{"id":151573,"date":"2000-09-01T00:00:00","date_gmt":"2000-09-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/automated-selection-of-materialized-views-and-indexes-for-sql-databases\/"},"modified":"2018-10-16T21:11:07","modified_gmt":"2018-10-17T04:11:07","slug":"automated-selection-of-materialized-views-and-indexes-for-sql-databases","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/automated-selection-of-materialized-views-and-indexes-for-sql-databases\/","title":{"rendered":"Automated Selection of Materialized Views and Indexes for SQL Databases."},"content":{"rendered":"

Automatically selecting an appropriate set of
\nmaterialized views and indexes for SQL
\ndatabases is a non-trivial task. A judicious choice
\nmust be cost-driven and influenced by the
\nworkload experienced by the system. Although
\nthere has been work in materialized view
\nselection in the context of multidimensional
\n(OLAP) databases, no past work has looked at
\nthe problem of building an industry-strength tool
\nfor automated selection of materialized views
\nand indexes for SQL workloads. In this paper,
\nwe present an end-to-end solution to the problem
\nof selecting materialized views and indexes. We
\ndescribe results of extensive experimental
\nevaluation that demonstrate the effectiveness of
\nour techniques. Our solution is implemented as
\npart of a tuning wizard that ships with Microsoft
\nSQL Server 2000.<\/p>\n","protected":false},"excerpt":{"rendered":"

Automatically selecting an appropriate set of materialized views and indexes for SQL databases is a non-trivial task. A judicious choice must be cost-driven and influenced by the workload experienced by the system. Although there has been work in materialized view selection in the context of multidimensional (OLAP) databases, no past work has looked at the […]<\/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":[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-151573","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-data-platform-analytics","msr-locale-en_us"],"msr_publishername":"Very Large Data Bases Endowment Inc.","msr_edition":"VLDB","msr_affiliation":"","msr_published_date":"2000-09-01","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"VLDB","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":"","msr_publicationurl":"http:\/\/www.vldb.org\/conf\/2000\/P496.pdf","msr_doi":"","msr_publication_uploader":[{"type":"url","title":"http:\/\/www.vldb.org\/conf\/2000\/P496.pdf","viewUrl":false,"id":false,"label_id":0}],"msr_related_uploader":"","msr_attachments":[{"id":0,"url":"http:\/\/www.vldb.org\/conf\/2000\/P496.pdf"}],"msr-author-ordering":[{"type":"user_nicename","value":"sagrawal","user_id":33490,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=sagrawal"},{"type":"user_nicename","value":"surajitc","user_id":33764,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=surajitc"},{"type":"user_nicename","value":"viveknar","user_id":34602,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=viveknar"}],"msr_impact_theme":[],"msr_research_lab":[],"msr_event":[],"msr_group":[957177],"msr_project":[169456],"publication":[],"video":[],"download":[],"msr_publication_type":"inproceedings","related_content":{"projects":[{"ID":169456,"post_title":"AutoAdmin","post_name":"autoadmin","post_type":"msr-project","post_date":"2001-11-02 14:41:11","post_modified":"2019-02-05 12:04:17","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/autoadmin\/","post_excerpt":"Database management systems provide functionality that is central to developing business applications. 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