PPT-Minimal MapReduce Algorithms

Author : faustina-dinatale | Published Date : 2017-05-18

By Yufei Tao Wenqing Lin Xiaokui Xia Edited by Tuval Rotem First topic What is MapReduce But first Regular day at work Youre doing your regular work while suddenly

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Minimal MapReduce Algorithms: Transcript

By Yufei Tao Wenqing Lin Xiaokui Xia Edited by Tuval Rotem First topic What is MapReduce But first Regular day at work Youre doing your regular work while suddenly your boss . Algorithms for Updating Minimal Spanning Trees* FRANCIS CHIN Department of Computing Science, University of Alberta, Canada T6G 2Hl AND DAVID HOUCK Department of Mathematics, University Mar and . Hadoop. Debapriyo Majumdar. Data Mining – Fall 2014. Indian Statistical Institute Kolkata. November 10, 2014. Let’s keep the intro short. Modern data mining: process immense amount of data quickly. , Collective Communication, and Services. Oral Exam, . Bingjing. Zhang. Outline. MapReduce. MapReduce. Frameworks. Iterative . MapReduce. Frameworks. Frameworks Based on . MapReduce. and Alternatives. Simplified Data Processing on Large . Clusters. by Jeffrey Dean and Sanjay . Ghemawa. Presented by Jon Logan. Outline. Problem Statement / Motivation. An Example Program. MapReduce. . vs. Hadoop. GFS / HDFS. Parallel Computing. MapReduce. Examples. Parallel Efficiency. Assignment. Parallel Computing. Parallel efficiency with . p. processors. Traditional parallel computing:. focus on compute intensive tasks. Erkki. . Makinen. University of . Tempere. . Tempere. , Finland. Margareta Ackerman. University of Waterloo. Waterloo, ON. What kind of words does this NFA accepts?. 0. 1. 1. 1. 0. 1. 0. A. B. C. D. Erkki. . Makinen. University of . Tempere. . Tempere. , Finland. Margareta Ackerman. University of Waterloo. Waterloo, ON. What kind of words does this NFA accepts?. 0. 1. 1. 1. 0. 1. 0. A. B. C. D. http. ://. SALSA. hpc.indiana.edu. . School of Informatics and Computing. Indiana University. Indexed. HBase. Analysis Tools for . Data Enabled Science. Summer Workshop on Algorithms and . Cyberinfrastructure. Monotone . Local. . Search. Fedor. . Fomin. , Serge . Gaspers. , . Daniel . Lokshtanov. , . Saket . Saurabh. solution. Subset. Problems. Input: . Universe. . U. (. of. . size. . n. ), . implicit. ”. Cathy O’Neil & Rachel . Schutt. , 2013. R & Hadoop. Compute squares. 2. R. # create a list of 10 integers. ints. <- 1:10. # equivalent to . ints. <- c(1,2,3,4,5,6,7,8,9,10). # compute the squares. Jimmy Lin. The iSchool. University of Maryland. Monday, March 30, 2009. This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States. See http://creativecommons.org/licenses/by-nc-sa/3.0/us/ for details. MapReduce. Fei. . Teng. Doga Tuncay. Outline. Goal. Genetic Algorithm. Why . MapReduce. . Hadoop. /Twister. Performance Issues. References. Goal. Implement a genetic algorithm on Twister to prove that Twister is an ideal . Source. MapReduce. : Simplified Data Processing in Large Clusters. . Jefferey. Dean and Sanjay . Ghemawat. . OSDI 2004. Example Scenario. 3. Genome data from roughly . one million users. 125 MB of data per user. MapReduce. Architecture. MapReduce. Internals. MapReduce. Examples. JobTracker. Interface. MapReduce. : A Real World Analogy. Coins Deposit. ?. MapReduce. : A Real World Analogy. Coins Deposit. Coins Counting Machine.

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