PPT-Database and MapReduce

RoadMap Role of relational databases in todays organizations Where does MapReduce fit in MapReduce algorithms for processing relational data How do I perform a join etc Evolving roles of relational databases and MapReduce. 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). Cloud, Part II: Search => Cluster Apps => Scalable Machine Learning. David E. Culler. . CS162 – Operating Systems and Systems Programming. Lecture 40. December 3, . 2014. Proj. : CP 2 . today. IUPUI Computer Science. February 11 2011. Geoffrey Fox. gcf@indiana.edu. . . http://www.infomall.org. . http://www.futuregrid.org. . Director, Digital Science Center, Pervasive Technology Institute. 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:. with MapReduce. Lars Kolb. , Hanna Köpcke, Andreas Thor, Erhard Rahm. Database . Group Leipzig. http://dbs.uni-leipzig.de. Glasgow, . CloudDB. 2011. Identification of semantically equivalent entities. By . Yufei. Tao, . Wenqing. . Lin, . Xiaokui. . Xia. Edited by Tuval Rotem. First topic:. What is MapReduce. But first…. Regular day at work. You’re doing your regular work while suddenly your boss, . : 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. Implications . for . Software Environments.  . eScience. in the Cloud . 2014. Redmond WA. April 30 2014. Geoffrey . Fox . gcf@indiana.edu. . . http://www.infomall.org. School of Informatics and Computing. ”. 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. 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. Used slides from RAD Lab at UC Berkeley about the cloud ( . http://abovetheclouds.cs.berkeley.edu/. ) and slides from Jimmy Lin. ’. s slides (http://www.umiacs.umd.edu/~jimmylin/cloud-2010-Spring/index.html) (licensed under Creation Commons Attribution 3.0 License).