{"id":168225,"date":"2015-04-01T00:00:00","date_gmt":"2015-04-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/squeries-visual-regular-expressions-for-querying-and-exploring-event-sequences\/"},"modified":"2018-10-16T20:11:05","modified_gmt":"2018-10-17T03:11:05","slug":"squeries-visual-regular-expressions-for-querying-and-exploring-event-sequences","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/squeries-visual-regular-expressions-for-querying-and-exploring-event-sequences\/","title":{"rendered":"(s|qu)eries: Visual Regular Expressions for Querying and Exploring Event Sequences"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Many different domains collect event sequence data and rely on finding and analyzing patterns within it to gain meaningful insights. Current systems that support such queries either provide limited expressiveness, hinder exploratory workflows or present interaction and visualization models which do not scale well to large and multi-faceted data sets. In this paper we present (s<em>|<\/em>qu)eries (pronounced \u201cSqueries\u201d), a visual query interface for creating queries on sequences (series) of data, based on regular expressions. (s<i>|<\/i>qu)eries is a touchbased system that exposes the full expressive power of regular expressions in an approachable way and interleaves query specification with result visualizations. Being able to visually investigate the results of different query-parts supports debugging and encourages iterative query-building as well as exploratory work-flows. We validate our design and implementation through a set of informal interviews with data scientists that analyze event sequences on a daily basis.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Many different domains collect event sequence data and rely on finding and analyzing patterns within it to gain meaningful insights. Current systems that support such queries either provide limited expressiveness, hinder exploratory workflows or present interaction and visualization models which do not scale well to large and multi-faceted data sets. In this paper we present [&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":null,"msr_publishername":"ACM - Association for Computing Machinery","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":"\u00a9 ACM. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. 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