# Knowledge Base Completion via Search-Based Question Answering

Save this PDF as:

Size: px
Start display at page:

## Transcription

### Machine Learning using MapReduce

Machine Learning using MapReduce What is Machine Learning Machine learning is a subfield of artificial intelligence concerned with techniques that allow computers to improve their outputs based on previous

Facebook Friend Suggestion Eytan Daniyalzade and Tim Lipus 1. Introduction Facebook is a social networking website with an open platform that enables developers to extract and utilize user information

### December 4, 2013 MATH 171 BASIC LINEAR ALGEBRA B. KITCHENS

December 4, 2013 MATH 171 BASIC LINEAR ALGEBRA B KITCHENS The equation 1 Lines in two-dimensional space (1) 2x y = 3 describes a line in two-dimensional space The coefficients of x and y in the equation

### 6.2.8 Neural networks for data mining

6.2.8 Neural networks for data mining Walter Kosters 1 In many application areas neural networks are known to be valuable tools. This also holds for data mining. In this chapter we discuss the use of neural

### OPRE 6201 : 2. Simplex Method

OPRE 6201 : 2. Simplex Method 1 The Graphical Method: An Example Consider the following linear program: Max 4x 1 +3x 2 Subject to: 2x 1 +3x 2 6 (1) 3x 1 +2x 2 3 (2) 2x 2 5 (3) 2x 1 +x 2 4 (4) x 1, x 2

### Chapter 6: The Information Function 129. CHAPTER 7 Test Calibration

Chapter 6: The Information Function 129 CHAPTER 7 Test Calibration 130 Chapter 7: Test Calibration CHAPTER 7 Test Calibration For didactic purposes, all of the preceding chapters have assumed that the

### Chapter 6. The stacking ensemble approach

82 This chapter proposes the stacking ensemble approach for combining different data mining classifiers to get better performance. Other combination techniques like voting, bagging etc are also described

### Evaluation in Machine Learning (2) Aims. Introduction

Acknowledgements Evaluation in Machine Learning (2) 15s1: COMP9417 Machine Learning and Data Mining School of Computer Science and Engineering, University of New South Wales Material derived from slides

### Lesson 17: Graphing the Logarithm Function

Lesson 17 Name Date Lesson 17: Graphing the Logarithm Function Exit Ticket Graph the function () = log () without using a calculator, and identify its key features. Lesson 17: Graphing the Logarithm Function

### Categorical Data Visualization and Clustering Using Subjective Factors

Categorical Data Visualization and Clustering Using Subjective Factors Chia-Hui Chang and Zhi-Kai Ding Department of Computer Science and Information Engineering, National Central University, Chung-Li,

### The Scientific Data Mining Process

Chapter 4 The Scientific Data Mining Process When I use a word, Humpty Dumpty said, in rather a scornful tone, it means just what I choose it to mean neither more nor less. Lewis Carroll [87, p. 214] In

### MINITAB ASSISTANT WHITE PAPER

MINITAB ASSISTANT WHITE PAPER This paper explains the research conducted by Minitab statisticians to develop the methods and data checks used in the Assistant in Minitab 17 Statistical Software. One-Way

### Web-Scale Extraction of Structured Data Michael J. Cafarella, Jayant Madhavan & Alon Halevy

The Deep Web: Surfacing Hidden Value Michael K. Bergman Web-Scale Extraction of Structured Data Michael J. Cafarella, Jayant Madhavan & Alon Halevy Presented by Mat Kelly CS895 Web-based Information Retrieval

### Appendix C: Graphs. Vern Lindberg

Vern Lindberg 1 Making Graphs A picture is worth a thousand words. Graphical presentation of data is a vital tool in the sciences and engineering. Good graphs convey a great deal of information and can

### CHAPTER 2 Estimating Probabilities

CHAPTER 2 Estimating Probabilities Machine Learning Copyright c 2016. Tom M. Mitchell. All rights reserved. *DRAFT OF January 24, 2016* *PLEASE DO NOT DISTRIBUTE WITHOUT AUTHOR S PERMISSION* This is a

### AP Physics 1 and 2 Lab Investigations

AP Physics 1 and 2 Lab Investigations Student Guide to Data Analysis New York, NY. College Board, Advanced Placement, Advanced Placement Program, AP, AP Central, and the acorn logo are registered trademarks

