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Complex event processing (CEP) systems continuously evaluate
large workloads of pattern queries under tight time constraints.
Event trend aggregation queries with Kleene patterns are commonly
used to retrieve summarized insights about the recent trends
in event streams. State-of-art methods are limited either due to
repetitive computations or unnecessary trend construction. Existing
shared approaches are guided by statically selected and hence
rigid sharing plans that are often sub-optimal under stream fluctuations.
In this work, we propose a novel framework Hamlet
that is the first to overcome these limitations. Hamlet introduces
two key innovations. First, Hamlet adaptively decides at run time
whether to share or not to share computations depending on the
current stream properties to harvest the maximum sharing benefit.
Second, Hamlet is equipped with a highly efficient shared trend
aggregation strategy that avoids trend construction. Our experimental
study on both real and synthetic data sets demonstrates that
Hamlet consistently reduces query latency by up to five orders of
magnitude compared to state-of-the-art approaches.
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