{"id":154456,"date":"2007-01-01T00:00:00","date_gmt":"2007-01-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/physical-design-refinement-the-merge-reduce-approach-2\/"},"modified":"2024-02-28T13:14:15","modified_gmt":"2024-02-28T21:14:15","slug":"physical-design-refinement-the-merge-reduce-approach-2","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/physical-design-refinement-the-merge-reduce-approach-2\/","title":{"rendered":"Physical design refinement: The merge-reduce approach"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Physical database design tools rely on a DBA-provided workload to pick an \u201coptimal\u201d set of indexes and materialized views. Such tools allow either creating a new such configuration or adding new structures to existing ones. However, these tools do not provide adequate support for incremental and flexible refinement of existing physical structures. Although such refinements are often very valuable for DBAs, a completely manual approach to refinement can lead to infeasible solutions (e.g., excessive use of space). In this paper, we focus on the important problem of physical design refinement and propose a transformational architecture that is based upon two novel primitive operations, called merging and reduction. These operators help refine a configuration, treating indexes and materialized views in a unified way, as well as succinctly explain the refinement process to DBAs.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Physical database design tools rely on a DBA-provided workload to pick an \u201coptimal\u201d set of indexes and materialized views. Such tools allow either creating a new such configuration or adding new structures to existing ones. However, these tools do not provide adequate support for incremental and flexible refinement of existing physical structures. Although such refinements [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"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-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-154456","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-data-platform-analytics","msr-locale-en_us"],"msr_publishername":"Association for Computing Machinery, 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