BUSINESS INTELLIGENCE AND ANALYTICS RAMESH SHARDA DU.docxfelicidaddinwoodie
BUSINESS INTELLIGENCE
AND ANALYTICS
RAMESH SHARDA
DURSUN DELEN
EFRAIM TURBAN
TENTH EDITION
.•
TENTH EDITION
BUSINESS INTELLIGENCE
AND ANALYTICS:
SYSTEMS FOR DECISION SUPPORT
Ramesh Sharda
Oklahoma State University
Dursun Delen
Oklahoma State University
Efraim Turban
University of Hawaii
With contributions by
J.E.Aronson
Tbe University of Georgia
Ting-Peng Liang
National Sun Yat-sen University
David King
]DA Software Group, Inc.
PEARSON
Boston Columbus Indianapolis New York San Francisco Upper Saddle River
Amsterdam Cape Town Dubai London Madrid Milan Munich Paris Montreal Toronto
Delhi Mexico City Sao Paulo Sydney Hong Kong Seoul Singapore Taipei Tokyo
Editor in Chief: Stephanie Wall
Executive Editor: Bob Horan
Program Manager Team Lead: Ashley Santora
Program Manager: Denise Vaughn
Executive Marketing Manager: Anne Fahlgren
Project Manager Team Lead: Judy Leale
Project Manager: Tom Benfatti
Operations Specialist: Michelle Klein
Creative Director: Jayne Conte
Cover Designer: Suzanne Behnke
Digital Production Project Manager: Lisa
Rinaldi
Full-Service Project Management: George Jacob,
Integra Software
Solution
s.
Printer/Binder: Edwards Brothers Malloy-Jackson
Road
Cover Printer: Lehigh/Phoenix-Hagerstown
Text Font: Garamond
Credits and acknowledgments borrowed from other sources and reproduced, with permission, in this textbook
appear on the appropriate page within text.
Microsoft and/ or its respective suppliers make no representations about the suitability of the information
contained in the documents and related graphics published as part of the services for any purpose. All such
documents and related graphics are provided "as is" without warranty of any kind. Microsoft and/or its
respective suppliers hereby disclaim all warranties and conditions with regard to this information, including
all warranties and conditions of merchantability, whether express, implied or statutory, fitness for a particular
purpose, title and non-infringement. In no event shall Microsoft and/or its respective suppliers be liable for
any special, indirect or consequential damages or any damages whatsoever resulting from loss of use , data or
profits, whether in an action of contract, negligence or other tortious action, arising out of or in connection
with the use or performance of information available from the services.
The documents and related graphics contained herein could include technical inaccuracies or typographical
errors. Changes are periodically added to the information here in. Microsoft and/or its respective suppliers may
make improvements and/or changes in the product(s) and/ or the program(s) described herein at any time.
Partial screen shots may be viewed in full within the software version specified.
Microsoft® Windows®, and Microsoft Office® are registered trademarks of the Microsoft Corporation in the U.S.A.
and other countries. This book is not .
BUSINESS INTELLIGENCE AND ANALYTICS RAMESH SHARDA DUTawnaDelatorrejs
BUSINESS INTELLIGENCE
AND ANALYTICS
RAMESH SHARDA
DURSUN DELEN
EFRAIM TURBAN
TENTH EDITION
.•
TENTH EDITION
BUSINESS INTELLIGENCE
AND ANALYTICS:
SYSTEMS FOR DECISION SUPPORT
Ramesh Sharda
Oklahoma State University
Dursun Delen
Oklahoma State University
Efraim Turban
University of Hawaii
With contributions by
J.E.Aronson
Tbe University of Georgia
Ting-Peng Liang
National Sun Yat-sen University
David King
]DA Software Group, Inc.
PEARSON
Boston Columbus Indianapolis New York San Francisco Upper Saddle River
Amsterdam Cape Town Dubai London Madrid Milan Munich Paris Montreal Toronto
Delhi Mexico City Sao Paulo Sydney Hong Kong Seoul Singapore Taipei Tokyo
Editor in Chief: Stephanie Wall
Executive Editor: Bob Horan
Program Manager Team Lead: Ashley Santora
Program Manager: Denise Vaughn
Executive Marketing Manager: Anne Fahlgren
Project Manager Team Lead: Judy Leale
Project Manager: Tom Benfatti
Operations Specialist: Michelle Klein
Creative Director: Jayne Conte
Cover Designer: Suzanne Behnke
Digital Production Project Manager: Lisa
Rinaldi
Full-Service Project Management: George Jacob,
Integra Software
Solution
s.
Printer/Binder: Edwards Brothers Malloy-Jackson
Road
Cover Printer: Lehigh/Phoenix-Hagerstown
Text Font: Garamond
Credits and acknowledgments borrowed from other sources and reproduced, with permission, in this textbook
appear on the appropriate page within text.
Microsoft and/ or its respective suppliers make no representations about the suitability of the information
contained in the documents and related graphics published as part of the services for any purpose. All such
documents and related graphics are provided "as is" without warranty of any kind. Microsoft and/or its
respective suppliers hereby disclaim all warranties and conditions with regard to this information, including
all warranties and conditions of merchantability, whether express, implied or statutory, fitness for a particular
purpose, title and non-infringement. In no event shall Microsoft and/or its respective suppliers be liable for
any special, indirect or consequential damages or any damages whatsoever resulting from loss of use , data or
profits, whether in an action of contract, negligence or other tortious action, arising out of or in connection
with the use or performance of information available from the services.
The documents and related graphics contained herein could include technical inaccuracies or typographical
errors. Changes are periodically added to the information here in. Microsoft and/or its respective suppliers may
make improvements and/or changes in the product(s) and/ or the program(s) described herein at any time.
Partial screen shots may be viewed in full within the software version specified.
Microsoft® Windows®, and Microsoft Office® are registered trademarks of the Microsoft Corporation in the U.S.A.
and other countries. This book is not ...
BUSINESS INTELLIGENCE AND ANALYTICS RAMESH SHARDA DUjenkinsmandie
BUSINESS INTELLIGENCE
AND ANALYTICS
RAMESH SHARDA
DURSUN DELEN
EFRAIM TURBAN
TENTH EDITION
.•
TENTH EDITION
BUSINESS INTELLIGENCE
AND ANALYTICS:
SYSTEMS FOR DECISION SUPPORT
Ramesh Sharda
Oklahoma State University
Dursun Delen
Oklahoma State University
Efraim Turban
University of Hawaii
With contributions by
J.E.Aronson
Tbe University of Georgia
Ting-Peng Liang
National Sun Yat-sen University
David King
]DA Software Group, Inc.
PEARSON
Boston Columbus Indianapolis New York San Francisco Upper Saddle River
Amsterdam Cape Town Dubai London Madrid Milan Munich Paris Montreal Toronto
Delhi Mexico City Sao Paulo Sydney Hong Kong Seoul Singapore Taipei Tokyo
Editor in Chief: Stephanie Wall
Executive Editor: Bob Horan
Program Manager Team Lead: Ashley Santora
Program Manager: Denise Vaughn
Executive Marketing Manager: Anne Fahlgren
Project Manager Team Lead: Judy Leale
Project Manager: Tom Benfatti
Operations Specialist: Michelle Klein
Creative Director: Jayne Conte
Cover Designer: Suzanne Behnke
Digital Production Project Manager: Lisa
Rinaldi
Full-Service Project Management: George Jacob,
Integra Software
Solution
s.
Printer/Binder: Edwards Brothers Malloy-Jackson
Road
Cover Printer: Lehigh/Phoenix-Hagerstown
Text Font: Garamond
Credits and acknowledgments borrowed from other sources and reproduced, with permission, in this textbook
appear on the appropriate page within text.
Microsoft and/ or its respective suppliers make no representations about the suitability of the information
contained in the documents and related graphics published as part of the services for any purpose. All such
documents and related graphics are provided "as is" without warranty of any kind. Microsoft and/or its
respective suppliers hereby disclaim all warranties and conditions with regard to this information, including
all warranties and conditions of merchantability, whether express, implied or statutory, fitness for a particular
purpose, title and non-infringement. In no event shall Microsoft and/or its respective suppliers be liable for
any special, indirect or consequential damages or any damages whatsoever resulting from loss of use, data or
profits, whether in an action of contract, negligence or other tortious action, arising out of or in connection
with the use or performance of information available from the services.
The documents and related graphics contained herein could include technical inaccuracies or typographical
errors. Changes are periodically added to the information herein. Microsoft and/or its respective suppliers may
make improvements and/or changes in the product(s) and/ or the program(s) described herein at any time.
Partial screen shots may be viewed in full within the software version specified.
Microsoft® Windows®, and Microsoft Office® are registered trademarks of the Microsoft Corporation in the U.S.A.
and other countries. This book is not sp ...
