PPT-Materialized Views in Probabilistic Databases

Author : conchita-marotz | Published Date : 2016-12-11

for Information Exchange and Query Optimization Christopher Re and Dan Suciu University of Washington 1 Motivating Example Optimization materialized but imprecise

Presentation Embed Code

Download Presentation

Download Presentation The PPT/PDF document "Materialized Views in Probabilistic Data..." is the property of its rightful owner. Permission is granted to download and print the materials on this website for personal, non-commercial use only, and to display it on your personal computer provided you do not modify the materials and that you retain all copyright notices contained in the materials. By downloading content from our website, you accept the terms of this agreement.

Materialized Views in Probabilistic Databases: Transcript

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. 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.. Bhargav Kanagal & Amol Deshpande. University of Maryland. Introduction. Correlated Probabilistic data generated in many scenarios. Data Integration [AFM06]: Conflicting information best captured using “mutual exclusivity”. 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. from. IE Models . Olga . Mykytiuk. , 21 July 2011. M.Theobald. Outline . Motivation for probabilistic databases. Model for automatic extraction. Different representation . One-row model. Multi-row model . Indranil Gupta. Associate Professor. Dept. of Computer Science, University of Illinois at Urbana-Champaign. Joint work with . Muntasir. . Raihan. . Rahman. , Lewis Tseng, Son Nguyen, . Nitin. . Vaidya. 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. Nathan Clement. Computational Sciences Laboratory. Brigham Young University. Provo, Utah, USA. Next-Generation Sequencing. Problem Statement . Map next-generation sequence reads with variable nucleotide confidence to .

Download Document

Here is the link to download the presentation.
"Materialized Views in Probabilistic Databases"The content belongs to its owner. You may download and print it for personal use, without modification, and keep all copyright notices. By downloading, you agree to these terms.