### Social Media Mining. Data Mining Essentials

Introduction Data production rate has been increased dramatically (Big Data) and we are able store much more data than before E.g., purchase data, social media data, mobile phone data Businesses and customers

### Experiments in Web Page Classification for Semantic Web

Experiments in Web Page Classification for Semantic Web Asad Satti, Nick Cercone, Vlado Kešelj Faculty of Computer Science, Dalhousie University E-mail: {rashid,nick,vlado}@cs.dal.ca Abstract We address

### Multivariate Analysis of Ecological Data

Multivariate Analysis of Ecological Data MICHAEL GREENACRE Professor of Statistics at the Pompeu Fabra University in Barcelona, Spain RAUL PRIMICERIO Associate Professor of Ecology, Evolutionary Biology

### Deferring Elimination of Design Alternatives in Object- Oriented Methods

Deferring Elimination of Design Alternatives in Object- Oriented Methods Mehmet Aksit and Francesco Marcelloni TRESE project, Department of Computer Science, University of Twente, P.O. Box 217, 7500 AE

### Chapter 21: The Discounted Utility Model

Chapter 21: The Discounted Utility Model 21.1: Introduction This is an important chapter in that it introduces, and explores the implications of, an empirically relevant utility function representing intertemporal

### Using Excel (Microsoft Office 2007 Version) for Graphical Analysis of Data

Using Excel (Microsoft Office 2007 Version) for Graphical Analysis of Data Introduction In several upcoming labs, a primary goal will be to determine the mathematical relationship between two variable

### DYNAMIC QUERY FORMS WITH NoSQL

IMPACT: International Journal of Research in Engineering & Technology (IMPACT: IJRET) ISSN(E): 2321-8843; ISSN(P): 2347-4599 Vol. 2, Issue 7, Jul 2014, 157-162 Impact Journals DYNAMIC QUERY FORMS WITH

### large-scale machine learning revisited Léon Bottou Microsoft Research (NYC)

large-scale machine learning revisited Léon Bottou Microsoft Research (NYC) 1 three frequent ideas in machine learning. independent and identically distributed data This experimental paradigm has driven

### IBM SPSS Direct Marketing 22

IBM SPSS Direct Marketing 22 Note Before using this information and the product it supports, read the information in Notices on page 25. Product Information This edition applies to version 22, release

### Chapter 14: Production Possibility Frontiers

Chapter 14: Production Possibility Frontiers 14.1: Introduction In chapter 8 we considered the allocation of a given amount of goods in society. We saw that the final allocation depends upon the initial

### Data Mining. Nonlinear Classification

Data Mining Unit # 6 Sajjad Haider Fall 2014 1 Nonlinear Classification Classes may not be separable by a linear boundary Suppose we randomly generate a data set as follows: X has range between 0 to 15

### Machine Learning model evaluation. Luigi Cerulo Department of Science and Technology University of Sannio

Machine Learning model evaluation Luigi Cerulo Department of Science and Technology University of Sannio Accuracy To measure classification performance the most intuitive measure of accuracy divides the

### Research Statement Immanuel Trummer www.itrummer.org

Research Statement Immanuel Trummer www.itrummer.org We are collecting data at unprecedented rates. This data contains valuable insights, but we need complex analytics to extract them. My research focuses

### IBM SPSS Direct Marketing 23

IBM SPSS Direct Marketing 23 Note Before using this information and the product it supports, read the information in Notices on page 25. Product Information This edition applies to version 23, release

### Bootstrapping Big Data

Bootstrapping Big Data Ariel Kleiner Ameet Talwalkar Purnamrita Sarkar Michael I. Jordan Computer Science Division University of California, Berkeley {akleiner, ameet, psarkar, jordan}@eecs.berkeley.edu

### ROC Graphs: Notes and Practical Considerations for Data Mining Researchers

ROC Graphs: Notes and Practical Considerations for Data Mining Researchers Tom Fawcett 1 1 Intelligent Enterprise Technologies Laboratory, HP Laboratories Palo Alto Alexandre Savio (GIC) ROC Graphs GIC

### Multi-source hybrid Question Answering system

Multi-source hybrid Question Answering system Seonyeong Park, Hyosup Shim, Sangdo Han, Byungsoo Kim, Gary Geunbae Lee Pohang University of Science and Technology, Pohang, Republic of Korea {sypark322,

