Explosive increase of real-time data sources, so-called "data streams" (or just "steams") and increasing demands for real-time analysis over streams give rise to realtime analysis over streams. However, developing tailor-made systems for such applications is not always desirable due to high developing costs and long developing periods. To cope with this problem, this paper proposes a novel architecture for online analytical processing (OLAP) over streams exploiting off-the-shelf stream processing engine (SPE) combined with OLAP engine. It allows users to perform OLAP analysis over streams for the latest time period, called Interval of Interest (Iol). The system in the meantime processes multiple continuous query language (CQL) queries corresponding to different aggregation levels in cube lattice. To cover arbitrary aggregation levels using limited system's memory, we propose to partially deploy CQL queries for those with higher reference frequencies, whereas the results are dynamically calculated using existing aggregation results with the help of OLAP engine. For optimal CQL query deployment, we propose a cost-based optimization method that maximizes the performance. The experimental results show that the proposed architecture is feasible enough to realize stream OLAP by combining an SPE and an OLAP engine. Also, the proposed system significantly outperforms other comparative methods by generating optimized query deployment plans.