| 000 -LEADER |
|---|
| fixed length control field |
12043nam a22004097a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
CITU |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20250812105623.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
141017s2015 flu b 001 0 eng |
| 010 ## - LIBRARY OF CONGRESS CONTROL NUMBER |
|---|
| LC control number |
2014040184 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9781482234510 |
| 041 ## - LANGUAGE CODE |
|---|
| Language code of text/sound track or separate title |
eng |
| 042 ## - AUTHENTICATION CODE |
|---|
| Authentication code |
pcc |
| 050 00 - LIBRARY OF CONGRESS CALL NUMBER |
|---|
| Classification number |
HD30.215 |
| Item number |
.P75 2015 |
| 082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
658/.0557 |
| Edition number |
23 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Preferred name for the person |
Pries, Kim H., |
| Dates associated with a name |
1955- |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Big data analytics : |
| Remainder of title |
a practical guide for managers / |
| Statement of responsibility, etc |
Kim H. Pries, Robert Dunnigan. |
| 264 #1 - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication, distribution, etc |
Boca Raton, FL : |
| Name of publisher, distributor, etc |
CRC Press, |
| Date of publication, distribution, etc |
[2015] |
| 264 #4 - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Date of publication, distribution, etc |
c2015 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xix, 556 pages ; |
| Dimensions |
24 cm |
| 336 ## - CONTENT TYPE |
|---|
| Content type term |
text |
| Content type code |
txt |
| Source |
rdacontent |
| 337 ## - MEDIA TYPE |
|---|
| Media type term |
unmediated |
| Media type code |
n |
| Source |
rdamedia |
| 338 ## - CARRIER TYPE |
|---|
| Carrier type term |
volume |
| Carrier type code |
nc |
| Source |
rdacarrier |
| 500 ## - GENERAL NOTE |
|---|
| General note |
ABOUT THE AUTHOR/S<br/>Kim H. Pries has four college degrees: a bachelor of arts in history from the University of Texas at El Paso (UTEP), a bachelor of science in metallurgical engineering from UTEP, a master of science in engineering from UTEP, and a master of science in metallurgical engineering and materials science from Carnegie-Mellon University.<br/><br/>Pries worked as a computer systems manager, a software engineer for an electrical utility, and a scientific programmer under a defense contract for Stoneridge, Incorporated (SRI). He has worked as software manager, engineering services manager, reliability section manager, and product integrity and reliability director.<br/><br/>In addition to his other responsibilities, Pries has provided Six Sigma training for both UTEP and SRI and cost reduction initiatives for SRI. Pries is also a founding faculty member of Practical Project Management. Additionally, in concert with Jon Quigley, Pries was a cofounder and principal with Value Transformation, LLC, a training, testing, cost improvement, and product development consultancy.<br/><br/>He trained for Introduction to Engineering Design and Computer Science and Software Engineering with Project Lead the Way. He currently teaches biotechnology, computer science and software engineering, and introduction to engineering design at the beautiful Parkland High School in the Ysleta Independent School District of El Paso, Texas.<br/><br/>Robert Dunnigan is a manager with Janus Consulting Partners and is based in Dallas, Texas. He holds a bachelor of science in psychology and in sociology with an anthropology emphasis from North Dakota State University. He also holds a master of business administration from INSEAD, "the business school for the world," where he attended the Singapore campus.<br/><br/>As a Peace Corps volunteer, Robert served over 3 years in Honduras developing agribusiness opportunities. As a consultant, he later worked on the Afghanistan Small and Medium Enterprise Development project in Afghanistan, where he traveled the country with his Afghan colleagues and friends seeking opportunities to develop a manufacturing sector in the country.<br/><br/>Robert is an American Society for Quality–certified Six Sigma Black Belt and a Scrum Alliance–certified Scrum Master. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc |
Includes bibliographical references and index. |
| 505 0# - CONTENTS |
|---|
| Formatted contents note |