### 2. Simple Linear Regression

Research methods - II 3 2. Simple Linear Regression Simple linear regression is a technique in parametric statistics that is commonly used for analyzing mean response of a variable Y which changes according

### BIDM Project. Predicting the contract type for IT/ITES outsourcing contracts

BIDM Project Predicting the contract type for IT/ITES outsourcing contracts N a n d i n i G o v i n d a r a j a n ( 6 1 2 1 0 5 5 6 ) The authors believe that data modelling can be used to predict if an

### Research Report Supporting Sales Coaching November 2015

Research Report Supporting Sales Coaching November 2015 Research underwriters Copyright 2015 by the Sales Management Association. All rights reserved. Authors Robert J. Kelly Chairman The Sales Management

### Algorithm set of steps used to solve a mathematical computation. Area The number of square units that covers a shape or figure

Fifth Grade CCSS Math Vocabulary Word List *Terms with an asterisk are meant for teacher knowledge only students need to learn the concept but not necessarily the term. Addend Any number being added Algorithm

### In this chapter, you will learn improvement curve concepts and their application to cost and price analysis.

7.0 - Chapter Introduction In this chapter, you will learn improvement curve concepts and their application to cost and price analysis. Basic Improvement Curve Concept. You may have learned about improvement

### Simple Predictive Analytics Curtis Seare

Using Excel to Solve Business Problems: Simple Predictive Analytics Curtis Seare Copyright: Vault Analytics July 2010 Contents Section I: Background Information Why use Predictive Analytics? How to use

### If A is divided by B the result is 2/3. If B is divided by C the result is 4/7. What is the result if A is divided by C?

Problem 3 If A is divided by B the result is 2/3. If B is divided by C the result is 4/7. What is the result if A is divided by C? Suggested Questions to ask students about Problem 3 The key to this question

### CVChess: Computer Vision Chess Analytics

CVChess: Computer Vision Chess Analytics Jay Hack and Prithvi Ramakrishnan Abstract We present a computer vision application and a set of associated algorithms capable of recording chess game moves fully

### Identifying Market Price Levels using Differential Evolution

Identifying Market Price Levels using Differential Evolution Michael Mayo University of Waikato, Hamilton, New Zealand mmayo@waikato.ac.nz WWW home page: http://www.cs.waikato.ac.nz/~mmayo/ Abstract. Evolutionary

### SAS BI Dashboard 3.1. User s Guide

SAS BI Dashboard 3.1 User s Guide The correct bibliographic citation for this manual is as follows: SAS Institute Inc. 2007. SAS BI Dashboard 3.1: User s Guide. Cary, NC: SAS Institute Inc. SAS BI Dashboard

### Stanford s Distantly-Supervised Slot-Filling System

Stanford s Distantly-Supervised Slot-Filling System Mihai Surdeanu, Sonal Gupta, John Bauer, David McClosky, Angel X. Chang, Valentin I. Spitkovsky, Christopher D. Manning Computer Science Department,

### Making Sense of the Mayhem: Machine Learning and March Madness

Making Sense of the Mayhem: Machine Learning and March Madness Alex Tran and Adam Ginzberg Stanford University atran3@stanford.edu ginzberg@stanford.edu I. Introduction III. Model The goal of our research

### Accurately and Efficiently Measuring Individual Account Credit Risk On Existing Portfolios

Accurately and Efficiently Measuring Individual Account Credit Risk On Existing Portfolios By: Michael Banasiak & By: Daniel Tantum, Ph.D. What Are Statistical Based Behavior Scoring Models And How Are

### ONTOLOGIES A short tutorial with references to YAGO Cosmina CROITORU

ONTOLOGIES p. 1/40 ONTOLOGIES A short tutorial with references to YAGO Cosmina CROITORU Unlocking the Secrets of the Past: Text Mining for Historical Documents Blockseminar, 21.2.-11.3.2011 ONTOLOGIES

### PATTERN RECOGNITION AND MACHINE LEARNING CHAPTER 4: LINEAR MODELS FOR CLASSIFICATION

PATTERN RECOGNITION AND MACHINE LEARNING CHAPTER 4: LINEAR MODELS FOR CLASSIFICATION Introduction In the previous chapter, we explored a class of regression models having particularly simple analytical

### STATISTICA. Clustering Techniques. Case Study: Defining Clusters of Shopping Center Patrons. and

Clustering Techniques and STATISTICA Case Study: Defining Clusters of Shopping Center Patrons STATISTICA Solutions for Business Intelligence, Data Mining, Quality Control, and Web-based Analytics Table

