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