English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Complex event processing (CEP) systems continuously evaluate large workloadsof pattern queries under tight time constraints. Event trend aggregationqueries with Kleene patterns are commonly used to retrieve summarized insightsabout the recent trends in event streams. State-of-art methods are limitedeither due to repetitive computations or unnecessary trend construction.Existing shared approaches are guided by statically selected and hence rigidsharing plans that are often sub-optimal under stream fluctuations. In thiswork, we propose a novel framework Hamlet that is the first to overcome theselimitations. Hamlet introduces two key innovations. First, Hamlet adaptivelydecides whether to share or not to share computations depending on the currentstream properties at run time to harvest the maximum sharing benefit. Second,Hamlet is equipped with a highly efficient shared trend aggregation strategythat avoids trend construction. Our experimental study on both real andsynthetic data sets demonstrates that Hamlet consistently reduces query latencyby up to five orders of magnitude compared to the state-of-the-art approaches.

To Share, or not to Share Online Event Trend Aggregation Over Bursty Event Streams