### Inferential Statistics

Inferential Statistics Sampling and the normal distribution Z-scores Confidence levels and intervals Hypothesis testing Commonly used statistical methods Inferential Statistics Descriptive statistics are

### Cluster Analysis for Evaluating Trading Strategies 1

CONTRIBUTORS Jeff Bacidore Managing Director, Head of Algorithmic Trading, ITG, Inc. Jeff.Bacidore@itg.com +1.212.588.4327 Kathryn Berkow Quantitative Analyst, Algorithmic Trading, ITG, Inc. Kathryn.Berkow@itg.com

### A Software Tool for. Automatically Veried Operations on. Intervals and Probability Distributions. Daniel Berleant and Hang Cheng

A Software Tool for Automatically Veried Operations on Intervals and Probability Distributions Daniel Berleant and Hang Cheng Abstract We describe a software tool for performing automatically veried arithmetic

### D-optimal plans in observational studies

D-optimal plans in observational studies Constanze Pumplün Stefan Rüping Katharina Morik Claus Weihs October 11, 2005 Abstract This paper investigates the use of Design of Experiments in observational

### Equilibrium: Illustrations

Draft chapter from An introduction to game theory by Martin J. Osborne. Version: 2002/7/23. Martin.Osborne@utoronto.ca http://www.economics.utoronto.ca/osborne Copyright 1995 2002 by Martin J. Osborne.

### Common Tools for Displaying and Communicating Data for Process Improvement

Common Tools for Displaying and Communicating Data for Process Improvement Packet includes: Tool Use Page # Box and Whisker Plot Check Sheet Control Chart Histogram Pareto Diagram Run Chart Scatter Plot

### Visualization Quick Guide

Visualization Quick Guide A best practice guide to help you find the right visualization for your data WHAT IS DOMO? Domo is a new form of business intelligence (BI) unlike anything before an executive

### EXCEL EXERCISE AND ACCELERATION DUE TO GRAVITY

EXCEL EXERCISE AND ACCELERATION DUE TO GRAVITY Objective: To learn how to use the Excel spreadsheet to record your data, calculate values and make graphs. To analyze the data from the Acceleration Due

### Such As Statements, Kindergarten Grade 8

Such As Statements, Kindergarten Grade 8 This document contains the such as statements that were included in the review committees final recommendations for revisions to the mathematics Texas Essential

### Chapter 19 Operational Amplifiers

Chapter 19 Operational Amplifiers The operational amplifier, or op-amp, is a basic building block of modern electronics. Op-amps date back to the early days of vacuum tubes, but they only became common

### Local outlier detection in data forensics: data mining approach to flag unusual schools

Local outlier detection in data forensics: data mining approach to flag unusual schools Mayuko Simon Data Recognition Corporation Paper presented at the 2012 Conference on Statistical Detection of Potential

### 16.1 MAPREDUCE. For personal use only, not for distribution. 333

For personal use only, not for distribution. 333 16.1 MAPREDUCE Initially designed by the Google labs and used internally by Google, the MAPREDUCE distributed programming model is now promoted by several

### Performance Level Descriptors Grade 6 Mathematics

Performance Level Descriptors Grade 6 Mathematics Multiplying and Dividing with Fractions 6.NS.1-2 Grade 6 Math : Sub-Claim A The student solves problems involving the Major Content for grade/course with

### Report of for Chapter 2 pretest

Report of for Chapter 2 pretest Exam: Chapter 2 pretest Category: Organizing and Graphing Data 1. "For our study of driving habits, we recorded the speed of every fifth vehicle on Drury Lane. Nearly every

### Measuring Lift Quality in Database Marketing

Measuring Lift Quality in Database Marketing Gregory Piatetsky-Shapiro Xchange Inc. One Lincoln Plaza, 89 South Street Boston, MA 2111 gps@xchange.com Sam Steingold Xchange Inc. One Lincoln Plaza, 89 South

### Choices, choices, choices... Which sequence database? Which modifications? What mass tolerance?

Optimization 1 Choices, choices, choices... Which sequence database? Which modifications? What mass tolerance? Where to begin? 2 Sequence Databases Swiss-prot MSDB, NCBI nr dbest Species specific ORFS

