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<oembed><version>1.0</version><provider_name>Microsoft Research</provider_name><provider_url>https://www.microsoft.com/en-us/research</provider_url><author_name>Olga Poppe</author_name><author_url>https://www.microsoft.com/en-us/research/people/olpoppe/</author_url><title>To Share or not to Share Online Event Trend Aggregation Over Bursty Event Streams - Microsoft Research</title><type>rich</type><width>600</width><height>338</height><html>&lt;blockquote class="wp-embedded-content" data-secret="wHN9EjZIv3"&gt;&lt;a href="https://www.microsoft.com/en-us/research/publication/hamlet/"&gt;To Share or not to Share Online Event Trend Aggregation Over Bursty Event Streams&lt;/a&gt;&lt;/blockquote&gt;&lt;iframe sandbox="allow-scripts" security="restricted" src="https://www.microsoft.com/en-us/research/publication/hamlet/embed/#?secret=wHN9EjZIv3" width="600" height="338" title="&#x201C;To Share or not to Share Online Event Trend Aggregation Over Bursty Event Streams&#x201D; &#x2014; Microsoft Research" data-secret="wHN9EjZIv3" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"&gt;&lt;/iframe&gt;&lt;script type="text/javascript"&gt;
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</html><description>Complex event processing (CEP) systems continuously evaluate large workloads of pattern queries under tight time constraints. Event trend aggregation queries with Kleene patterns are commonly used to retrieve summarized insights about the recent trends in event streams. State-of-art methods are limited either due to repetitive computations or unnecessary trend construction. Existing shared approaches are guided [&hellip;]</description></oembed>
