Stochastically consistent caching and dynamic duty cycling for erratic sensor sources
DCOSS'06 Proceedings of the Second IEEE international conference on Distributed Computing in Sensor Systems
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We present a novel approach to approximate evaluationof standing aggregate queries over streaming data, subjectto user-specified error bounds. Our method models thebehavior of aggregates as Brownian motions, and adaptivelyupdates the model according to stream characteristics.This approach has two advantages. First, it greatly improvessystem scalability since we can defer query evaluationas long as the difference between the returned andtrue aggregate values remains within user-specified bounds.Second, we are able to provide approximate answers duringstream interruptions by estimating the rate at which thestreams and the aggregate drift during the blackout periods.We also study processor allocation issues in such approximateaggregate evaluation systems. Our experiments showthat our model captures the behavior of real-world streamssuch as sensor data and stock traces with excellent fidelity,and scales very well for large numbers of standing queries.