'Map function' presentation slideshows

Cloud Computing Systems

Cloud Computing Systems. Lin Gu. Hong Kong University of Science and Technology. Sept. 14, 2011. How to effectively compute in a datacenter?. Is MapReduce the best answer to computation in the cloud? What is the limitation of MapReduce?

758 views • 62 slides

MapReduce Theory, Implementation and Algorithms

828 views • 60 slides

Parallel Computing and Programming Enviroments

Distributed and Cloud Computing K. Hwang, G. Fox and J. Dongarra Chapter 6: Cloud Programming and Software Environments Part 1 Adapted from Kai Hwang, University of Southern California with additions from Matei Zaharia , EECS, UC Berkeley November 25, 2012.

903 views • 57 slides

.NET and MongoDB

Building Applications with NoRM and MongoDB Fairfield/Westchester Code Camp 2010 2010-11-06 John C. Zablocki Development Lead, MagazineRadar Adjunct, Fairfield University. .NET and MongoDB. Agenda. What is NoSQL? NoSQL Databases Introducing MongoDB .NET Drivers for MongoDB

709 views • 52 slides

CouchDB

CouchDB. - Sai Divya Panditi - Priyanka Yechuri. Overview. Introduction SQL vs CouchDB CouchDB Features CouchDB Core API Futon Security Application. Overview. Demo Code Advantages DisAdvantages Iris Couch Conclusion References. Introduction. Created By : Damien Katz

2.29k views • 51 slides

Review of Map-Reduce A Common Mistake Size/Communication Trade-Off Specific Tradeoffs

Designing Efficient Map-Reduce Algorithms. Review of Map-Reduce A Common Mistake Size/Communication Trade-Off Specific Tradeoffs. Jeffrey D. Ullman Stanford University. Research Is Joint Work of. Foto Afrati (NTUA) Anish Das Sarma (Google) Semih Salihoglu (Stanford) JU.

491 views • 43 slides

Lecture 10: Linkage Analysis III

Lecture 10: Linkage Analysis III. Date: 9/26/02 Revisit segregation ratio distortion. Haplotype coding Three point analysis Multipoint analysis. Additive Segregation Ratio Distortion. Systematic genotype classification error occurs.

553 views • 41 slides

Data Analysis from Cores to Clouds

Data Analysis from Cores to Clouds. HPC 2008 High Performance Computing and Grids Cetraro Italy July 3 2008 Geoffrey Fox, Seung-Hee Bae, Neil Devadasan, Jaliya Ekanayake , Rajarshi Guha, Marlon Pierce, Shrideep Pallickara , Xiaohong Qiu, David Wild, Huapeng Yuan

435 views • 36 slides

Distributed MapReduce Team B

418 views • 32 slides

Distributed and Parallel Processing Technology Chapter2. MapReduce

Distributed and Parallel Processing Technology Chapter2. MapReduce. Sun Jo. Introduction. MapReduce is a programming model for data processing. Hadoop can run MapReduce programs written in various languages. We shall look at the same program expressed in Java, Ruby, Python, and C++.

425 views • 30 slides

Shahram Ghandeharizadeh Computer Science Department University of Southern California

MapReduce Dean and Ghemawat. MapReduce: Simplified Data Processing on Large Clusters. Communications of the ACM, Vol. 51, No. 1, January 2008. Shahram Ghandeharizadeh Computer Science Department University of Southern California. A Shared-Nothing Framework.

410 views • 29 slides

Chapter 2 - 1

334 views • 27 slides

MapReduce

MapReduce. Simplified Data Processing on Large Clusters Google, Inc. Presented by Prasad Raghavendra. Introduction. Model for processing large data sets. Contains Map and Reduce functions.

492 views • 23 slides

MapReduce , the Big Data Workhorse

MapReduce , the Big Data Workhorse. Vyassa Baratham, Stony Brook University April 20, 2013, 1:05-2:05pm cSplash 2013. Distributed Computing. Use several computers to process large amounts of data Often significant distribution overhead If math helps:

334 views • 21 slides

AN INTRODUCTION TO RECOMBINATION AND LINKAGE ANALYSIS

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Hadoop : The Definitive Guide Chap. 2 MapReduce

Hadoop : The Definitive Guide Chap. 2 MapReduce. Kisung Kim. MapReduce. Programming model for parallel data processing Hadoop can run MapReduce programs written in various languages: e.g . Java, Ruby, Python, C ++ In this chapter Introduce MapReduce programming using a simple example

660 views • 18 slides

CSE 341 Lecture 8

CSE 341 Lecture 8. curried functions Ullman 5.5 slides created by Marty Stepp http://www.cs.washington.edu/341/. Recall: helper ML files. We are starting to accumulate lots of helper code map , filter , reduce , -- , etc. Let's put it into a helper file utility.sml

358 views • 16 slides

??? : ??? ??? : ??? ??? ??? ??? Dept. of Electrical Engineering

?????: P2P Video Streaming Multi-layer Video Distribution and Acquisition in Cloud Computing ?????????. ??? : ??? ??? : ??? ??? ??? ??? Dept. of Electrical Engineering National Sun Yat-Sen University. Peer-to-Peer over HDFS. Torrent Recovery using HDFS.

244 views • 8 slides

CS123 Quiz 2

CS123 Quiz 2. Question 5. HVAC system When t <=74, the fan rate is 2360; When t >= 86, the fan rate is 3870; (a) What is the formula when 74 < t < 86? Multiple ways to solve this problem The easiest way is to use solve function to solve linear equations of 2 unknowns

227 views • 7 slides