{"id":160917,"date":"2010-11-01T00:00:00","date_gmt":"2010-11-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/real-time-spatio-temporal-analytics-using-microsoft-streaminsight\/"},"modified":"2018-10-16T20:49:22","modified_gmt":"2018-10-17T03:49:22","slug":"real-time-spatio-temporal-analytics-using-microsoft-streaminsight","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/real-time-spatio-temporal-analytics-using-microsoft-streaminsight\/","title":{"rendered":"Real-Time Spatio-Temporal Analytics using Microsoft StreamInsight"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Microsoft StreamInsight (StreamInsight, for brevity) is a platform for developing and deploying streaming applications that run continuous queries over high-rate streaming events. StreamInsight adopts a temporal stream model to handle imperfections in event delivery and define consistency guarantees on the output. This demo highlights the ability of StreamInsight to monitor, analyze and correlate spatio-temporal stream data that is generated by moving objects. The demo scenario is based on the Microsoft Shuttle Service where GPS readings are generated and streamed by shuttles as they move around the Microsoft main campus in Redmond, WA. The demo presents a set of relational continuous queries as well as various real-time analytics that help improve the efficiency of the shuttle service in terms of the average wait time per passenger and the average daily mileage per shuttle.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Microsoft StreamInsight (StreamInsight, for brevity) is a platform for developing and deploying streaming applications that run continuous queries over high-rate streaming events. StreamInsight adopts a temporal stream model to handle imperfections in event delivery and define consistency guarantees on the output. This demo highlights the ability of StreamInsight to monitor, analyze and correlate spatio-temporal stream [&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":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13554],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-160917","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-human-computer-interaction","msr-locale-en_us"],"msr_publishername":"ACM","msr_edition":"18th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (GIS '10), San Jose, California","msr_affiliation":"","msr_published_date":"2010-11-01","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"206913","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","title":"shuttledemo-gis10.pdf","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/shuttledemo-gis10.pdf","id":206913,"label_id":0}],"msr_related_uploader":"","msr_attachments":[{"id":206913,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/shuttledemo-gis10.pdf"}],"msr-author-ordering":[{"type":"user_nicename","value":"badrishc","user_id":31166,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=badrishc"},{"type":"text","value":"Mohamed Ali","user_id":0,"rest_url":false},{"type":"text","value":"Balan S. Raman","user_id":0,"rest_url":false},{"type":"text","value":"Ed Katibah","user_id":0,"rest_url":false}],"msr_impact_theme":[],"msr_research_lab":[],"msr_event":[],"msr_group":[957177],"msr_project":[170875],"publication":[],"video":[],"download":[],"msr_publication_type":"inproceedings","related_content":{"projects":[{"ID":170875,"post_title":"Streams","post_name":"streams","post_type":"msr-project","post_date":"2011-11-21 13:31:30","post_modified":"2017-06-19 10:26:41","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/streams\/","post_excerpt":"In the streams research project, we propose novel architectures, efficient processing techniques, models, and applications to support time-oriented queries over real-time and offline data streams. Our current focus in the project centers around Trill, a high-performance streaming analytics engine that is now used across Microsoft. 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