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Streaming applications from cluster monitoring to algorithmic
trading deploy Kleene queries to detect and aggregate
event trends. Rich event matching semantics determine how
to compose events into trends. The expressive power of stateof-
the-art streaming systems remains limited since they do
not support many of these semantics. Worse yet, they suffer
from long delays and high memory costs because they
maintain aggregates at a fine granularity. To overcome these
limitations, our Coarse-Grained Event Trend Aggregation
(Cogra) approach supports a rich variety of event matching
semantics within one system. Better yet, Cogra incrementally
maintains aggregates at the coarsest granularity
possible for each of these semantics. In this way, Cogra minimizes
the number of aggregates – reducing both time and
space complexity. Our experiments demonstrate that Cogra
achieves up to six orders of magnitude speed-up and up to
seven orders of magnitude memory reduction compared to
state-of-the-art approaches.
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