{"id":156812,"date":"2004-01-01T00:00:00","date_gmt":"2004-01-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/automatic-categorization-of-query-results\/"},"modified":"2018-10-16T21:12:25","modified_gmt":"2018-10-17T04:12:25","slug":"automatic-categorization-of-query-results","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/automatic-categorization-of-query-results\/","title":{"rendered":"Automatic Categorization of Query Results"},"content":{"rendered":"
\n

Exploratory ad-hoc queries could return too many answers \u2013 a phenomenon commonly referred to as \u201cinformation overload\u201d. In this paper, we propose to automatically categorize the results of SQL queries to address this problem. We dynamically generate a labeled, hierarchical category structure \u2013 users can determine whether a category is relevant or not by examining simply its label; she can then explore just the relevant categories and ignore the remaining ones, thereby reducing information overload. We first develop analytical models to estimate information overload faced by a user for a given exploration. Based on those models, we formulate the categorization problem as a cost optimization problem and develop heuristic algorithms to compute the min-cost categorization.<\/p>\n<\/div>\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

Exploratory ad-hoc queries could return too many answers \u2013 a phenomenon commonly referred to as \u201cinformation overload\u201d. In this paper, we propose to automatically categorize the results of SQL queries to address this problem. We dynamically generate a labeled, hierarchical category structure \u2013 users can determine whether a category is relevant or not by examining […]<\/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":[13563,13555],"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-156812","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-data-platform-analytics","msr-research-area-search-information-retrieval","msr-locale-en_us"],"msr_publishername":"","msr_edition":"ACM SIGMOD 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