BUSINESS INTELLIGENCE AND ANALYTICS RAMESH SHARDA DUChereCoble417
BUSINESS INTELLIGENCE
AND ANALYTICS
RAMESH SHARDA
DURSUN DELEN
EFRAIM TURBAN
TENTH EDITION
.•
TENTH EDITION
BUSINESS INTELLIGENCE
AND ANALYTICS:
SYSTEMS FOR DECISION SUPPORT
Ramesh Sharda
Oklahoma State University
Dursun Delen
Oklahoma State University
Efraim Turban
University of Hawaii
With contributions by
J.E.Aronson
Tbe University of Georgia
Ting-Peng Liang
National Sun Yat-sen University
David King
]DA Software Group, Inc.
PEARSON
Boston Columbus Indianapolis New York San Francisco Upper Saddle River
Amsterdam Cape Town Dubai London Madrid Milan Munich Paris Montreal Toronto
Delhi Mexico City Sao Paulo Sydney Hong Kong Seoul Singapore Taipei Tokyo
Editor in Chief: Stephanie Wall
Executive Editor: Bob Horan
Program Manager Team Lead: Ashley Santora
Program Manager: Denise Vaughn
Executive Marketing Manager: Anne Fahlgren
Project Manager Team Lead: Judy Leale
Project Manager: Tom Benfatti
Operations Specialist: Michelle Klein
Creative Director: Jayne Conte
Cover Designer: Suzanne Behnke
Digital Production Project Manager: Lisa
Rinaldi
Full-Service Project Management: George Jacob,
Integra Software
Solution
s.
Printer/Binder: Edwards Brothers Malloy-Jackson
Road
Cover Printer: Lehigh/Phoenix-Hagerstown
Text Font: Garamond
Credits and acknowledgments borrowed from other sources and reproduced, with permission, in this textbook
appear on the appropriate page within text.
Microsoft and/ or its respective suppliers make no representations about the suitability of the information
contained in the documents and related graphics published as part of the services for any purpose. All such
documents and related graphics are provided "as is" without warranty of any kind. Microsoft and/or its
respective suppliers hereby disclaim all warranties and conditions with regard to this information, including
all warranties and conditions of merchantability, whether express, implied or statutory, fitness for a particular
purpose, title and non-infringement. In no event shall Microsoft and/or its respective suppliers be liable for
any special, indirect or consequential damages or any damages whatsoever resulting from loss of use, data or
profits, whether in an action of contract, negligence or other tortious action, arising out of or in connection
with the use or performance of information available from the services.
The documents and related graphics contained herein could include technical inaccuracies or typographical
errors. Changes are periodically added to the information herein. Microsoft and/or its respective suppliers may
make improvements and/or changes in the product(s) and/ or the program(s) described herein at any time.
Partial screen shots may be viewed in full within the software version specified.
Microsoft® Windows®, and Microsoft Office® are registered trademarks of the Microsoft Corporation in the U.S.A.
and other countries. This book is not sp ...
BUSINESS INTELLIGENCE AND ANALYTICS RAMESH SHARDA DU.docxjasoninnes20
BUSINESS INTELLIGENCE
AND ANALYTICS
RAMESH SHARDA
DURSUN DELEN
EFRAIM TURBAN
TENTH EDITION
.•
TENTH EDITION
BUSINESS INTELLIGENCE
AND ANALYTICS:
SYSTEMS FOR DECISION SUPPORT
Ramesh Sharda
Oklahoma State University
Dursun Delen
Oklahoma State University
Efraim Turban
University of Hawaii
With contributions by
J.E.Aronson
Tbe University of Georgia
Ting-Peng Liang
National Sun Yat-sen University
David King
]DA Software Group, Inc.
PEARSON
Boston Columbus Indianapolis New York San Francisco Upper Saddle River
Amsterdam Cape Town Dubai London Madrid Milan Munich Paris Montreal Toronto
Delhi Mexico City Sao Paulo Sydney Hong Kong Seoul Singapore Taipei Tokyo
Editor in Chief: Stephanie Wall
Executive Editor: Bob Horan
Program Manager Team Lead: Ashley Santora
Program Manager: Denise Vaughn
Executive Marketing Manager: Anne Fahlgren
Project Manager Team Lead: Judy Leale
Project Manager: Tom Benfatti
Operations Specialist: Michelle Klein
Creative Director: Jayne Conte
Cover Designer: Suzanne Behnke
Digital Production Project Manager: Lisa
Rinaldi
Full-Service Project Management: George Jacob,
Integra Software
Solution
s.
Printer/Binder: Edwards Brothers Malloy-Jackson
Road
Cover Printer: Lehigh/Phoenix-Hagerstown
Text Font: Garamond
Credits and acknowledgments borrowed from other sources and reproduced, with permission, in this textbook
appear on the appropriate page within text.
Microsoft and/ or its respective suppliers make no representations about the suitability of the information
contained in the documents and related graphics published as part of the services for any purpose. All such
documents and related graphics are provided "as is" without warranty of any kind. Microsoft and/or its
respective suppliers hereby disclaim all warranties and conditions with regard to this information, including
all warranties and conditions of merchantability, whether express, implied or statutory, fitness for a particular
purpose, title and non-infringement. In no event shall Microsoft and/or its respective suppliers be liable for
any special, indirect or consequential damages or any damages whatsoever resulting from loss of use, data or
profits, whether in an action of contract, negligence or other tortious action, arising out of or in connection
with the use or performance of information available from the services.
The documents and related graphics contained herein could include technical inaccuracies or typographical
errors. Changes are periodically added to the information herein. Microsoft and/or its respective suppliers may
make improvements and/or changes in the product(s) and/ or the program(s) described herein at any time.
Partial screen shots may be viewed in full within the software version specified.
Microsoft® Windows®, and Microsoft Office® are registered trademarks of the Microsoft Corporation in the U.S.A.
and other countries. This book is not sp ...
Chapter 10 Modeling and Analysis Heuristic Search Methods EstelaJeffery653
Chapter 10: Modeling and Analysis: Heuristic Search
Methods and Simulation
Learning Objectives
• Explain the basic concepts of simulation and when to
use it
• Understand the concepts and applications of different
types of simulation
• Explain what is meant by Monte Carlo and discrete
event simulation
Simulation
• Simulation is the “appearance” of reality
• It is often used to conduct what-if analysis on the
model of the actual system
• It is a popular DSS technique for conducting
experiments with a computer on a comprehensive
model of the system to assess its dynamic behavior
• Often used when the system is too complex for other
DSS techniques
Application Case 10.3
Simulating Effects of Hepatitis B
Interventions
Questions for Discussion
1. Explain the advantage of operations research methods such
as simulation over clinical trial methods in determining the
best control measure for Hepatitis B.
2. In what ways do the decision and Markov models provide
cost-effective ways of combating the disease?
3. Discuss how multidisciplinary background is an asset in
finding a solution for the problem described in the case.
4. Besides healthcare, in what other domain could such a
modeling approach help reduce cost?
Major Characteristics of Simulation
• Imitates reality and captures its richness both in
shape and behavior
• “Represent” versus “Imitate”
• Technique for conducting experiments
• Descriptive, not normative tool
• Often to “solve” [i.e., analyze] very complex
systems/problems
• Simulation should be used only when a numerical
optimization is not possible
Advantages of Simulation
• The theory is fairly straightforward
• Great deal of time compression
• Experiment with different alternatives
• The model reflects manager’s perspective
• Can handle wide variety of problem types
• Can include the real complexities of problems
• Produces important performance measures
• Often it is the only DSS modeling tool for non-structured problems
Disadvantages of Simulation
• Cannot guarantee an optimal solution
• Slow and costly construction process
• Cannot transfer solutions and inferences to solve other problems
(problem specific)
• So easy to explain/sell to managers, may lead to overlooking
analytical solutions
• Software may require special skills
Simulation Methodology
Steps:
1. Define problem 5. Conduct experiments
2. Construct the model 6. Evaluate results
3. Test and validate model 7. Implement solution
4. Design experiments
Simulation Types
• Probabilistic/Stochastic vs. Deterministic Simulation
• Uses probability distributions
• Time-dependent vs. Time-independent Simulation
• Monte Carlo technique (X = A + B)[A, B, and X are all
distributions]
• Discrete Event vs. Continuous Simulation
• Simulation Implementation
• Visual Simulation and/or Object-Oriented Simulation
Visual Interactive Simulation (VIS)
• Visual interactive modeling (VIM), also called Visual
Inte ...
BUSINESS INTELLIGENCE AND ANALYTICS RAMESH SHARDA DU.docxfelicidaddinwoodie
BUSINESS INTELLIGENCE
AND ANALYTICS
RAMESH SHARDA
DURSUN DELEN
EFRAIM TURBAN
TENTH EDITION
.•
TENTH EDITION
BUSINESS INTELLIGENCE
AND ANALYTICS:
SYSTEMS FOR DECISION SUPPORT
Ramesh Sharda
Oklahoma State University
Dursun Delen
Oklahoma State University
Efraim Turban
University of Hawaii
With contributions by
J.E.Aronson
Tbe University of Georgia
Ting-Peng Liang
National Sun Yat-sen University
David King
]DA Software Group, Inc.