### Demographics of Atlanta, Georgia:

Demographics of Atlanta, Georgia: A Visual Analysis of the 2000 and 2010 Census Data 36-315 Final Project Rachel Cohen, Kathryn McKeough, Minnar Xie & David Zimmerman Ethnicities of Atlanta Figure 1: From

### Gamma Distribution Fitting

Chapter 552 Gamma Distribution Fitting Introduction This module fits the gamma probability distributions to a complete or censored set of individual or grouped data values. It outputs various statistics

### Practical Graph Mining with R. 5. Link Analysis

Practical Graph Mining with R 5. Link Analysis Outline Link Analysis Concepts Metrics for Analyzing Networks PageRank HITS Link Prediction 2 Link Analysis Concepts Link A relationship between two entities

### Mining the Software Change Repository of a Legacy Telephony System

Mining the Software Change Repository of a Legacy Telephony System Jelber Sayyad Shirabad, Timothy C. Lethbridge, Stan Matwin School of Information Technology and Engineering University of Ottawa, Ottawa,

### Building a Question Classifier for a TREC-Style Question Answering System

Building a Question Classifier for a TREC-Style Question Answering System Richard May & Ari Steinberg Topic: Question Classification We define Question Classification (QC) here to be the task that, given

### Cracking the Sudoku: A Deterministic Approach

Cracking the Sudoku: A Deterministic Approach David Martin Erica Cross Matt Alexander Youngstown State University Center for Undergraduate Research in Mathematics Abstract The model begins with the formulation

### Technology Step-by-Step Using StatCrunch

Technology Step-by-Step Using StatCrunch Section 1.3 Simple Random Sampling 1. Select Data, highlight Simulate Data, then highlight Discrete Uniform. 2. Fill in the following window with the appropriate

Section 5.4 The Quadratic Formula 481 5.4 The Quadratic Formula Consider the general quadratic function f(x) = ax + bx + c. In the previous section, we learned that we can find the zeros of this function

### Subordinating to the Majority: Factoid Question Answering over CQA Sites

Journal of Computational Information Systems 9: 16 (2013) 6409 6416 Available at http://www.jofcis.com Subordinating to the Majority: Factoid Question Answering over CQA Sites Xin LIAN, Xiaojie YUAN, Haiwei

### COMP9417: Machine Learning and Data Mining

COMP9417: Machine Learning and Data Mining A note on the two-class confusion matrix, lift charts and ROC curves Session 1, 2003 Acknowledgement The material in this supplementary note is based in part

### Agility, Uncertainty, and Software Project Estimation Todd Little, Landmark Graphics

Agility, Uncertainty, and Software Project Estimation Todd Little, Landmark Graphics Summary Prior studies in software development project estimation have demonstrated large variations in the estimated

### Final Project Report

CPSC545 by Introduction to Data Mining Prof. Martin Schultz & Prof. Mark Gerstein Student Name: Yu Kor Hugo Lam Student ID : 904907866 Due Date : May 7, 2007 Introduction Final Project Report Pseudogenes

### t Tests in Excel The Excel Statistical Master By Mark Harmon Copyright 2011 Mark Harmon

t-tests in Excel By Mark Harmon Copyright 2011 Mark Harmon No part of this publication may be reproduced or distributed without the express permission of the author. mark@excelmasterseries.com www.excelmasterseries.com

### A Logistic Regression Approach to Ad Click Prediction

A Logistic Regression Approach to Ad Click Prediction Gouthami Kondakindi kondakin@usc.edu Satakshi Rana satakshr@usc.edu Aswin Rajkumar aswinraj@usc.edu Sai Kaushik Ponnekanti ponnekan@usc.edu Vinit Parakh

### False_ If there are no fixed costs, then average cost cannot be higher than marginal cost for all output levels.

LECTURE 10: SINGLE INPUT COST FUNCTIONS ANSWERS AND SOLUTIONS True/False Questions False_ When a firm is using only one input, the cost function is simply the price of that input times how many units of

### Can you design an algorithm that searches for the maximum value in the list?

The Science of Computing I Lesson 2: Searching and Sorting Living with Cyber Pillar: Algorithms Searching One of the most common tasks that is undertaken in Computer Science is the task of searching. We

### Sampling Distributions and the Central Limit Theorem

135 Part 2 / Basic Tools of Research: Sampling, Measurement, Distributions, and Descriptive Statistics Chapter 10 Sampling Distributions and the Central Limit Theorem In the previous chapter we explained

