'Large scale data' presentation slideshows

Thilina Gunarathne (tgunarat@indiana.edu) Advisor : Prof.Geoffrey Fox (gcf@indiana.edu)

Scalable Parallel Computing on Clouds : Efficient and scalable architectures to perform pleasingly parallel, MapReduce and iterative data intensive computations on cloud environments. Thilina Gunarathne (tgunarat@indiana.edu) Advisor : Prof.Geoffrey Fox (gcf@indiana.edu)

892 views • 75 slides

Welcome to Cloud Computing. The Internet of People and Things. An Introduction to Cloud Computing : The Internet . Pr

Welcome to Cloud Computing. The Internet of People and Things. An Introduction to Cloud Computing : The Internet . Presenter: Roy McCollum

617 views • 42 slides

iRODS and Large-Scale Data Management

iRODS and Large-Scale Data Management. Leesa Brieger. Renaissance Computing Institute ( RENCI). A research unit of UNC Chapel Hill Current Director: Stan Ahalt, formerly from the Ohio Supercomputer Center State-supported Governed by the Triangle universities: UNC Chapel Hill

497 views • 37 slides

Max-Margin Additive Classifiers for Detection

Max-Margin Additive Classifiers for Detection. Subhransu Maji & Alexander Berg University of California at Berkeley Columbia University ICCV 2009, Kyoto, Japan. Accuracy vs. Evaluation Time for SVM Classifiers. Non-linear Kernel. Evaluation time. Linear Kernel. Accuracy.

536 views • 36 slides

Scalable Benchmarks and Kernels for Data Mining and Analytics

Scalable Benchmarks and Kernels for Data Mining and Analytics. Vipin Kumar University of Minnesota kumar@cs.umn.edu www.cs.umn.edu/~kumar Joint work with Alok Choudhary and Gokhan Memik (Northwestern) and Michael Steinbach (University of Minnesota) Research funded by NSF.

503 views • 31 slides

Revolutionizing the Journal through Big Data Computational Research

379 views • 28 slides

Homework 4

Homework 4. Code for word count http://grepcode.com/file/repository.cloudera.com/content/repositories/releases/com.cloudera.hadoop/hadoop-examples/0.20.2-320/org/apache/hadoop/examples/WordCount.java# WordCount. Data Bases in Cloud Environments. Based on: Md . Ashfakul Islam

397 views • 25 slides

Impact of the Tevatron on Technology and Innovation John Womersley Chief Executive, STFC

Impact of the Tevatron on Technology and Innovation John Womersley Chief Executive, STFC 11 June 2012. The Science & Technology Facilities Council. Research in UK universities, RAL and DL. JCMT Telescope. CERN: LHC. ESA: Top Sat. ILL and ESRF. ESO : Alma Array.

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Applications of Data Mining for Predicting Material Properties

Applications of Data Mining for Predicting Material Properties. Vipin Kumar University of Minnesota kumar@cs.umn.edu www.cs.umn.edu/~kumar Research supported by NSF, ARL. Why Data Mining? Commercial Viewpoint. Lots of data is being collected and warehoused Web data

428 views • 24 slides

Data-Intensive Computing

Data-Intensive Computing. Data-intensive computing focuses on class of applications that deal with a large amount of data.

370 views • 20 slides

Term Paper

Term Paper. By Narendra Muppavarapu Venkatasai pulluri. Data Reduction in Very Large Spatio -Temporal Datasets Exploratory spatio -temporal visualization: an analytical review Spatio temporal symbolization of multidimensional time series. Outline. Motivation Introduction

471 views • 20 slides

Nutrient Management Planning

Nutrient Management Planning. Will these be mandated in your state? An emerging national issue is how to account for agricultural non-point source pollution. If your state has passed legislation concerning Total Maximum Daily Loads (TMDLs) you should plan for NMP mandates?.

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Research to Reality William Ribarsky Remco Chang University of North Carolina at Charlotte

Research to Reality William Ribarsky Remco Chang University of North Carolina at Charlotte. Some Tools in Use that Were Developed at the SouthEast RVAC. The SRVAC developed several tools and capabilities for complex problem-solving and analysis, typically with large-scale data.

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Paper Analysis

Paper Analysis. Presented by Team 8. Kartik Sunku Manasa Valli Mantripragada Surya Bheema raju Pasumarthi. Outline. Motivation Problem Definition Different Approaches Used Description of Papers Comparison Conclusion References. Motivation.

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Virtualization

Virtualization. B. Ramamurthy. References. Practical Virtualization Solutions: Virtualization from the Trenches by K. Hess and A. Newman, Prentice-Hall Inc., 2009, ISBN 13: 978-0-13-714297-2.

354 views • 15 slides

Predicting Product Adoption in Large-Scale Social Networks

Predicting Product Adoption in Large-Scale Social Networks . Offensive: Hao Chen. General Problems:. Can IM system be a good social network example? By only study one product sample, can we generalize the universe of all niche products. General Problems:.

324 views • 14 slides

Emerging Platform#6: Cloud Computing

Emerging Platform#6: Cloud Computing. B. Ramamurthy. Outline of the talk. Golden Era in Computing Data and Computing challenges Cloud Computing Popular Cloud Providers Our experience with Cloud hosting Summary References Questions and Answers. A Golden Era in Computing.

309 views • 13 slides

No Child Left Behind

No Child Left Behind. Cristina Marzello, Lindsey Olechna . Briann Lafty , Megan Wallace, Mary Byrne . Group Activity. Break into 3 groups Each group will be given an article to analyze Group test Review results. Pros of No Child Left Behind.

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Cyborgs , Androids, and Beyond

210 views • 7 slides

idm04-xmdv

Grant Number: IIS- 0119276 Institution of PI: WPI PIs: Matthew O. Ward and Elke A. Rundensteiner Title: Order, Spacing, and Clustering in Visual Exploration of Large Scale Data.

84 views • 1 slides