The problem of statistics and aggregate maintenance over data streams has gained popularity in recent years especially in telecommunications network monitoring, trend-related analysis, web-click streams, stock tickers, and other time-variant data. The amount of data generated in such applications can become too large to store, or if stored too large to scan multiple times. We consider queries over data streams that are biased towards the more recent values. We develop a technique that summarizes a dynamic stream incrementally at multiple resolutions. This approximation can be used to answer point queries, range queries, and inner product queries. Moreover the precision of answers can be changed adoptively by a client.