{"id":184740,"date":"2010-03-29T00:00:00","date_gmt":"2010-04-03T21:46:49","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/lahar-warehousing-markovian-streams\/"},"modified":"2016-08-22T11:28:23","modified_gmt":"2016-08-22T18:28:23","slug":"lahar-warehousing-markovian-streams","status":"publish","type":"msr-video","link":"https:\/\/www.microsoft.com\/en-us\/research\/video\/lahar-warehousing-markovian-streams\/","title":{"rendered":"Lahar:  Warehousing Markovian Streams"},"content":{"rendered":"<div class=\"asset-content\">\n<p>In this talk, I present Lahar, a warehousing system for a general class of imprecise, sequential data called Markovian streams.  These imprecise streams are commonly used to model location sequences inferred from noisy sensors such as RFID\/GPS, text inferred from spoken audio, etc.  In the context of Lahar, I introduce algorithms for supporting sophisticated analytics on these streams (e.g. &#8220;How many coffee breaks did Bob take in May that lasted over an hour?&#8221; or &#8220;Find the start\/end timestamps of every podcast snippet containing the phrase &#8216;health care&#8217;.&#8221;)  The rich semantics of both queries and data in Lahar pose serious efficiency challenges.  In this talk, I present several techniques to address these challenges, including novel indexing and approximation approaches.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this talk, I present Lahar, a warehousing system for a general class of imprecise, sequential data called Markovian streams. These imprecise streams are commonly used to model location sequences inferred from noisy sensors such as RFID\/GPS, text inferred from spoken audio, etc. In the context of Lahar, I introduce algorithms for supporting sophisticated analytics [&hellip;]<\/p>\n","protected":false},"featured_media":195571,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr_hide_image_in_river":0,"footnotes":""},"research-area":[],"msr-video-type":[],"msr-locale":[268875],"msr-post-option":[],"msr-session-type":[],"msr-impact-theme":[],"msr-pillar":[],"msr-episode":[],"msr-research-theme":[],"class_list":["post-184740","msr-video","type-msr-video","status-publish","has-post-thumbnail","hentry","msr-locale-en_us"],"msr_download_urls":"","msr_external_url":"https:\/\/youtu.be\/p_WdJYjypmI","msr_secondary_video_url":"","msr_video_file":"","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/184740","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-video"}],"version-history":[{"count":0,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/184740\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media\/195571"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=184740"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=184740"},{"taxonomy":"msr-video-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video-type?post=184740"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=184740"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=184740"},{"taxonomy":"msr-session-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-session-type?post=184740"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=184740"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=184740"},{"taxonomy":"msr-episode","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-episode?post=184740"},{"taxonomy":"msr-research-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-theme?post=184740"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}