PPT-Materialized View Selection for XQuery Workloads

Asterios Katsifodimos 1 Ioana Manolescu 1 amp Vasilis Vassalos 2 1 Inria Saclay amp Université Paris Sud 2 Athens University of Economics and Business Athens University of . 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 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.. 2014, . Fall. Pusan National University. Ki-. Joune. Li. These slides are made from the materials that Prof. Jeffrey D. Ullman distributes via his course web page (. http://infolab.stanford.edu/~ullman/dscb/gslides.html. Sanjay . Agrawal. . Surajit. . Chaudhuri. . Vivek. . Narasayya. Hasan Kumar Reddy A (09005065). 1. Outline. Motivation. Introduction. Architecture. Algorithm. Candidate Selection. Configuration Enumeration. 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 . 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+). Trent Steele. Southeast Region. Sr. Systems Engineer. (336) 482-5725. trent.steele@tintri.com. www.thevbox.net. . @. thevbox. . The Business. IT…What are we here for? . What drives your business? . :. a Big Data Benchmark Suite from Internet Services. Lei . Wang. , . Jianfeng . Zhan. , . . Chunjie. . Luo, . Yuqing Zhu, . . Qiang. Yang, . Yongqiang He, . . Wanling. . Gao. , . Zhen Jia. , . Cloud Migration Summary of Findings. Please contact us at. Eplexity– We can put together. this document with your team . . sales@eplexity.com. Current state analysis. The value of migrating to Public Cloud (AWS). Sanjay . Agrawal. . Surajit. . Chaudhuri. . Vivek. . Narasayya. Hasan Kumar Reddy A (09005065). 1. Outline. Motivation. Introduction. Architecture. Algorithm. Candidate Selection. Configuration Enumeration. Ashok Padmaraju. Course. : Topics on Software Engineering. Instructor: . Dr. Sergiu Dascalu. Introduction to Big Data workflows. “Big Data” is a broad term for datasets that are so large or complex..