Database management systems (DBMSs) have been used successfully in business applications that require persistent storage of relatively static data. However, emerging applications, such as sensor networks, real-time Internet traffic analysis and on-line financial trading, require support for continuous processing of unbounded streams of data. The fundamental assumption of a data stream management system (DSMS) is that new data are generated continually, making it infeasible to store a stream in its entirety. At best, we may be able to maintain a sliding window of recently arrived data. Furthermore, since the contents of a sliding window evolve over time, it makes sense for users to ask a query once and receive updated answers over time. In this talk, I will describe my recent research on shared processing of continuous aggregation queries over sliding windows.