PPT-Automated Selection of Materialized Views and Indexes for S

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Sanjay Agrawal Surajit Chaudhuri Vivek Narasayya Hasan Kumar Reddy A 09005065 1 Outline Motivation Introduction Architecture Algorithm Candidate Selection Configuration

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Automated Selection of Materialized Views and Indexes for S: Transcript

Sanjay Agrawal Surajit Chaudhuri Vivek Narasayya Hasan Kumar Reddy A 09005065 1 Outline Motivation Introduction Architecture Algorithm Candidate Selection Configuration Enumeration. com surajitcmicrosoftcom viveknarmicrosoftcom Abstract Automatically selecting an appropriate set of materialized views and indexes for SQL databases is a nontrivial task A judicious choice must be costdriven and influenced by the workload experience 11 of Canada Perrmsslon to copy wIthout fee all or part of this matenal 1s granted provided that the copies are not made or chstrlbuted for dmxt commercial advantage the ACM copyrIght notxe and the title of the pubhcauon and its date appear and notxe V. iews for Replication. COUG Presentation, Feb 20, 2014. Jane Lamont, jlamont@geologic.com. Materialized Views 101. Types and uses of materialized views. Basic setup of materialized views. Common types of refreshes used on materialized views.. Todd J. Green. Zachary G. Ives Val Tannen. University of Pennsylvania. . March 24, 2009. @ ICDT 09, Saint Petersburg. Change is a Constant in Data Management. Databases are highly . dynamic. ; many kinds of . Materialized Instances After Changes to Mappings and Data. Todd J. Green. Zachary G. Ives. ICDE ’12 Washington, DC April 2, 2012. &. Change is a Constant in Data Management. Databases are highly . Asterios Katsifodimos. 1. , Ioana Manolescu. 1. & . Vasilis. . Vassalos. 2. 1. Inria . Saclay. & . Université. Paris-. Sud. , . 2. Athens . University of Economics and Business. Athens University of . The unrelieved weight of a heavy motionless mass.. Revenue-producing part of a cargo OR total weight of crew, instruments or equipment carried by an aircraft . Principles of Handling: Selection Process and Techniques. Sanjay . Agrawal. . Surajit. . Chaudhuri. . Vivek. . Narasayya. Hasan Kumar Reddy A (09005065). 1. Outline. Motivation. Introduction. Architecture. Algorithm. Candidate Selection. Configuration Enumeration. for Information Exchange and Query Optimization. Christopher Re and Dan . Suciu. University of Washington. 1. Motivating Example: Optimization. materialized – but imprecise – view. Similarity between users (8M+). Delivering a Full Market Perspective. 2 For . Professional Investor Use Only. Not for Public Distribution. Please see DISCLOSURES at the back for important information regarding this presentation.. Indexing Basics in SQL Server. Mindy Curnutt. TMW Systems, Inc.. VP, Information Management. SQL Server MVP. Twitter: @. sqlgirl. About Me. [ . 2. ]. Wife & Mother. Busy-Bee. Musician . Soap Making . Asterios Katsifodimos. 1. , Ioana Manolescu. 1. & . Vasilis. . Vassalos. 2. 1. Inria . Saclay. & . Université. Paris-. Sud. , . 2. Athens . University of Economics and Business. Athens University of . Shah (. nimesh.s. ). . , . Amit. . Bhawnani. (. amit.b. ). Outline. Why Indexes . ?. Demo. Types of Single-level Ordered Indexes. Primary Indexes. Clustering Indexes. Secondary Indexes. Multilevel Indexes. Sanjay . Agrawal. . Surajit. . Chaudhuri. . Vivek. . Narasayya. Hasan Kumar Reddy A (09005065). 1. Outline. Motivation. Introduction. Architecture. Algorithm. Candidate Selection. Configuration Enumeration.

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