PEARSON
Boston Columbus Indianapolis New York San Francisco Upper Saddle River
Amsterdam Cape Town Dubai London Madrid Milan Munich Paris Montreal Toronto
Delhi Mexico City Sao Paulo Sydney Hong Kong Seoul Singapore Taipei Tokyo
Editor in Chief: Stephanie Wall
Executive Editor: Bob Horan
Program Manager Team Lead: Ashley Santora
Program Manager: Denise Vaughn
Executive Marketing Manager: Anne Fahlgren
Project Manager Team Lead: Judy Leale
Project Manager: Tom Benfatti
Operations Specialist: Michelle Klein
Creative Director: Jayne Conte
Cover Designer: Suzanne Behnke
Digital Production Project Manager: Lisa
Rinaldi
Full-Service Project Management: George Jacob,
Integra Software
Solution
s.
Printer/Binder: Edwards Brothers Malloy-Jackson
Road
Cover Printer: Lehigh/Phoenix-Hagerstown
Text Font: Garamond
Credits and acknowledgments borrowed from other sources and reproduced, with permission, in this textbook
appear on the appropriate page within text.
Microsoft and/ or its respective suppliers make no representations about the suitability of the information
contained in the documents and related graphics published as part of the services for any purpose. All such
documents and related graphics are provided "as is" without warranty of any kind. Microsoft and/or its
respective suppliers hereby disclaim all warranties and conditions with regard to this information, including
all warranties and conditions of merchantability, whether express, implied or statutory, fitness for a particular
purpose, title and non-infringement. In no event shall Microsoft and/or its respective suppliers be liable for
any special, indirect or consequential damages or any damages whatsoever resulting from loss of use , data or
profits, whether in an action of contract, negligence or other tortious action, arising out of or in connection
with the use or performance of information available from the services.
The documents and related graphics contained herein could include technical inaccuracies or typographical
errors. Changes are periodically added to the information here in. Microsoft and/or its respective suppliers may
make improvements and/or changes in the product(s) and/ or the program(s) described herein at any time.
Partial screen shots may be viewed in full within the software version specified.
Microsoft® Windows®, and Microsoft Office® are registered trademarks of the Microsoft Corporation in the U.S.A.
and other countries. This book is not .
BUSINESS INTELLIGENCE AND ANALYTICS RAMESH SHARDA DUTawnaDelatorrejs
BUSINESS INTELLIGENCE
AND ANALYTICS
RAMESH SHARDA
DURSUN DELEN
EFRAIM TURBAN
TENTH EDITION
.•
TENTH EDITION
BUSINESS INTELLIGENCE
AND ANALYTICS:
SYSTEMS FOR DECISION SUPPORT
Ramesh Sharda
Oklahoma State University
Dursun Delen
Oklahoma State University
Efraim Turban
University of Hawaii
With contributions by
J.E.Aronson
Tbe University of Georgia
Ting-Peng Liang
National Sun Yat-sen University
David King
]DA Software Group, Inc.
PEARSON
Boston Columbus Indianapolis New York San Francisco Upper Saddle River
Amsterdam Cape Town Dubai London Madrid Milan Munich Paris Montreal Toronto
Delhi Mexico City Sao Paulo Sydney Hong Kong Seoul Singapore Taipei Tokyo
Editor in Chief: Stephanie Wall
Executive Editor: Bob Horan
Program Manager Team Lead: Ashley Santora
Program Manager: Denise Vaughn
Executive Marketing Manager: Anne Fahlgren
Project Manager Team Lead: Judy Leale
Project Manager: Tom Benfatti
Operations Specialist: Michelle Klein
Creative Director: Jayne Conte
Cover Designer: Suzanne Behnke
Digital Production Project Manager: Lisa
Rinaldi
Full-Service Project Management: George Jacob,
Integra Software
Solution
s.
Printer/Binder: Edwards Brothers Malloy-Jackson
Road
Cover Printer: Lehigh/Phoenix-Hagerstown
Text Font: Garamond
Credits and acknowledgments borrowed from other sources and reproduced, with permission, in this textbook
appear on the appropriate page within text.
Microsoft and/ or its respective suppliers make no representations about the suitability of the information
contained in the documents and related graphics published as part of the services for any purpose. All such
documents and related graphics are provided "as is" without warranty of any kind. Microsoft and/or its
respective suppliers hereby disclaim all warranties and conditions with regard to this information, including
all warranties and conditions of merchantability, whether express, implied or statutory, fitness for a particular
purpose, title and non-infringement. In no event shall Microsoft and/or its respective suppliers be liable for
any special, indirect or consequential damages or any damages whatsoever resulting from loss of use , data or
profits, whether in an action of contract, negligence or other tortious action, arising out of or in connection
with the use or performance of information available from the services.
The documents and related graphics contained herein could include technical inaccuracies or typographical
errors. Changes are periodically added to the information here in. Microsoft and/or its respective suppliers may
make improvements and/or changes in the product(s) and/ or the program(s) described herein at any time.
Partial screen shots may be viewed in full within the software version specified.
Microsoft® Windows®, and Microsoft Office® are registered trademarks of the Microsoft Corporation in the U.S.A.
and other countries. This book is not ...
BUSINESS INTELLIGENCE AND ANALYTICS RAMESH SHARDA DUjenkinsmandie
BUSINESS INTELLIGENCE
AND ANALYTICS
RAMESH SHARDA
DURSUN DELEN
EFRAIM TURBAN
TENTH EDITION
.•
TENTH EDITION
BUSINESS INTELLIGENCE
AND ANALYTICS:
SYSTEMS FOR DECISION SUPPORT
Ramesh Sharda
Oklahoma State University
Dursun Delen
Oklahoma State University
Efraim Turban
University of Hawaii
With contributions by
J.E.Aronson
Tbe University of Georgia
Ting-Peng Liang
National Sun Yat-sen University
David King
]DA Software Group, Inc.
PEARSON
Boston Columbus Indianapolis New York San Francisco Upper Saddle River
Amsterdam Cape Town Dubai London Madrid Milan Munich Paris Montreal Toronto
Delhi Mexico City Sao Paulo Sydney Hong Kong Seoul Singapore Taipei Tokyo
Editor in Chief: Stephanie Wall
Executive Editor: Bob Horan
Program Manager Team Lead: Ashley Santora
Program Manager: Denise Vaughn
Executive Marketing Manager: Anne Fahlgren
Project Manager Team Lead: Judy Leale
Project Manager: Tom Benfatti
Operations Specialist: Michelle Klein
Creative Director: Jayne Conte
Cover Designer: Suzanne Behnke
Digital Production Project Manager: Lisa
Rinaldi
Full-Service Project Management: George Jacob,
Integra Software
Solution
s.
Printer/Binder: Edwards Brothers Malloy-Jackson
Road
Cover Printer: Lehigh/Phoenix-Hagerstown
Text Font: Garamond
Credits and acknowledgments borrowed from other sources and reproduced, with permission, in this textbook
appear on the appropriate page within text.
Microsoft and/ or its respective suppliers make no representations about the suitability of the information
contained in the documents and related graphics published as part of the services for any purpose. All such
documents and related graphics are provided "as is" without warranty of any kind. Microsoft and/or its
respective suppliers hereby disclaim all warranties and conditions with regard to this information, including
all warranties and conditions of merchantability, whether express, implied or statutory, fitness for a particular
purpose, title and non-infringement. In no event shall Microsoft and/or its respective suppliers be liable for
any special, indirect or consequential damages or any damages whatsoever resulting from loss of use, data or
profits, whether in an action of contract, negligence or other tortious action, arising out of or in connection
with the use or performance of information available from the services.
The documents and related graphics contained herein could include technical inaccuracies or typographical
errors. Changes are periodically added to the information herein. Microsoft and/or its respective suppliers may
make improvements and/or changes in the product(s) and/ or the program(s) described herein at any time.
Partial screen shots may be viewed in full within the software version specified.
Microsoft® Windows®, and Microsoft Office® are registered trademarks of the Microsoft Corporation in the U.S.A.
and other countries. This book is not sp ...
BUSINESS INTELLIGENCE AND ANALYTICS RAMESH SHARDA DUChereCoble417
BUSINESS INTELLIGENCE
AND ANALYTICS
RAMESH SHARDA
DURSUN DELEN
EFRAIM TURBAN
TENTH EDITION
.•
TENTH EDITION
BUSINESS INTELLIGENCE
AND ANALYTICS:
SYSTEMS FOR DECISION SUPPORT
Ramesh Sharda
Oklahoma State University
Dursun Delen
Oklahoma State University
Efraim Turban
University of Hawaii
With contributions by
J.E.Aronson
Tbe University of Georgia
Ting-Peng Liang
National Sun Yat-sen University
David King
]DA Software Group, Inc.
PEARSON
Boston Columbus Indianapolis New York San Francisco Upper Saddle River
Amsterdam Cape Town Dubai London Madrid Milan Munich Paris Montreal Toronto
Delhi Mexico City Sao Paulo Sydney Hong Kong Seoul Singapore Taipei Tokyo
Editor in Chief: Stephanie Wall
Executive Editor: Bob Horan
Program Manager Team Lead: Ashley Santora
Program Manager: Denise Vaughn
Executive Marketing Manager: Anne Fahlgren
Project Manager Team Lead: Judy Leale
Project Manager: Tom Benfatti
Operations Specialist: Michelle Klein
Creative Director: Jayne Conte
Cover Designer: Suzanne Behnke
Digital Production Project Manager: Lisa
Rinaldi
Full-Service Project Management: George Jacob,
Integra Software
Solution
s.
