  Streaming applications from health care analytics 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
state-of-the-art systems remains limited in that they do not support the rich
variety of these semantics. Worse yet, they suffer from long delays and high
memory costs because they opt to maintain aggregates at a fine granularity. To
overcome these limitations, our Coarse-Grained Event Trend Aggregation (Cogra)
approach supports this rich diversity 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 four orders of magnitude
speed-up and up to eight orders of magnitude memory reduction compared to
state-of-the-art approaches.
