Memory-Limited Execution of Windowed Stream Joins

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Memory-Limited Execution of Windowed Stream Joins
We address the problem of computing approximate answers to continuous sliding-window joins over data streams when the available memory may be insufficient to keep the entire join state. One approximation scenario is to provide a maximum subset of the result, with the objective of losing as few result tuples as possible. An alternative scenario is to provide a random sample of the join result, e.g., if the output of the join is being aggregated. We show formally that neither approximation can be addressed effectively for a slidingwindow join of arbitrary input streams. Previous work has addressed only the maximum-subset problem, and has implicitly used a frequencybased model of stream arrival. We address the sampling problem for this model. More importantly, we point out a broad class of applications for which an age-based model of stream arrival is more appropriate, and we address both approximation scenarios under this new model. Finally, for the case of multiple joins being execute...
Utkarsh Srivastava, Jennifer Widom
Added 02 Jul 2010
Updated 02 Jul 2010
Type Conference
Year 2004
Where VLDB
Authors Utkarsh Srivastava, Jennifer Widom
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