Printer/Binder: Edwards Brothers Malloy-Jackson
Road
Cover Printer: Lehigh/Phoenix-Hagerstown
Text Font: Garamond
Credits and acknowledgments borrowed from other sources and reproduced, with permission, in this textbook
appear on the appropriate page within text.
Microsoft and/ or its respective suppliers make no representations about the suitability of the information
contained in the documents and related graphics published as part of the services for any purpose. All such
documents and related graphics are provided "as is" without warranty of any kind. Microsoft and/or its
respective suppliers hereby disclaim all warranties and conditions with regard to this information, including
all warranties and conditions of merchantability, whether express, implied or statutory, fitness for a particular
purpose, title and non-infringement. In no event shall Microsoft and/or its respective suppliers be liable for
any special, indirect or consequential damages or any damages whatsoever resulting from loss of use, data or
profits, whether in an action of contract, negligence or other tortious action, arising out of or in connection
with the use or performance of information available from the services.
The documents and related graphics contained herein could include technical inaccuracies or typographical
errors. Changes are periodically added to the information herein. Microsoft and/or its respective suppliers may
make improvements and/or changes in the product(s) and/ or the program(s) described herein at any time.
Partial screen shots may be viewed in full within the software version specified.
Microsoft® Windows®, and Microsoft Office® are registered trademarks of the Microsoft Corporation in the U.S.A.
and other countries. This book is not sp ...
BUSINESS INTELLIGENCE AND ANALYTICS RAMESH SHARDA DU.docxjasoninnes20
BUSINESS INTELLIGENCE
AND ANALYTICS
RAMESH SHARDA
DURSUN DELEN
EFRAIM TURBAN
TENTH EDITION
.•
TENTH EDITION
BUSINESS INTELLIGENCE
AND ANALYTICS:
SYSTEMS FOR DECISION SUPPORT
Ramesh Sharda
Oklahoma State University
Dursun Delen
Oklahoma State University
Efraim Turban
University of Hawaii
With contributions by
J.E.Aronson
Tbe University of Georgia
Ting-Peng Liang
National Sun Yat-sen University
David King
]DA Software Group, Inc.
PEARSON
Boston Columbus Indianapolis New York San Francisco Upper Saddle River
Amsterdam Cape Town Dubai London Madrid Milan Munich Paris Montreal Toronto
Delhi Mexico City Sao Paulo Sydney Hong Kong Seoul Singapore Taipei Tokyo
Editor in Chief: Stephanie Wall
Executive Editor: Bob Horan
Program Manager Team Lead: Ashley Santora
Program Manager: Denise Vaughn
Executive Marketing Manager: Anne Fahlgren
Project Manager Team Lead: Judy Leale
Project Manager: Tom Benfatti
Operations Specialist: Michelle Klein
Creative Director: Jayne Conte
Cover Designer: Suzanne Behnke
Digital Production Project Manager: Lisa
Rinaldi
Full-Service Project Management: George Jacob,
Integra Software
Solution
s.
Printer/Binder: Edwards Brothers Malloy-Jackson
Road
Cover Printer: Lehigh/Phoenix-Hagerstown
Text Font: Garamond
Credits and acknowledgments borrowed from other sources and reproduced, with permission, in this textbook
appear on the appropriate page within text.
Microsoft and/ or its respective suppliers make no representations about the suitability of the information
contained in the documents and related graphics published as part of the services for any purpose. All such
documents and related graphics are provided "as is" without warranty of any kind. Microsoft and/or its
respective suppliers hereby disclaim all warranties and conditions with regard to this information, including
all warranties and conditions of merchantability, whether express, implied or statutory, fitness for a particular
purpose, title and non-infringement. In no event shall Microsoft and/or its respective suppliers be liable for
any special, indirect or consequential damages or any damages whatsoever resulting from loss of use, data or
profits, whether in an action of contract, negligence or other tortious action, arising out of or in connection
with the use or performance of information available from the services.
The documents and related graphics contained herein could include technical inaccuracies or typographical
errors. Changes are periodically added to the information herein. Microsoft and/or its respective suppliers may
make improvements and/or changes in the product(s) and/ or the program(s) described herein at any time.
Partial screen shots may be viewed in full within the software version specified.
Microsoft® Windows®, and Microsoft Office® are registered trademarks of the Microsoft Corporation in the U.S.A.
and other countries. This book is not sp ...
Chapter 10 Modeling and Analysis Heuristic Search Methods EstelaJeffery653
Chapter 10: Modeling and Analysis: Heuristic Search
Methods and Simulation
Learning Objectives
• Explain the basic concepts of simulation and when to
use it
• Understand the concepts and applications of different
types of simulation
• Explain what is meant by Monte Carlo and discrete
event simulation
Simulation
• Simulation is the “appearance” of reality
• It is often used to conduct what-if analysis on the
model of the actual system
• It is a popular DSS technique for conducting
experiments with a computer on a comprehensive
model of the system to assess its dynamic behavior
• Often used when the system is too complex for other
DSS techniques
Application Case 10.3
Simulating Effects of Hepatitis B
Interventions
Questions for Discussion
1. Explain the advantage of operations research methods such
as simulation over clinical trial methods in determining the
best control measure for Hepatitis B.
2. In what ways do the decision and Markov models provide
cost-effective ways of combating the disease?
3. Discuss how multidisciplinary background is an asset in
finding a solution for the problem described in the case.
4. Besides healthcare, in what other domain could such a
modeling approach help reduce cost?
Major Characteristics of Simulation
• Imitates reality and captures its richness both in
shape and behavior
• “Represent” versus “Imitate”
• Technique for conducting experiments
• Descriptive, not normative tool
• Often to “solve” [i.e., analyze] very complex
systems/problems
• Simulation should be used only when a numerical
optimization is not possible
Advantages of Simulation
• The theory is fairly straightforward
• Great deal of time compression
• Experiment with different alternatives
• The model reflects manager’s perspective
• Can handle wide variety of problem types
• Can include the real complexities of problems
• Produces important performance measures
• Often it is the only DSS modeling tool for non-structured problems
Disadvantages of Simulation
• Cannot guarantee an optimal solution
• Slow and costly construction process
• Cannot transfer solutions and inferences to solve other problems
(problem specific)
• So easy to explain/sell to managers, may lead to overlooking
analytical solutions
• Software may require special skills
Simulation Methodology
Steps:
1. Define problem 5. Conduct experiments
2. Construct the model 6. Evaluate results
3. Test and validate model 7. Implement solution
4. Design experiments
Simulation Types
• Probabilistic/Stochastic vs. Deterministic Simulation
• Uses probability distributions
• Time-dependent vs. Time-independent Simulation
• Monte Carlo technique (X = A + B)[A, B, and X are all
distributions]
• Discrete Event vs. Continuous Simulation
• Simulation Implementation
• Visual Simulation and/or Object-Oriented Simulation
Visual Interactive Simulation (VIS)
• Visual interactive modeling (VIM), also called Visual
Inte ...
Analytics, Data Science, and Artificial Intelligence, 11th Editi.docxamrit47
Analytics, Data Science, and Artificial Intelligence, 11th Edition.pdf
ANALYTICS, DATA SCIENCE, &
ARTIFICIAL INTELLIGENCE
SYSTEMS FOR DECISION SUPPORT
E L E V E N T H E D I T I O N
Ramesh Sharda
Oklahoma State University
Dursun Delen
Oklahoma State University
Efraim Turban
University of Hawaii
Microsoft and/or its respective suppliers make no representations about the suitability of the information
contained in the documents and related graphics published as part of the services for any purpose. All such
documents and related graphics are provided “as is” without warranty of any kind. Microsoft and/or its respective
suppliers hereby disclaim all warranties and conditions with regard to this information, including all warranties
and conditions of merchantability, whether express, implied or statutory, fitness for a particular purpose, title and
non-infringement. In no event shall Microsoft and/or its respective suppliers be liable for any special, indirect
or consequential damages or any damages whatsoever resulting from loss of use, data or profits, whether in an
action of contract, negligence or other tortious action, arising out of or in connection with the use or performance
of information available from the services. The documents and related graphics contained herein could include
technical inaccuracies or typographical errors. Changes are periodically added to the information herein. Microsoft
and/or its respective suppliers may make improvements and/or changes in the product(s) and/or the program(s)
described herein at any time. Partial screen shots may be viewed in full within the software version specified.
Microsoft® Windows® and Microsoft Office® are registered trademarks of Microsoft Corporation in the U.S.A. and
other countries. This book is not sponsored or endorsed by or affiliated with the Microsoft Corporation.
