{"id":160249,"date":"2010-09-28T00:00:00","date_gmt":"2010-09-28T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/data-stream-management-systems-for-computational-finance\/"},"modified":"2018-10-16T20:07:36","modified_gmt":"2018-10-17T03:07:36","slug":"data-stream-management-systems-for-computational-finance","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/data-stream-management-systems-for-computational-finance\/","title":{"rendered":"Data Stream Management Systems for Computational Finance"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Computational finance leverages computer technologies to build models from large amounts of data to extract insight. With the increasing amounts of data available to build and refine models, the difference between seizing an opportunity and missing one is the latency involved in processing this data. This article motivates the need for a specialized platform \u2013 called a Data Stream Management System (DSMS) \u2013 to perform complex processing with minimal latency over large volumes of temporal data. Using a running example from the financial domain, it describes the salient features of these platforms and the advantages they offer.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Computational finance leverages computer technologies to build models from large amounts of data to extract insight. With the increasing amounts of data available to build and refine models, the difference between seizing an opportunity and missing one is the latency involved in processing this data. This article motivates the need for a specialized platform \u2013 [&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":[13563],"msr-publication-type":[193718],"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-160249","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-data-platform-analytics","msr-locale-en_us"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2010-09-28","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-TR-2010-130","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":"206958","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","title":"streams-finance.pdf","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/streams-finance.pdf","id":206958,"label_id":0}],"msr_related_uploader":"","msr_attachments":[{"id":206958,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/streams-finance.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":"user_nicename","value":"jongold","user_id":32389,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=jongold"},{"type":"text","value":"Beysim Sezgin","user_id":0,"rest_url":false},{"type":"text","value":"Balan S. Raman","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":"techreport","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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