{"id":588988,"date":"2019-05-20T23:06:48","date_gmt":"2019-05-21T06:06:48","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&#038;p=588988"},"modified":"2019-07-02T19:22:32","modified_gmt":"2019-07-03T02:22:32","slug":"quickinsights-quick-and-automatic-discovery-of-insights-from-multi-dimensional-data","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/quickinsights-quick-and-automatic-discovery-of-insights-from-multi-dimensional-data\/","title":{"rendered":"QuickInsights: Quick and Automatic Discovery of Insights from Multi-Dimensional Data"},"content":{"rendered":"<p>Discovering interesting data patterns is a common and important analytical need in data analysis and exploration, with increasing user demand for automated discovery abilities. However, automatically discovering interesting patterns from multi-dimensional data remains challenging. Existing techniques focus on mining individual types of patterns. There is a lack of unified formulation for different pattern types, as well as general mining frameworks to derive them effectively and efficiently. We present a novel technique <em>QuickInsights<\/em>, which quickly and automatically discovers interesting patterns from multi-dimensional data. <em>QuickInsights<\/em> proposes a unified formulation of interesting patterns, called <em>insights<\/em>, and designs a systematic mining framework to discover high-quality insights efficiently. We demonstrate the effectiveness and efficiency of <em>QuickInsights<\/em> through our evaluation on 447 real datasets as well as user studies on both expert users and non-expert users. <em>QuickInsights<\/em> is released in Microsoft Power BI.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Discovering interesting data patterns is a common and important analytical need in data analysis and exploration, with increasing user demand for automated discovery abilities. However, automatically discovering interesting patterns from multi-dimensional data remains challenging. Existing techniques focus on mining individual types of patterns. There is a lack of unified formulation for different pattern types, as 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