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In this dissertation, we (the author and her research collaborators) consider a distributed system that disseminates highvolume event streams to many simultaneous monitoring applications over a lowbandwidth network. For bandwidth efficiency, we propose a groupaware stream filtering'' approach, used together with multicasting, that exploits two overlooked, yet important, properties of monitoring applications: 1) many of them can tolerate some degree of slack'' in their data quality requirements, and 2) there may exist multiple subsets of the source data satisfying the quality needs of an application. We can thus choose the best alternative'' subset for each application to maximize the data overlap within the group to best benefit from multicasting. Here we provide a general framework for the groupaware stream filtering problem, which we prove is NPhard. We introduce a suite of heuristicsbased algorithms that ensure data quality (specifically, granularity and timeliness) while preserving bandwidth. Our evaluation shows that groupaware stream filtering is effective in trading CPU time for bandwidth savings, compared with selfinterested filtering.
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