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Load Shedding for Window Joins over Streams

Authors: Donghong Han; Chuan Xiao 0001; Rui Zhou 0001; Guoren Wang; Huan Huo; Xiaoyun Hui;

Load Shedding for Window Joins over Streams

Abstract

We present a novel load shedding technique over sliding window joins. We first construct a dual window architectural model including join-windows and aux-windows. With the statistics built on aux-windows, an effective load shedding strategy is developed to produce maximum subset join outputs. For the streams with high arrival rates, we propose an approach incorporating front-shedding and rear-shedding, and then address the problem of how to cooperate these two shedding processes through a series of calculations. Based on extensive experimentation with synthetic data and real life data, we show that our load shedding strategy delivers superb join output performance, and dominates the existing strategies.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
6
Average
Top 10%
Average
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