{"id":152531,"date":"2001-05-01T00:00:00","date_gmt":"2001-05-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/a-robust-optimization-based-approach-for-approximate-answering-of-aggregate-queries\/"},"modified":"2018-10-16T20:01:46","modified_gmt":"2018-10-17T03:01:46","slug":"a-robust-optimization-based-approach-for-approximate-answering-of-aggregate-queries","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/a-robust-optimization-based-approach-for-approximate-answering-of-aggregate-queries\/","title":{"rendered":"A Robust, Optimization-Based Approach for Approximate Answering of Aggregate Queries"},"content":{"rendered":"<div class=\"asset-content\">\n<p>The ability to approximately answer aggregation queries accurately and efficiently is of great benefit for decision support and data mining tools. In contrast to previous sampling-based studies, we treat the problem as an optimization problem whose goal is to minimize the error in answering queries in the given workload. A key novelty of our approach is that we can tailor the choice of samples to be robust even for workloads that are \u201csimilar\u201d but not necessarily identical to the given workload. Finally, our techniques recognize the importance of taking into account the variance in the data distribution in a principled manner. We show how our solution can be implemented on a database system, and present results of extensive experiments on Microsoft SQL Server 2000 that demonstrate the superior quality of our method compared to previous work.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>The ability to approximately answer aggregation queries accurately and efficiently is of great benefit for decision support and data mining tools. In contrast to previous sampling-based studies, we treat the problem as an optimization problem whose goal is to minimize the error in answering queries in the given workload. A key novelty of our approach [&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":"Association for Computing Machinery, Inc.","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"SIGMOD","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"MSR-TR-2001-37","msr_organization":"","msr_pages_string":"45","msr_page_range_start":"45","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"Copyright \u00a9 2007 by the Association for Computing Machinery, Inc. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and\/or a fee. Request permissions from Publications Dept, ACM Inc., fax +1 (212) 869-0481, or permissions@acm.org. The definitive version of this paper can be found at ACM's Digital Library --http:\/\/www.acm.org\/dl\/.","msr_conference_name":"","msr_doi":"","msr_arxiv_id":"","msr_s2_paper_id":"","msr_mag_id":"","msr_pubmed_id":"","msr_other_authors":"Gautam Das","msr_other_contributors":"","msr_speaker":"","msr_award":"","msr_affiliation":"","msr_institution":"Microsoft Research","msr_host":"","msr_version":"","msr_duration":"","msr_original_fields_of_study":"","msr_release_tracker_id":"","msr_s2_match_type":"","msr_citation_count_updated":"","msr_published_date":"2001-05-01","msr_highlight_text":"","msr_notes":"Technical Report UU-CS-2001-35, Departement of Computer Science, Universiteit Utrecht","msr_longbiography":"","msr_publicationurl":"","msr_external_url":"","msr_secondary_video_url":"","msr_conference_url":"","msr_journal_url":"","msr_s2_pdf_url":"","msr_year":2001,"msr_citation_count":0,"msr_influential_citations":0,"msr_reference_count":0,"msr_s2_match_confidence":0,"msr_microsoftintellectualproperty":true,"msr_s2_open_access":false,"msr_s2_author_ids":[],"msr_pub_ids":[],"msr_hide_image_in_river":0,"footnotes":""},"msr-research-highlight":[],"research-area":[13555],"msr-publication-type":[193718],"msr-publisher":[],"msr-focus-area":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-152531","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-search-information-retrieval","msr-locale-en_us"],"msr_publishername":"Association for Computing Machinery, Inc.","msr_edition":"SIGMOD","msr_affiliation":"","msr_published_date":"2001-05-01","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"45","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"MSR-TR-2001-37","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"Technical Report UU-CS-2001-35, Departement of Computer Science, Universiteit Utrecht","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":"210743","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","title":"tr-2001-37.doc","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/tr-2001-37.doc","id":210743,"label_id":0},{"type":"file","title":"SIG01-AQP.pdf","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/SIG01-AQP.pdf","id":210742,"label_id":0}],"msr_related_uploader":"","msr_citation_count":0,"msr_citation_count_updated":"","msr_s2_paper_id":"","msr_influential_citations":0,"msr_reference_count":0,"msr_arxiv_id":"","msr_s2_author_ids":[],"msr_s2_open_access":false,"msr_s2_pdf_url":null,"msr_attachments":[{"id":210743,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/tr-2001-37.doc"},{"id":210742,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/SIG01-AQP.pdf"}],"msr-author-ordering":[{"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":"text","value":"Gautam Das","user_id":0,"rest_url":false},{"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":[967236],"publication":[],"video":[],"msr-tool":[],"msr_publication_type":"techreport","related_content":{"projects":[{"ID":967236,"post_title":"Query Optimization for Database Systems","post_name":"query-optimization-for-database-systems","post_type":"msr-project","post_date":"2023-12-11 15:19:29","post_modified":"2023-12-11 15:19:32","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/query-optimization-for-database-systems\/","post_excerpt":"The query optimizer is a crucial component in a relational database system and is responsible for finding a good execution plan for a SQL query. For cloud database service providers, the importance of query optimization is amplified due to the scale (e.g., millions of databases hosted) and variety of different workloads for which the query optimizer is expected to work well \"out-of-the-box\". 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