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Available viahttp://dbpubs.stanford.edu/pub/2002-55
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Submitted on 15th of November 2002
Author Olston, Chris; Jiang, Jing; Widom, Jennifer
Title Adaptive Filters for Continuous Queries over Distributed Data Streams
Date of publication 2002
Published in Technical Report
Citation Olston, Chris; Jiang, Jing; Widom, Jennifer. Adaptive Filters for Continuous Queries over Distributed Data Streams, Technical Report
Number of pages 19
Language English
Project TRAPP
Type Other
Subject group Distributed Systems
Abstract We consider an environment where distributed data sources continuously stream updates to a centralized processor that monitors continuous queries over the distributed data. Significant communication overhead is incurred in the presence of rapid update streams, and we propose a new technique for reducing the overhead. Users register continuous queries with precision requirements at the central stream processor, which installs filters at remote data sources. The filters adapt to changing conditions to minimize stream rates while guaranteeing that all continuous queries still receive the updates necessary to provide answers of adequate precision at all times. Our approach enables applications to trade precision for communication overhead at a fine granularity by individually adjusting the precision constraints of continuous queries over streams in a multi-query workload. Through experiments performed on synthetic data simulations and a real network monitoring implementation, we demonstrate the effectiveness of our approach in achieving low communication overhead compared with alternate approaches.
Keywords adaptive filters, distributed data streams, approximate answers
Contact address olston@stanford.edu
Sponsored by This work was supported by the National Science Foundation under grants IIS-0118173 and IIS-9817799 and by a National Science Foundation graduate research fellowship.
Notes This paper supplants a previous February 2002 technical report by Olston and Widom entitled "Approximate Caching for Continuous Queries over Distributed Data Sources."
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