PPT-Lecture 2 MapReduce in brief

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Source MapReduce Simplified Data Processing in Large Clusters Jefferey Dean and Sanjay Ghemawat OSDI 2004 Example Scenario 3 Genome data from roughly one million

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Lecture 2 MapReduce in brief: Transcript

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. ：. R. eusing . results . of . mapreduce. . jobs. Jun Fan. Outline. Introduction to . MapReduce. MapReduce. and its implementations such as . Hadoop. are common in . Facebook. , Yahoo, and Google as well as smaller companies. : Simplified Data Processing on Large Clusters. Jeffrey Dean & . Sanjay . Ghemawat. Appeared in:. OSDI '04: Sixth Symposium on Operating System Design and Implementation, San Francisco, CA, December, 2004. . Tsung. -Wei Huang. and Martin D. F. Wong. Department of Electrical and Computer Engineering (ECE). University of Illinois at Urbana-Champaign (UIUC), IL, USA. 2015 ACM International Symposium on Physical Design (ISPD). 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. 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. Paradigms Activity at Data Intensive Workshop. . Shantenu Jha represented by . Geoffrey Fox. gcf@indiana.edu. . . http://www.infomall.org. . http://www.futuregrid.org. . . http://salsahpc.indiana.edu. Parallel Computing. MapReduce. Examples. Parallel Efficiency. Assignment. Parallel Computing. Parallel efficiency with . p. processors. Traditional parallel computing:. focus on compute intensive tasks. Jaliya Ekanayake. Community Grids Laboratory, . Digital Science Center. Pervasive Technology Institute. Indiana University. HPDC – 2010 MAPREDUCE’10 Workshop, Chicago, 06/22/2010. Acknowledgements to:. , the Big Data Workhorse. Vyassa Baratham, Stony Brook University. April 20, 2013, 1:05-2:05pm. cSplash. 2013. Use several computers to process large amounts of data. Often significant . distribution overhead. Efficient and scalable architectures to perform pleasingly parallel, MapReduce and iterative data intensive computations on cloud environments. Thilina. . Gunarathne. (tgunarat@indiana.edu). Advisor : . . Shantenu Jha represented by . Geoffrey Fox. gcf@indiana.edu. . . http://www.infomall.org. . http://www.futuregrid.org. . . http://salsahpc.indiana.edu/. . Director, Digital Science Center, Pervasive Technology Institute. : Stopping Group Attacks by Spotting Lockstep Behavior in Social. . Networks (WWW2013). BEUTEL, ALEX, WANHONG XU, VENKATESAN GURUSWAMI, . CHRISTOPHER PALOW, AND CHRISTOS FALOUTSOS.. GROUP 20. Outline. Yasin N. Silva and Jason Reed. Arizona State University. 1. This work is licensed under a Creative Commons Attribution-. NonCommercial. -. ShareAlike. 4.0 International License. See http://creativecommons.org/licenses/by-nc-sa/4.0/ for details.. 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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