Most of emerging applications deal with an infinite data stream in an incessant, immense and volatile manner. Consequently, it is very important to analyze not only the varying characteristics of a source data stream in a short-term period but also those in a long-term period. For this purpose, this paper demonstrates an OLAP system, DS-Cuber (Data Stream Cuber) for the analysis of data streams. The proposed system consists of two analytic components: short-term and long-term, so that it can provide an integrated analysis environment for infinite data streams. Furthermore, each of these two components supports diversified exception detection methods which can be used for the automatic identification of abnormality in the data elements of a data stream in order to guide the data cube navigation of a user effectively. Network traffic flow streams are used to demonstrate the features of the DS-Cube system.