Dynamic adjustment of sliding windows over data streams

The data stream systems provide sliding windows to preserve the arrival of recent streaming data in order to support continuous queries in real-time. In this paper, we consider the problem of adjusting the buffer size of sliding windows dynamically when the rate of streaming data changes or when queries start or end. Based on the status of available memory resource and the requirement of queries for memory, we propose the corresponding algorithms of adjustment with greedy method and dynamic programming method, which minimize the total error of queries or achieve low memory overhead. The analytical and experimental results show that our algorithms can be applied to the data stream systems efficiently.