Vice President of Courseware Portfolio
Management: Andrew Gilfillan
Executive Portfolio Manager: Samantha Lewis
Team Lead, Content Production: Laura Burgess
Content Producer: Faraz Sharique Ali
Portfolio Management Assistant: Bridget Daly
Director of Product Marketing: Brad Parkins
Director of Field Marketing: Jonathan Cottrell
Product Marketing Manager: Heather Taylor
Field Marketing Manager: Bob Nisbet
Product Marketing Assistant: Liz Bennett
Field Marketing Assistant: Derrica Moser
Senior Operations Specialist: Diane Peirano
Senior Art Director: Mary Seiner
Interior and Cover Design: Pearson CSC
Cover Photo: Phonlamai Photo/Shutterstock
Senior Product Model Manager: Eric Hakanson
Manager, Digital Studio: Heather Darby
Course Producer, MyLab MIS: Jaimie Noy
Digital Studio Producer: Tanika Henderson
Full-Service Project Manager: Gowthaman
Sadhanandham
Full Service Vendor: Integra Software Service
Pvt. Ltd.
Manufacturing Buyer: LSC Communications,
Maura Zaldivar-Garcia
Text Printer/Bindery: LSC Communications
Cover Printer: Phoenix Color
ISBN 10: 0-13-519201-3
ISBN 13: 97.
Analytics, Data Science, and Artificial Intelligence, 11th Editi.docxjack60216
Analytics, Data Science, and Artificial Intelligence, 11th Edition.pdf
ANALYTICS, DATA SCIENCE, &
ARTIFICIAL INTELLIGENCE
SYSTEMS FOR DECISION SUPPORT
E L E V E N T H E D I T I O N
Ramesh Sharda
Oklahoma State University
Dursun Delen
Oklahoma State University
Efraim Turban
University of Hawaii
Microsoft and/or its respective suppliers make no representations about the suitability of the information
contained in the documents and related graphics published as part of the services for any purpose. All such
documents and related graphics are provided “as is” without warranty of any kind. Microsoft and/or its respective
suppliers hereby disclaim all warranties and conditions with regard to this information, including all warranties
and conditions of merchantability, whether express, implied or statutory, fitness for a particular purpose, title and
non-infringement. In no event shall Microsoft and/or its respective suppliers be liable for any special, indirect
or consequential damages or any damages whatsoever resulting from loss of use, data or profits, whether in an
action of contract, negligence or other tortious action, arising out of or in connection with the use or performance
of information available from the services. The documents and related graphics contained herein could include
technical inaccuracies or typographical errors. Changes are periodically added to the information herein. Microsoft
and/or its respective suppliers may make improvements and/or changes in the product(s) and/or the program(s)
described herein at any time. Partial screen shots may be viewed in full within the software version specified.
Microsoft® Windows® and Microsoft Office® are registered trademarks of Microsoft Corporation in the U.S.A. and
other countries. This book is not sponsored or endorsed by or affiliated with the Microsoft Corporation.
Vice President of Courseware Portfolio
Management: Andrew Gilfillan
Executive Portfolio Manager: Samantha Lewis
Team Lead, Content Production: Laura Burgess
Content Producer: Faraz Sharique Ali
Portfolio Management Assistant: Bridget Daly
Director of Product Marketing: Brad Parkins
Director of Field Marketing: Jonathan Cottrell
Product Marketing Manager: Heather Taylor
Field Marketing Manager: Bob Nisbet
Product Marketing Assistant: Liz Bennett
Field Marketing Assistant: Derrica Moser
Senior Operations Specialist: Diane Peirano
Senior Art Director: Mary Seiner
Interior and Cover Design: Pearson CSC
Cover Photo: Phonlamai Photo/Shutterstock
Senior Product Model Manager: Eric Hakanson
Manager, Digital Studio: Heather Darby
Course Producer, MyLab MIS: Jaimie Noy
Digital Studio Producer: Tanika Henderson
Full-Service Project Manager: Gowthaman
Sadhanandham
Full Service Vendor: Integra Software Service
Pvt. Ltd.
Manufacturing Buyer: LSC Communications,
Maura Zaldivar-Garcia
Text Printer/Bindery: LSC Communications
Cover Printer: Phoenix Color
ISBN 10: 0-13-519201-3
ISBN 13: 97.
Analytics, Data Science, and Artificial Intelligence, 11th Editi.docxSHIVA101531
Analytics, Data Science, and Artificial Intelligence, 11th Edition.pdf
ANALYTICS, DATA SCIENCE, &
ARTIFICIAL INTELLIGENCE
SYSTEMS FOR DECISION SUPPORT
E L E V E N T H E D I T I O N
Ramesh Sharda
Oklahoma State University
Dursun Delen
Oklahoma State University
Efraim Turban
University of Hawaii
Microsoft and/or its respective suppliers make no representations about the suitability of the information
contained in the documents and related graphics published as part of the services for any purpose. All such
documents and related graphics are provided “as is” without warranty of any kind. Microsoft and/or its respective
suppliers hereby disclaim all warranties and conditions with regard to this information, including all warranties
and conditions of merchantability, whether express, implied or statutory, fitness for a particular purpose, title and
non-infringement. In no event shall Microsoft and/or its respective suppliers be liable for any special, indirect
or consequential damages or any damages whatsoever resulting from loss of use, data or profits, whether in an
action of contract, negligence or other tortious action, arising out of or in connection with the use or performance
of information available from the services. The documents and related graphics contained herein could include
technical inaccuracies or typographical errors. Changes are periodically added to the information herein. Microsoft
and/or its respective suppliers may make improvements and/or changes in the product(s) and/or the program(s)
described herein at any time. Partial screen shots may be viewed in full within the software version specified.
Microsoft® Windows® and Microsoft Office® are registered trademarks of Microsoft Corporation in the U.S.A. and
other countries. This book is not sponsored or endorsed by or affiliated with the Microsoft Corporation.
Vice President of Courseware Portfolio
Management: Andrew Gilfillan
Executive Portfolio Manager: Samantha Lewis
Team Lead, Content Production: Laura Burgess
Content Producer: Faraz Sharique Ali
Portfolio Management Assistant: Bridget Daly
Director of Product Marketing: Brad Parkins
Director of Field Marketing: Jonathan Cottrell
Product Marketing Manager: Heather Taylor
Field Marketing Manager: Bob Nisbet
Product Marketing Assistant: Liz Bennett
Field Marketing Assistant: Derrica Moser
Senior Operations Specialist: Diane Peirano
Senior Art Director: Mary Seiner
Interior and Cover Design: Pearson CSC
Cover Photo: Phonlamai Photo/Shutterstock
Senior Product Model Manager: Eric Hakanson
Manager, Digital Studio: Heather Darby
Course Producer, MyLab MIS: Jaimie Noy
Digital Studio Producer: Tanika Henderson
Full-Service Project Manager: Gowthaman
Sadhanandham
Full Service Vendor: Integra Software Service
Pvt. Ltd.
Manufacturing Buyer: LSC Communications,
Maura Zaldivar-Garcia
Text Printer/Bindery: LSC Communications
Cover Printer: Phoenix Color
ISBN 10: 0-13-519201-3
ISBN 13: 97.
Analytics, Data Science, and Artificial Intelligence, 11th Editi.docxdaniahendric
Analytics, Data Science, and Artificial Intelligence, 11th Edition.pdf
ANALYTICS, DATA SCIENCE, &
ARTIFICIAL INTELLIGENCE
SYSTEMS FOR DECISION SUPPORT
E L E V E N T H E D I T I O N
Ramesh Sharda
Oklahoma State University
Dursun Delen
Oklahoma State University
Efraim Turban
University of Hawaii
Microsoft and/or its respective suppliers make no representations about the suitability of the information
contained in the documents and related graphics published as part of the services for any purpose. All such
documents and related graphics are provided “as is” without warranty of any kind. Microsoft and/or its respective
suppliers hereby disclaim all warranties and conditions with regard to this information, including all warranties
and conditions of merchantability, whether express, implied or statutory, fitness for a particular purpose, title and
non-infringement. In no event shall Microsoft and/or its respective suppliers be liable for any special, indirect
or consequential damages or any damages whatsoever resulting from loss of use, data or profits, whether in an
action of contract, negligence or other tortious action, arising out of or in connection with the use or performance
of information available from the services. The documents and related graphics contained herein could include
technical inaccuracies or typographical errors. Changes are periodically added to the information herein. Microsoft
and/or its respective suppliers may make improvements and/or changes in the product(s) and/or the program(s)
described herein at any time. Partial screen shots may be viewed in full within the software version specified.
Microsoft® Windows® and Microsoft Office® are registered trademarks of Microsoft Corporation in the U.S.A. and
other countries. This book is not sponsored or endorsed by or affiliated with the Microsoft Corporation.
