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SIGMOD '19: Proceedings of the 2019 International Conference on Management of Data



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Information systems -- (Computer science), Data structures (Computer science)


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.


© ACM {2109}. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published as: Event Trend Aggregation Under Rich Event Matching Semantics. In Proceedings of the 2019 International Conference on Management of Data (pp. 555-572). Version of record:



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