Table of Contents<br/><br/>Introduction<br/>So What Is Big Data?<br/>Growing Interest in Decision Making<br/>What This Book Addresses<br/>The Conversation about Big Data<br/>Technological Change as a Driver of Big Data<br/>The Central Question: So What?<br/>Our Goals as Authors<br/>References<br/><br/>The Mother of Invention?s Triplets: Moore?s Law, the Proliferation of Data, and Data Storage Technology<br/>Moore?s Law<br/>Parallel Computing, Between and Within Machines<br/>Quantum Computing<br/>Recap of Growth in Computing Power<br/>Storage, Storage Everywhere<br/>Grist for the Mill: Data Used and Unused<br/>Agriculture<br/>Automotive<br/>Marketing in the Physical World<br/>Online Marketing<br/>Asset Reliability and Efficiency<br/>Process Tracking and Automation<br/>Toward a Definition of Big Data<br/>Putting Big Data in Context<br/>Key Concepts of Big Data and Their Consequences<br/>Summary<br/>References.<br/><br/>Hadoop<br/>Power through Distribution<br/> Cost Effectiveness of Hadoop<br/>Not Every Problem Is a Nail<br/> Some Technical Aspects<br/>Troubleshooting Hadoop<br/>Running Hadoop<br/>Hadoop File System<br/> MapReduce<br/>Pig and Hive<br/>Installation<br/>Current Hadoop Ecosystem<br/>Hadoop Vendors<br/> Cloudera<br/>Amazon Web Services (AWS)<br/>Hortonworks<br/>IBM<br/>Intel<br/>MapR<br/>Microsoft<br/> To Run Pig Latin Using Powershell<br/>Pivotal<br/>References<br/><br/>HBase and Other Big Data Databases<br/>Evolution from Flat File to the Three V?s<br/> Flat File<br/> Hierarchical Database<br/> Network Database<br/> Relational Database<br/> Object-Oriented Databases<br/> Relational-Object Databases<br/>Transition to Big Data Databases<br/> What Is Different bbout HBase?<br/> What Is Bigtable?<br/> What Is MapReduce?<br/> What Are the Various Modalities for Big Data Databases?<br/>Graph Databases<br/> How Does a Graph Database Work?<br/> What is the Performance of a Graph Database?<br/>Document Databases<br/>Key-Value Databases<br/>Column-Oriented Databases<br/> HBase<br/> Apache Accumulo<br/>References<br/><br/>Machine Learning<br/>Machine Learning Basics<br/>Classifying with Nearest Neighbors<br/>Naive Bayes<br/>Support Vector Machines<br/>Improving Classification with Adaptive Boosting<br/>Regression<br/>Logistic Regression<br/>Tree-Based Regression<br/>K-Means Clustering<br/>Apriori Algorithm<br/>Frequent Pattern-Growth<br/>Principal Component Analysis (PCA)<br/>Singular Value Decomposition<br/>Neural Networks<br/>Big Data and MapReduce<br/>Data Exploration<br/>Spam Filtering<br/>Ranking<br/>Predictive Regression<br/>Text Regression<br/>Multidimensional Scaling<br/>Social Graphing<br/>References<br/><br/>Statistics<br/>Statistics, Statistics Everywhere<br/>Digging into the Data<br/>Standard Deviation: The Standard Measure of Dispersion<br/>The Power of Shapes: Distributions<br/>Distributions: Gaussian Curve<br/>Distributions: Why Be Normal?<br/>Distributions: The Long Arm of the Power Law<br/>The Upshot? Statistics Are not Bloodless<br/>Fooling Ourselves: Seeing What We Want to See in the Data<br/>We Can Learn Much from an Octopus<br/>Hypothesis Testing: Seeking a Verdict<br/> Two-Tailed Testing<br/>Hypothesis Testing: A Broad Field<br/>Moving on to Specific Hypothesis Tests<br/>Regression and Correlation<br/>p Value in Hypothesis Testing: A Successful Gatekeeper?<br/>Specious Correlations and Overfitting the Data<br/>A Sample of Common Statistical Software Packages<br/> Minitab<br/> SPSS<br/> R<br/> SAS<br/> Big Data Analytics<br/> Hadoop Integration<br/> Angoss<br/> Statistica<br/> Capabilities<br/>Summary<br/>References<br/><br/>Google<br/>Big Data Giants<br/>Google<br/> Go<br/> Android<br/> Google Product Offerings<br/> Google Analytics<br/> Advertising and Campaign Performance<br/> Analysis and Testing<br/>Facebook<br/>Ning<br/>Non-United States Social Media<br/> Tencent<br/> Line<br/> Sina Weibo<br/> Odnoklassniki<br/> Vkontakte<br/> Nimbuzz<br/>Ranking Network Sites<br/>Negative Issues with Social Networks<br/>Amazon<br/>Some Final Words<br/>References<br/><br/>Geographic Information Systems (GIS)<br/>GIS Implementations<br/>A GIS Example<br/>GIS Tools<br/>GIS Databases<br/>References<br/><br/>Discovery<br/>Faceted Search versus Strict Taxonomy<br/>First Key Ability: Breaking Down Barriers<br/>Second Key Ability: Flexible Search and Navigation<br/>Underlying Technology<br/>The Upshot<br/>Summary<br/>References<br/><br/>Data Quality<br/>Know Thy Data and Thyself<br/>Structured, Unstructured, and Semistructured Data<br/>Data Inconsistency: An Example from This Book<br/>The Black Swan and Incomplete Data<br/>How Data Can Fool Us<br/> Ambiguous Data<br/> Aging of Data or Variables<br/> Missing Variables May Change the Meaning<br/> Inconsistent Use of Units and Terminology<br/>Biases<br/> Sampling Bias<br/> Publication Bias<br/> Survivorship Bias<br/>Data as a Video, Not a Snapshot: Different Viewpoints as a Noise Filter<br/>What Is My Toolkit for Improving My Data?