Vice President of Courseware Portfolio
Management: Andrew Gilfillan
Executive Portfolio Manager: Samantha Lewis
Team Lead, Content Production: Laura Burgess
Content Producer: Faraz Sharique Ali
Portfolio Management Assistant: Bridget Daly
Director of Product Marketing: Brad Parkins
Director of Field Marketing: Jonathan Cottrell
Product Marketing Manager: Heather Taylor
Field Marketing Manager: Bob Nisbet
Product Marketing Assistant: Liz Bennett
Field Marketing Assistant: Derrica Moser
Senior Operations Specialist: Diane Peirano
Senior Art Director: Mary Seiner
Interior and Cover Design: Pearson CSC
Cover Photo: Phonlamai Photo/Shutterstock
Senior Product Model Manager: Eric Hakanson
Manager, Digital Studio: Heather Darby
Course Producer, MyLab MIS: Jaimie Noy
Digital Studio Producer: Tanika Henderson
Full-Service Project Manager: Gowthaman
Sadhanandham
Full Service Vendor: Integra Software Service
Pvt. Ltd.
Manufacturing Buyer: LSC Communications,
Maura Zaldivar-Garcia
Text Printer/Bindery: LSC Communications
Cover Printer: Phoenix Color
ISBN 10: 0-13-519201-3
ISBN 13: 97 ...
Analytics, Data Science, and Artificial Intelligence, 11th Editi.docxgreg1eden90113
Analytics, Data Science, and Artificial Intelligence, 11th Edition.pdf
ANALYTICS, DATA SCIENCE, &
ARTIFICIAL INTELLIGENCE
SYSTEMS FOR DECISION SUPPORT
E L E V E N T H E D I T I O N
Ramesh Sharda
Oklahoma State University
Dursun Delen
Oklahoma State University
Efraim Turban
University of Hawaii
Microsoft and/or its respective suppliers make no representations about the suitability of the information
contained in the documents and related graphics published as part of the services for any purpose. All such
documents and related graphics are provided “as is” without warranty of any kind. Microsoft and/or its respective
suppliers hereby disclaim all warranties and conditions with regard to this information, including all warranties
and conditions of merchantability, whether express, implied or statutory, fitness for a particular purpose, title and
non-infringement. In no event shall Microsoft and/or its respective suppliers be liable for any special, indirect
or consequential damages or any damages whatsoever resulting from loss of use, data or profits, whether in an
action of contract, negligence or other tortious action, arising out of or in connection with the use or performance
of information available from the services. The documents and related graphics contained herein could include
technical inaccuracies or typographical errors. Changes are periodically added to the information herein. Microsoft
and/or its respective suppliers may make improvements and/or changes in the product(s) and/or the program(s)
described herein at any time. Partial screen shots may be viewed in full within the software version specified.
Microsoft® Windows® and Microsoft Office® are registered trademarks of Microsoft Corporation in the U.S.A. and
other countries. This book is not sponsored or endorsed by or affiliated with the Microsoft Corporation.
Vice President of Courseware Portfolio
Management: Andrew Gilfillan
Executive Portfolio Manager: Samantha Lewis
Team Lead, Content Production: Laura Burgess
Content Producer: Faraz Sharique Ali
Portfolio Management Assistant: Bridget Daly
Director of Product Marketing: Brad Parkins
Director of Field Marketing: Jonathan Cottrell
Product Marketing Manager: Heather Taylor
Field Marketing Manager: Bob Nisbet
Product Marketing Assistant: Liz Bennett
Field Marketing Assistant: Derrica Moser
Senior Operations Specialist: Diane Peirano
Senior Art Director: Mary Seiner
Interior and Cover Design: Pearson CSC
Cover Photo: Phonlamai Photo/Shutterstock
Senior Product Model Manager: Eric Hakanson
Manager, Digital Studio: Heather Darby
Course Producer, MyLab MIS: Jaimie Noy
Digital Studio Producer: Tanika Henderson
Full-Service Project Manager: Gowthaman
Sadhanandham
Full Service Vendor: Integra Software Service
Pvt. Ltd.
Manufacturing Buyer: LSC Communications,
Maura Zaldivar-Garcia
Text Printer/Bindery: LSC Communications
Cover Printer: Phoenix Color
ISBN 10: 0-13-519201-3
ISBN 13: 97.
Analytics, Data Science, and Artificial Intelligence, 11th Editi.docxrossskuddershamus
Analytics, Data Science, and Artificial Intelligence, 11th Edition.pdf
ANALYTICS, DATA SCIENCE, &
ARTIFICIAL INTELLIGENCE
SYSTEMS FOR DECISION SUPPORT
E L E V E N T H E D I T I O N
Ramesh Sharda
Oklahoma State University
Dursun Delen
Oklahoma State University
Efraim Turban
University of Hawaii
Microsoft and/or its respective suppliers make no representations about the suitability of the information
contained in the documents and related graphics published as part of the services for any purpose. All such
documents and related graphics are provided “as is” without warranty of any kind. Microsoft and/or its respective
suppliers hereby disclaim all warranties and conditions with regard to this information, including all warranties
and conditions of merchantability, whether express, implied or statutory, fitness for a particular purpose, title and
non-infringement. In no event shall Microsoft and/or its respective suppliers be liable for any special, indirect
or consequential damages or any damages whatsoever resulting from loss of use, data or profits, whether in an
action of contract, negligence or other tortious action, arising out of or in connection with the use or performance
of information available from the services. The documents and related graphics contained herein could include
technical inaccuracies or typographical errors. Changes are periodically added to the information herein. Microsoft
and/or its respective suppliers may make improvements and/or changes in the product(s) and/or the program(s)
described herein at any time. Partial screen shots may be viewed in full within the software version specified.
Microsoft® Windows® and Microsoft Office® are registered trademarks of Microsoft Corporation in the U.S.A. and
other countries. This book is not sponsored or endorsed by or affiliated with the Microsoft Corporation.
Vice President of Courseware Portfolio
Management: Andrew Gilfillan
Executive Portfolio Manager: Samantha Lewis
Team Lead, Content Production: Laura Burgess
Content Producer: Faraz Sharique Ali
Portfolio Management Assistant: Bridget Daly
Director of Product Marketing: Brad Parkins
Director of Field Marketing: Jonathan Cottrell
Product Marketing Manager: Heather Taylor
Field Marketing Manager: Bob Nisbet
Product Marketing Assistant: Liz Bennett
Field Marketing Assistant: Derrica Moser
Senior Operations Specialist: Diane Peirano
Senior Art Director: Mary Seiner
Interior and Cover Design: Pearson CSC
Cover Photo: Phonlamai Photo/Shutterstock
Senior Product Model Manager: Eric Hakanson
Manager, Digital Studio: Heather Darby
Course Producer, MyLab MIS: Jaimie Noy
Digital Studio Producer: Tanika Henderson
Full-Service Project Manager: Gowthaman
Sadhanandham
Full Service Vendor: Integra Software Service
Pvt. Ltd.
Manufacturing Buyer: LSC Communications,
Maura Zaldivar-Garcia
Text Printer/Bindery: LSC Communications
Cover Printer: Phoenix Color
ISBN 10: 0-13-519201-3
ISBN 13: 97.
Could you increase your knowledge—and raise your grade—i.docxfaithxdunce63732
Could you increase your knowledge—
and raise your grade—if you…
…used an online tutorial that assisted you with Access
and Excel skills mapped to this book?
…learned to use Microsoft’s SharePoint, the number one
organizational tool for file sharing and collaboration?
…had flashcards and student PowerPoints
to prepare for lectures?
Visit , a valuable tool
for your student success and your
business career.
www.myMISlab.com
www.myMISlab.com
INTEGRATING BUSINESS WITH TECHNOLOGY
By completing the projects in this text, students will be able to demonstrate business knowledge, application
software proficiency, and Internet skills.These projects can be used by instructors as learning assessment tools
and by students as demonstrations of business, software, and problem-solving skills to future employers. Here
are some of the skills and competencies students using this text will be able to demonstrate:
Business Application skills: Use of both business and software skills in real-world business applications.
Demonstrates both business knowledge and proficiency in spreadsheet, database, and Web page/blog creation
tools.
Internet skills: Ability to use Internet tools to access information, conduct research, or perform online
calculations and analysis.
Analytical, writing and presentation skills: Ability to research a specific topic, analyze a problem, think
creatively, suggest a solution, and prepare a clear written or oral presentation of the solution, working either
individually or with others in a group.
Business Application Skills
BUSINESS SKILLS
Finance and Accounting
Financial statement analysis
Pricing hardware anrj software
Technology rent vs. buy decision
Total Cost of Ownership (TCO) analysis
Analyzing telecommunications services anrj costs
Risk assessment
Retirement planning
Capital budgeting
Human Resources
Employee training and skills tracking
Job posting database and Web page
Manufacturing and Production
Analyzing supplier performance and pricing
Inventory management
Bill of materials cost sensitivity analysis
Sales and Marketing
Sales trend analysis
SOFTWARE SKILLS
Spreadsheet charts
Spreadsheet formulas
Spreadsheet downloading and formatting
Spreadsheet formulas
Spreadsheet formulas
Spreadsheet formulas
Spreadsheet charts and formulas
Spreadsheet formulas and logical functions
Spreadsheet formulas
Database design
Database querying and reporting
Database design
Web page design and creation
Spreadsheet date functions
Database functions
Data filtering
Importing data into a database
Database querying and reporting
Spreadsheet data tables
Spreadsheet formulas
Database querying and reporting
CHAPTER
Chapter 2*
Chapter 10
Chapter 5
Chapter 5*
Chapter 7
Chapter 8
Chapter 11
Chapter 14
Chapter 14*
Chapter 13*
Chapter 15
Chapter 2
Chapter 6
Chapter 12*
Chapter 1
Customer reservation system
Improving marketing decisions
Customer profiling
Customer service analysis
Sales lead and.