### Option Portfolio Modeling

Value of Option (Total=Intrinsic+Time Euro) Option Portfolio Modeling Harry van Breen www.besttheindex.com E-mail: h.j.vanbreen@besttheindex.com Introduction The goal of this white paper is to provide

### So today we shall continue our discussion on the search engines and web crawlers. (Refer Slide Time: 01:02)

Internet Technology Prof. Indranil Sengupta Department of Computer Science and Engineering Indian Institute of Technology, Kharagpur Lecture No #39 Search Engines and Web Crawler :: Part 2 So today we

### Online Ensembles for Financial Trading

Online Ensembles for Financial Trading Jorge Barbosa 1 and Luis Torgo 2 1 MADSAD/FEP, University of Porto, R. Dr. Roberto Frias, 4200-464 Porto, Portugal jorgebarbosa@iol.pt 2 LIACC-FEP, University of

### 1 Review of Least Squares Solutions to Overdetermined Systems

cs4: introduction to numerical analysis /9/0 Lecture 7: Rectangular Systems and Numerical Integration Instructor: Professor Amos Ron Scribes: Mark Cowlishaw, Nathanael Fillmore Review of Least Squares

### Using simulation to calculate the NPV of a project

Using simulation to calculate the NPV of a project Marius Holtan Onward Inc. 5/31/2002 Monte Carlo simulation is fast becoming the technology of choice for evaluating and analyzing assets, be it pure financial

### NCSS Statistical Software Principal Components Regression. In ordinary least squares, the regression coefficients are estimated using the formula ( )

Chapter 340 Principal Components Regression Introduction is a technique for analyzing multiple regression data that suffer from multicollinearity. When multicollinearity occurs, least squares estimates

### ARE211, Fall2012. Contents. 2. Linear Algebra Preliminary: Level Sets, upper and lower contour sets and Gradient vectors 1

ARE11, Fall1 LINALGEBRA1: THU, SEP 13, 1 PRINTED: SEPTEMBER 19, 1 (LEC# 7) Contents. Linear Algebra 1.1. Preliminary: Level Sets, upper and lower contour sets and Gradient vectors 1.. Vectors as arrows.

### Performance Metrics for Graph Mining Tasks

Performance Metrics for Graph Mining Tasks 1 Outline Introduction to Performance Metrics Supervised Learning Performance Metrics Unsupervised Learning Performance Metrics Optimizing Metrics Statistical

### Despite its emphasis on credit-scoring/rating model validation,

RETAIL RISK MANAGEMENT Empirical Validation of Retail Always a good idea, development of a systematic, enterprise-wide method to continuously validate credit-scoring/rating models nonetheless received

### Chapter-1 : Introduction 1 CHAPTER - 1. Introduction

Chapter-1 : Introduction 1 CHAPTER - 1 Introduction This thesis presents design of a new Model of the Meta-Search Engine for getting optimized search results. The focus is on new dimension of internet

### Machine Learning. Mausam (based on slides by Tom Mitchell, Oren Etzioni and Pedro Domingos)

Machine Learning Mausam (based on slides by Tom Mitchell, Oren Etzioni and Pedro Domingos) What Is Machine Learning? A computer program is said to learn from experience E with respect to some class of

### Probabilistic Record Linkages for Generating a Comprehensive Epidemiological Research File on Maternal and Infant Health

Probabilistic Record Linkages Page 1 of 15 Probabilistic Record Linkages for Generating a Comprehensive Epidemiological Research File on Maternal and Infant Health Beate Danielsen, Health Information Solutions

### Normality Testing in Excel

Normality Testing in Excel By Mark Harmon Copyright 2011 Mark Harmon No part of this publication may be reproduced or distributed without the express permission of the author. mark@excelmasterseries.com

### Exercise 1: How to Record and Present Your Data Graphically Using Excel Dr. Chris Paradise, edited by Steven J. Price

Biology 1 Exercise 1: How to Record and Present Your Data Graphically Using Excel Dr. Chris Paradise, edited by Steven J. Price Introduction In this world of high technology and information overload scientists

### BIG DATA IN THE FINANCIAL WORLD

BIG DATA IN THE FINANCIAL WORLD Predictive and real-time analytics are essential to big data operation in the financial world Big Data Republic and Dell teamed up to survey financial organizations regarding