<br/> Ishikawa Diagram<br/> Interrelationship Digraph<br/> Force Field Analysis<br/>Data-Centric Methods<br/> Troubleshooting Queries from Source Data<br/> Troubleshooting Data Quality beyond the Source System<br/> Using Our Hidden Resources<br/>Summary<br/>References<br/><br/>Benefits<br/>Data Serendipity<br/>Converting Data Dreck to Usefulness<br/>Sales<br/>Returned Merchandise<br/>Security<br/>Medical<br/>Travel<br/> Lodging<br/> Vehicle<br/> Meals<br/>Geographical Information Systems<br/> New York City<br/> Chicago CLEARMAP<br/> Baltimore<br/> San Francisco<br/> Los Angeles<br/> Tucson, Arizona, University of Arizona, and COPLINK<br/>Social Networking<br/>Education<br/> General Educational Data<br/> Legacy Data<br/> Grades and other Indicators<br/> Testing Results<br/> Addresses, Phone Numbers, and More<br/>Concluding Comments<br/>References<br/><br/>Concerns<br/>Part Two: Basic Principles of National Application<br/> Collection Limitation Principle<br/> Data Quality Principle<br/> Purpose Specification Principle<br/> Use Limitation Principle<br/> Security Safeguards Principle<br/> Openness Principle<br/> Individual Participation Principle<br/> Accountability Principle<br/>Logical Fallacies<br/> Affirming the Consequent<br/> Denying the Antecedent<br/> Ludic Fallacy<br/>Cognitive Biases<br/> Confirmation Bias<br/> Notational Bias<br/> Selection/Sample Bias<br/> Halo Effect<br/> Consistency and Hindsight Biases<br/> Congruence Bias<br/> Von Restorff Effect<br/>Data Serendipity<br/> Converting Data Dreck to Usefulness Sales<br/>Merchandise Returns<br/>Security<br/> CompStat<br/> Medical<br/>Travel<br/> Lodging<br/> Vehicle<br/> Meals<br/>Social Networking<br/>Education<br/>Making Yourself Harder to Track<br/> Misinformation<br/> Disinformation<br/> Reducing/Eliminating Profiles<br/> Social Media<br/> Self Redefinition<br/> Identity Theft<br/> Facebook<br/>Concluding Comments<br/>References<br/><br/>Epilogue<br/> Michael Porter?s Five Forces Model<br/> Bargaining Power of Customers<br/> Bargaining Power of Suppliers<br/> Threat of New Entrants<br/> Others<br/>The OODA Loop<br/>Implementing Big Data<br/>Nonlinear, Qualitative Thinking<br/>Closing<br/>References |
| 520 0# - SUMMARY, ETC. |
|---|
| Summary, etc |
With this book, managers and decision makers are given the tools to make more informed decisions about big data purchasing initiatives. Big Data Analytics: A Practical Guide for Managers not only supplies descriptions of common tools, but also surveys the various products and vendors that supply the big data market. Comparing and contrasting the different types of analysis commonly conducted with big data, this accessible reference presents clear-cut explanations of the general workings of big data tools. Instead of spending time on HOW to install specific packages, it focuses on the reasons WHY readers would install a given package. The book provides authoritative guidance on a range of tools, including open source and proprietary systems. It details the strengths and weaknesses of incorporating big data analysis into decision-making and explains how to leverage the strengths while mitigating the weaknesses. Describes the benefits of distributed computing in simple terms; includes substantial vendor/tool material, especially for open source decisions; covers prominent software packages, including Hadoop and Oracle Endeca; Examines GIS and machine learning applications; Considers privacy and surveillance issues. The book further explores basic statistical concepts that, when misapplied, can be the source of errors. Time and again, big data is treated as an oracle that discovers results nobody would have imagined. While big data can serve this valuable function, all too often these results are incorrect yet are still reported unquestioningly. The probability of having erroneous results increases as a larger number of variables are compared unless preventative measures are taken. The approach taken by the authors is to explain these concepts so managers can ask better questions of their analysts and vendors about the appropriateness of the methods used to arrive at a conclusion. Because the world of science and medicine has been grappling with similar issues in the publication of studies, the authors draw on their efforts and apply them to big data.<br/><br/><br/>Features:<br/><br/> Provides guidance on an array of tools, including open source and proprietary systems<br/> Supplies proven techniques for ensuring data quality<br/> Considers surveillance and privacy issues<br/> Examines GIS applications and machine learning<br/> Describes how different types of organizations address their data needs |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Management |
| General subdivision |
Statistical methods. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Management |
| General subdivision |
Data processing. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Big data. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Data mining. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Database management. |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Dunnigan, Robert. |
| 906 ## - LOCAL DATA ELEMENT F, LDF (RLIN) |
|---|
| a |
7 |
| b |
cbc |
| c |
orignew |
| d |
1 |
| e |
ecip |
| f |
20 |
| g |
y-gencatlg |
| 942 ## - ADDED ENTRY ELEMENTS |
|---|
| Source of classification or shelving scheme |
|
| Item type |
BOOK |
| Issues (borrowed), all copies |
2 |