2
2
2
1
1
1
Organization Name: Insta-Buy
Insta-Buy is an E-Commerce Multinational American company. It was founded in 2010 and is based in Atlanta, Georgia. It mainly operates with grocery delivery and pick up and it offers services through web application and mobile application to various states in United States. It is one of the major online marketplaces for grocery delivery. The company is valued at $1 billion worth and has partnership with over 150 retailers. It is known for its fresh produce and timely delivery and pickup.
Predictive Analysis at Insta-Buy:
The predictive analytics is termed as what is likely to happen in the future. The predictive analytics is based on statistical and data mining technique. The aim of this technique is to predict the future of the project such as what would be the customer reaction on project, financial need, etc. In developing predictive analytical application, a number of techniques are used such as classification algorithms. The classification techniques are logistic regression, decision tree models and neural network. Clustering algorithms are used to segment customers in different groups which helps to target specific promotions to them. To estimate the relationship between different purchasing behavior, association mining technique is used (Mehra, 2014). As an example, for any product on Amazon.com results in the retailer also suggesting similar products that a customer might be interested in. Predictive analytics can be used in E-commerce to solve the following problems
1. Improve customer engagement and increase revenue
1. Launch promotions that target specific customer group
1. Optimizing prices to generate maximum profits
1. Keep proper inventory and reduce over stalking
1. Minimizing fraud happenings and protecting privacy
1. Provide batter customer service at low cost
1. Analyze data and make decision in real time
TOPICS:
Student: Ahmed
Topic: Bayesian Networks (Predicting Sales In E-commerce Using Bayesian Network Model)
Student: Meet
Topic: Predictive Analysis
Student: Peter
Topic: Privacy and Confidentiality in an e-Commerce World: Data Mining, Data Warehousing, Matching and Disclosure Limitation
Student: Nayeem
Topic: Ensemble Modeling
Student: Shek
Topic: L.Jack & Y.D. Tsai, Using Text Mining of Amazon Reviews to Explore User-Defined Product Highlights and Issues.
Student: Suma
Topic: Deep Neural Networks
REFERENCES:
Olufunke Rebecca Vincent, A. S. (2017). A Cognitive Buying Decision-Making Process in B2B E-Commerce Using Analytic-MLP. Elsevier.
https://www.researchgate.net/publication/319278239_A_Cognitive_Buying_Decision-Making_Process_in_B2B_E-Commerce_Using_Analytic-MLP
Wan, C. C. (2017). Forcasting E-commerce Key Performance Indicators
https://beta.vu.nl/nl/Images/stageverslag-wan_tcm235-867619.pdf
Fienberg, S. (2006). Privacy and Confidentiality in an e-Commerce World: Data Mining, Data Warehousing, Matching and Disclosure Limitation. Statistical Science, .
Presentation to Analytics Network of the OR Society Nov 2020Paul Laughlin
Presentation on 'The Softer Skills that Analysts need' presented by Paul Laughlin at a virtual event run for the Analytics Network group within the UK OR Society. Exploring Paul's 9 Step Model for effective analysis & explaining how Softer Skills are essential throughout that workflow.
Agile Mumbai 2022
Real-Time Insights and AI for better Products, Customer experience and Resilient Platform
Balvinder Kaur
Principal Consultant, Thoughtworks
Sushant Joshi
Product Manager, Thoughtworks
The Softer Skills that analysts need (beyond Data Visualisation)Paul Laughlin
A talk I gave at #DataVizLive online event in Nov 2020. Introducing the Laughlin Consultancy 9-step model for Softer Skills needed by Analysts & previewing some of those steps (beyond data visualisation & storytelling skills).
A fresh new experience
Project offers a redesigned user experience that is simple and intuitive. Teams can quickly add new members and set up tasks, and then easily switch between grids, boards, or timeline (Gantt) charts to track progress. And because Project is part of the Microsoft 365 family, project teams can save time and do more with built-in connections to familiar apps like Microsoft Teams and Office.
Animated image of a timeline being worked on in Microsoft Project.
Collaboration made easy
Designed to do much more than just track progress, Project works with Teams to support collaboration and make it easy to manage all aspects of a team project, including file sharing, chats, meetings, and much more. Team members in scattered locations can even edit tasks simultaneously, so they can get more done together, no matter where they are. To help teams stay on track, Project offers an automated scheduling engine based on effort, duration, and resources.
A presentation delivered by Robert Brooks at the Police Foundation's annual conference 'Policing and Justice for a Digital Age' (December 2016) on using big data and predictive analysis.
Are you getting the most out of your data?SAS Canada
Data is an organizations most valuable asset, but raw data by itself has little value. To drive data’s worth, it must be managed and processed to extract value and information that decision makers can leverage and turn into actionable insights. It is the ways in which a company choses to put that information to use that will determine the true value of its data.
Through business intelligence and business analytic tools, businesses are enabling themselves to make more strategic, accurate decisions, while optimizing business processes. Hear from Info-Tech Research Group and learn what you need to consider when choosing an analytics solution provider. The webinar will highlight Info-Tech Research Group’s recently published vendor landscape for selecting and implementing Business Intelligence and Business Analytics solutions. The report positions SAS as the only leader across all four categories of Enterprise BI, Mid-Market BI, Enterprise BA and Mid-Market BA.
Childhood Abuse and Delinquency 150 Words Research regarding.docxTawnaDelatorrejs
Childhood Abuse and Delinquency 150 Words
Research regarding spanking children has had mixed results, do you think spanking contributes to delinquency or helps to prevent it? Justify your response.
Please remember to use netiquette when responding to your classmates
.
Childrens StoryKnowing how to address a variety of situations in .docxTawnaDelatorrejs
Children's Story
Knowing how to address a variety of situations in the early childhood setting and effectively partnering with parents to do so are important skills for all teachers and caregivers. For this assignment, you will choose one of the following scenarios:
Shane has a difficult time separating from his mother each morning. At drop off, he clings to her and screams uncontrollably. After she leaves, Shane continues to scream and cry until you are able to soothe him.
Lisa often gets frustrated when trying to play with other children. She takes toys from their hands and even hits children with the toys.
Next, address each of the following points according to the teaching approach/setting that best reflects your style in your desired classroom setting (e.g. Montessori, Reggio Emilia, Waldorf, traditional preschool, etc.):
Outline a specific plan for addressing the discipline or guidance scenario.
Explain how your plan would support the teaching approach/setting.
Describe how you will create an effective partnership with parents to address the discipline or guidance scenario.
Describe one or two possible obstacles you might encounter when implementing your plan.
Discuss how you will address these obstacles.
The paper should be three to four pages in addition to the title page and the reference page. Use at least two scholarly sources in addition to your text. Your paper should also be formatted according to APA style as outlined in the Ashford Writing Center.
Description
:
Total Possible Score
: 6.00
Outlines a Specific Plan for Addressing the Discipline or Guidance Scenario
Total: 1.25
Distinguished - Outlines in detail a specific plan for addressing the discipline or guidance scenario. The plan is well supported by scholarly sources.
Proficient - Outlines a specific plan for addressing the discipline or guidance scenario. The plan is supported by scholarly sources but is missing minor details.
Basic - Vaguely outlines a plan for addressing the discipline or guidance scenario; however, the plan may not be sufficiently supported by scholarly sources and is missing relevant details.
Below Expectations - Attempts to outline a plan for addressing the scenario; however, the plan is not sufficiently supported by scholarly sources and is missing significant details.
Non-Performance - The outline of a specific plan is either nonexistent or lacks the components described in the assignment instructions.
Explains How the Plan Supports the Teaching Approach/Setting
Total: 0.50
Distinguished - Clearly and comprehensively explains how the plan supports the chosen teaching approach/setting. The explanation is well supported by scholarly sources.
Proficient - Explains how the plan supports the chosen teaching approach/setting. The explanation is supported by scholarly sources but is slightly underdeveloped.
Basic - Briefly explains how the plan supports the chosen teaching approach/setting. The explanation may not be sufficiently supported by s.
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ANALYTICS, DATA SCIENCE, &
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Microsoft and/or its respective suppliers make no representations about the suitability of the information
contained in the documents and related graphics published as part of the services for any purpose. All such
documents and related graphics are provided “as is” without warranty of any kind. Microsoft and/or its respective
suppliers hereby disclaim all warranties and conditions with regard to this information, including all warranties
and conditions of merchantability, whether express, implied or statutory, fitness for a particular purpose, title and
non-infringement. In no event shall Microsoft and/or its respective suppliers be liable for any special, indirect
or consequential damages or any damages whatsoever resulting from loss of use, data or profits, whether in an
action of contract, negligence or other tortious action, arising out of or in connection with the use or performance
of information available from the services. The documents and related graphics contained herein could include
technical inaccuracies or typographical errors. Changes are periodically added to the information herein. Microsoft
and/or its respective suppliers may make improvements and/or changes in the product(s) and/or the program(s)
described herein at any time. Partial screen shots may be viewed in full within the software version specified.
