical data; instead the decisions are derived from the most recent data obtained usually just minutes or seconds ago. A number of latest IT technologies are promising for rt-BI, such as real-time Data Warehouse, Complex Event Processing, real-time ETL, data stream base management systems, Stream Query Processing, and several rt-BI architectures that are available from both academic research and commercial products. One core component in the data analytic layer of typical rt-BI architecture is the data mining algorithm. Although stream data mining has been studied extensively during the last decade in algorithmic level, it has not been evaluated in relation to rt-BI. In this paper we conduct simulation experiments over traditional data mining algorithms vis-à-vis data stream mining algorithm with respect to their performance and applicability in rt-BI. Both synthetic and live data up to size of 106 are used in the tests. The results would be a useful reference for information technologists who want to implement rt-BI applications with the appropriate choice of mining algorithms

KeywordData Stream Mining Real-time Business Intelligence Performance Evaluation Java Weka Moa
URLView the original
Fulltext Access
Document TypeConference paper
CollectionFaculty of Science and Technology
AffiliationUniversity of Macau
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Yang, Hang,Fong, Simon. The Impacts of Data Stream Mining on Real-Time Business Intelligence[C]:IMT CASE Journal,2010.
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