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ABSTRACT
Streaming analytics deploy Kleene pattern queries to detect and aggregate
event trends on high-rate data streams. Despite increasing
workloads, most state-of-the-art systems process each query independently,
thus missing cost-saving sharing opportunities. Sharing
event trend aggregation poses several technical challenges. First,
Kleene patterns are in general difficult to share due to complex nesting
and arbitrarily long matches. Second, not all sharing opportunities
are beneficial because sharing Kleene patterns incurs non-trivial
overhead to ensure the correctness of final aggregation results. We
propose Muse (Multi-query Shared Event trend aggregation), the
first framework that shares aggregation queries with Kleene patterns
while avoiding expensive trend construction. To find the
beneficial sharing plan, the Muse optimizer effectively selects robust
sharing candidates from the exponentially large search space.
Our experiments demonstrate that Muse increases throughput by
4 orders of magnitude compared to state-of-the-art approaches.
ACM Reference Format:
Allison Rozet, Olga Poppe, Chuan Lei, and Elke A. Rundensteiner. 2020.
MUSE: Multi-query Event Trend Aggregation. In Proceedings of the 29th
ACM International Conference on Information and Knowledge Management
(CIKM ’20), October 19–23, 2020, Virtual Event, Ireland. ACM, New York, NY,
USA, 4 pages. https://doi.org/10.1145/3340531.3412138
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