A scalable approach to approximating aggregate queries over intermittent streams

We present a novel approach to approximate evaluation of standing aggregate queries over streaming data, subject to user-specified error bounds. Our method models the behavior of aggregates as Brownian motions, and adaptively updates the model according to stream characteristics. This approach has two advantages. First, it greatly improves system scalability since we can defer query evaluation as long as the difference between the returned and true aggregate values remains within user-specified bounds. Second, we are able to provide approximate answers during stream interruptions by estimating the rate at which the streams and the aggregate drift during the blackout periods. We also study processor allocation issues in such approximate aggregate evaluation systems. Our experiments show that our model captures the behavior of real-world streams such as sensor data and stock traces with excellent fidelity, and scales very well for large numbers of standing queries.