{"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 [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"user_nicename","value":"Justin Ding","user_id":"32435"},{"type":"user_nicename","value":"Shi Han","user_id":"33618"},{"type":"user_nicename","value":"Yong Xu","user_id":"37125"},{"type":"user_nicename","value":"Haidong Zhang","user_id":"31953"},{"type":"user_nicename","value":"Dongmei Zhang","user_id":"31665"}],"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and\/or a fee. Request permissions from Permissions@acm.org. SIGMOD\u201919, June 30-July 5, 2019, Amsterdam, Netherlands \u00a9 2019 Association for Computing Machinery. 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