'Scale data' diaporamas de présentation

IS 4800 Empirical Research Methods for Information Science Class Notes Feb 3, 2012

IS 4800 Empirical Research Methods for Information Science Class Notes Feb 3, 2012. Instructor: Prof. Carole Hafner, 446 WVH hafner@ccs.neu.edu Tel: 617-373-5116 Course Web site: www.ccs.neu.edu/course/is4800sp12/. Outline. First exam postponed until Friday Feb. 10

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Building Large-Scale Data-Centric Applications with Silverlight

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The future of the British RAE The REF (Research Excellence Framework) Jonathan Adams

The future of the British RAE The REF (Research Excellence Framework) Jonathan Adams. Research Assessment Exercise - timeline. 1980s - policy on concentration and selectivity 1986 - 1 st Research Selectivity Exercise 1989 - modified and formalised as the RAE

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CS 4700 / CS 5700 Network Fundamentals

Lecture 17 : Network Modeling (Not Everyone has a Datacenter). CS 4700 / CS 5700 Network Fundamentals. Wide-Area Network Research. Most research now focused on large-scale systems Challenges: testing and evaluation How to perform wide-area tests in a repeatable, reliable manner

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FIELD DATA COLLECTION and ANALYSIS Dr. Richard Gilbert RESEARCH TRAINING WORKSHOP

FIELD DATA COLLECTION and ANALYSIS Dr. Richard Gilbert RESEARCH TRAINING WORKSHOP. Things to Consider in Planning Data Analysis Your Research Proposal a) What were your original research objectives? b) Are these objectives still appropriate, or do you need to modify them?

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University of Washington Computer Science & Engineering Curriculum Revision: Why, What, How*

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Science and Technology Infusion Plan for Severe Weather

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Getting Started with IBM SPSS Stats 20

Getting Started with IBM SPSS Stats 20. Presented by: Rosey Zackula Center for Biostatistical Collaboration Office of Research Tuesday, October 23, 2012. http://www.rdinfo.org.uk/flowchart/Flowchart.html. COURSE OBJECTIVES. Become familiar with the SPSS system

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Identifying Model Structure and Scale Dependencies in Complex Sytems

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Spectral Feature Selection for Handling Very Large Scale Problems

Spectral Feature Selection for Handling Very Large Scale Problems. Zheng ( Alan ) Zhao. SAS Institute. Motivation. Petabyte datasets are rapidly becoming normal in data mining applications Google: processing 20 PB data per day (2008)

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A Practical Guide to SVM

A Practical Guide to SVM. Yihua Liao Dept. of Computer Science 2/3/03. Outline. Support vector machine basics GIST LIBSVM (SVMLight). Classification problems. Given: n training pairs, (<x i >, y i ), where

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Building Efficient Time Series Similarity Search Operator

Building Efficient Time Series Similarity Search Operator. Mijung Kim Summer Internship 2013 at HP Labs. Overview. The internship project is a part of a project that: builds a scalable analytics framework and c onstructs a set of analytic operators within the framework

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Development of Java Servlet for Graphing SQL-Data

Development of Java Servlet for Graphing SQL-Data. CPET 499 Project Presentation By Ian Gass. Overview. Introduction Specifications Design Analysis Class Definition Results. Introduction. Add-on to senior design project Senior design project description ASP based data management

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Correlational Research Strategy

Correlational Research Strategy. Goal . Examine and describe associations and relationships between variables No attempt at explanation No true IV; measurements taken on DV only No manipulation No control. Measurement on two variables. Identify patterns / consistency between two variables

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Evaluation Strategy

TT. Cap/Lim By Mission + Unks. Element Level Cap/Lim E, s, s, + Unks. FCS FoS/SoS Cap/Lim E, s, s, + Unks. OT. Data. Core Compl Systems. Joint/ Compl Systems. Sensitivity Analyses By Thread. M&S. M&S. M&S. Cap/Lim @ SoS, S, S, + Unks. Cap/Lim @ Mission,

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Combining Statistics, Sampling, and Indexing to Quantitatively Scale Massive Scientific Data

Combining Statistics, Sampling, and Indexing to Quantitatively Scale Massive Scientific Data. Science Problem: Climate and cosmological analysis and exploration via queries on their massive data sets may still result in a massive amount of data for the scientist to comb through and/or transfer.

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