{"id":164964,"date":"2013-06-01T00:00:00","date_gmt":"2013-06-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/stat-an-interactive-analytics-environment-for-big-data\/"},"modified":"2018-10-16T21:21:03","modified_gmt":"2018-10-17T04:21:03","slug":"stat-an-interactive-analytics-environment-for-big-data","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/stat-an-interactive-analytics-environment-for-big-data\/","title":{"rendered":"Stat! &#8211; An Interactive Analytics Environment for Big Data"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Exploratory analysis on big data requires us to rethink data management across the entire stack \u2013 from the underlying data processing techniques to the user experience. We demonstrate Stat! \u2013 a visualization and analytics environment that allows users to rapidly experiment with exploratory queries over big data. Data scientists can use Stat! to quickly refine to the correct query, while getting immediate feedback after processing a fraction of the data. Stat! can work with multiple processing engines in the backend; in this demo, we use Stat! with the Microsoft StreamInsight streaming engine. StreamInsight is used to generate incremental early results to queries and refine these results as more data is processed. Stat! allows data scientists to explore data, dynamically compose multiple queries to generate streams of partial results, and display partial results in both textual and visual form.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Exploratory analysis on big data requires us to rethink data management across the entire stack \u2013 from the underlying data processing techniques to the user experience. We demonstrate Stat! \u2013 a visualization and analytics environment that allows users to rapidly experiment with exploratory queries over big data. Data scientists can use Stat! to quickly refine [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13556,13554],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-164964","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-research-area-human-computer-interaction","msr-locale-en_us"],"msr_publishername":"ACM SIGMOD","msr_edition":"ACM SIGMOD International Conference on Management of Data (SIGMOD 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