Microsoft® Windows® and Microsoft Office® are registered trademarks of Microsoft Corporation in the U.S.A. and
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Vice President of Courseware Portfolio
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ANALYTICS, DATA SCIENCE, &
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documents and related graphics are provided “as is” without warranty of any kind. Microsoft and/or its respective
suppliers hereby disclaim all warranties and conditions with regard to this information, including all warranties
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of information available from the services. The documents and related graphics contained herein could include
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and/or its respective suppliers may make improvements and/or changes in the product(s) and/or the program(s)
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documents and related graphics are provided “as is” without warranty of any kind. Microsoft and/or its respective
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technical inaccuracies or typographical errors. Changes are periodically added to the information herein. Microsoft
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suppliers hereby disclaim all warranties and conditions with regard to this information, including all warranties
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ANALYTICS, DATA SCIENCE, &
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SYSTEMS FOR DECISION SUPPORT
E L E V E N T H E D I T I O N
Ramesh Sharda
Oklahoma State University
Dursun Delen
Oklahoma State University
Efraim Turban
University of Hawaii
Microsoft and/or its respective suppliers make no representations about the suitability of the information
contained in the documents and related graphics published as part of the services for any purpose. All such
documents and related graphics are provided “as is” without warranty of any kind. Microsoft and/or its respective
suppliers hereby disclaim all warranties and conditions with regard to this information, including all warranties
and conditions of merchantability, whether express, implied or statutory, fitness for a particular purpose, title and
non-infringement. In no event shall Microsoft and/or its respective suppliers be liable for any special, indirect
or consequential damages or any damages whatsoever resulting from loss of use, data or profits, whether in an
action of contract, negligence or other tortious action, arising out of or in connection with the use or performance
of information available from the services. The documents and related graphics contained herein could include
technical inaccuracies or typographical errors. Changes are periodically added to the information herein. Microsoft
and/or its respective suppliers may make improvements and/or changes in the product(s) and/or the program(s)
described herein at any time. Partial screen shots may be viewed in full within the software version specified.
Microsoft® Windows® and Microsoft Office® are registered trademarks of Microsoft Corporation in the U.S.A. and
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Vice President of Courseware Portfolio
Management: Andrew Gilfillan
Executive Portfolio Manager: Samantha Lewis
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Portfolio Management Assistant: Bridget Daly
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ANALYTICS, DATA SCIENCE, &
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SYSTEMS FOR DECISION SUPPORT
E L E V E N T H E D I T I O N
Ramesh Sharda
Oklahoma State University
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Oklahoma State University
Efraim Turban
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Microsoft and/or its respective suppliers make no representations about the suitability of the information
contained in the documents and related graphics published as part of the services for any purpose. All such
documents and related graphics are provided “as is” without warranty of any kind. Microsoft and/or its respective
suppliers hereby disclaim all warranties and conditions with regard to this information, including all warranties
and conditions of merchantability, whether express, implied or statutory, fitness for a particular purpose, title and
non-infringement. In no event shall Microsoft and/or its respective suppliers be liable for any special, indirect
or consequential damages or any damages whatsoever resulting from loss of use, data or profits, whether in an
action of contract, negligence or other tortious action, arising out of or in connection with the use or performance
of information available from the services. The documents and related graphics contained herein could include
technical inaccuracies or typographical errors. Changes are periodically added to the information herein. Microsoft
and/or its respective suppliers may make improvements and/or changes in the product(s) and/or the program(s)
described herein at any time. Partial screen shots may be viewed in full within the software version specified.
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Team Lead, Content Production: Laura Burgess
Content Producer: Faraz Sharique Ali
Portfolio Management Assistant: Bridget Daly
Director of Product Marketing: Brad Parkins
Director of Field Marketing: Jonathan Cottrell
Product Marketing Manager: Heather Taylor
Field Marketing Manager: Bob Nisbet
Product Marketing Assistant: Liz Bennett
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Senior Operations Specialist: Diane Peirano
Senior Art Director: Mary Seiner
Interior and Cover Design: Pearson CSC
Cover Photo: Phonlamai Photo/Shutterstock
Senior Product Model Manager: Eric Hakanson
Manager, Digital Studio: Heather Darby
Course Producer, MyLab MIS: Jaimie Noy
Digital Studio Producer: Tanika Henderson
Full-Service Project Manager: Gowthaman
Sadhanandham
Full Service Vendor: Integra Software Service
Pvt. Ltd.
Manufacturing Buyer: LSC Communications,
Maura Zaldivar-Garcia
Text Printer/Bindery: LSC Communications
Cover Printer: Phoenix Color
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Could you increase your knowledge—and raise your grade—i.docxfaithxdunce63732
Could you increase your knowledge—
and raise your grade—if you…
…used an online tutorial that assisted you with Access
and Excel skills mapped to this book?
…learned to use Microsoft’s SharePoint, the number one
organizational tool for file sharing and collaboration?
…had flashcards and student PowerPoints
to prepare for lectures?
Visit , a valuable tool
for your student success and your
business career.
www.myMISlab.com
www.myMISlab.com
INTEGRATING BUSINESS WITH TECHNOLOGY
By completing the projects in this text, students will be able to demonstrate business knowledge, application
software proficiency, and Internet skills.These projects can be used by instructors as learning assessment tools
and by students as demonstrations of business, software, and problem-solving skills to future employers. Here
are some of the skills and competencies students using this text will be able to demonstrate:
Business Application skills: Use of both business and software skills in real-world business applications.
Demonstrates both business knowledge and proficiency in spreadsheet, database, and Web page/blog creation
tools.
Internet skills: Ability to use Internet tools to access information, conduct research, or perform online
calculations and analysis.
Analytical, writing and presentation skills: Ability to research a specific topic, analyze a problem, think
creatively, suggest a solution, and prepare a clear written or oral presentation of the solution, working either
individually or with others in a group.
Business Application Skills
BUSINESS SKILLS
Finance and Accounting
Financial statement analysis
Pricing hardware anrj software
Technology rent vs. buy decision
Total Cost of Ownership (TCO) analysis
Analyzing telecommunications services anrj costs
Risk assessment
Retirement planning
Capital budgeting
Human Resources
Employee training and skills tracking
Job posting database and Web page
Manufacturing and Production
Analyzing supplier performance and pricing
Inventory management
Bill of materials cost sensitivity analysis
Sales and Marketing
Sales trend analysis
SOFTWARE SKILLS
Spreadsheet charts
Spreadsheet formulas
Spreadsheet downloading and formatting
Spreadsheet formulas
Spreadsheet formulas
Spreadsheet formulas
Spreadsheet charts and formulas
Spreadsheet formulas and logical functions
Spreadsheet formulas
Database design
Database querying and reporting
Database design
Web page design and creation
Spreadsheet date functions
Database functions
Data filtering
Importing data into a database
Database querying and reporting
Spreadsheet data tables
Spreadsheet formulas
Database querying and reporting
CHAPTER
Chapter 2*
Chapter 10
Chapter 5
Chapter 5*
Chapter 7
Chapter 8
Chapter 11
Chapter 14
Chapter 14*
Chapter 13*
Chapter 15
Chapter 2
Chapter 6
Chapter 12*
Chapter 1
Customer reservation system
Improving marketing decisions
Customer profiling
Customer service analysis
Sales lead and.
2
2
2
1
1
1
Organization Name: Insta-Buy
Insta-Buy is an E-Commerce Multinational American company. It was founded in 2010 and is based in Atlanta, Georgia. It mainly operates with grocery delivery and pick up and it offers services through web application and mobile application to various states in United States. It is one of the major online marketplaces for grocery delivery. The company is valued at $1 billion worth and has partnership with over 150 retailers. It is known for its fresh produce and timely delivery and pickup.
Predictive Analysis at Insta-Buy:
The predictive analytics is termed as what is likely to happen in the future. The predictive analytics is based on statistical and data mining technique. The aim of this technique is to predict the future of the project such as what would be the customer reaction on project, financial need, etc. In developing predictive analytical application, a number of techniques are used such as classification algorithms. The classification techniques are logistic regression, decision tree models and neural network. Clustering algorithms are used to segment customers in different groups which helps to target specific promotions to them. To estimate the relationship between different purchasing behavior, association mining technique is used (Mehra, 2014). As an example, for any product on Amazon.com results in the retailer also suggesting similar products that a customer might be interested in. Predictive analytics can be used in E-commerce to solve the following problems
1. Improve customer engagement and increase revenue
1. Launch promotions that target specific customer group
1. Optimizing prices to generate maximum profits
1. Keep proper inventory and reduce over stalking
1. Minimizing fraud happenings and protecting privacy
1. Provide batter customer service at low cost
1. Analyze data and make decision in real time
TOPICS:
Student: Ahmed
Topic: Bayesian Networks (Predicting Sales In E-commerce Using Bayesian Network Model)
Student: Meet
Topic: Predictive Analysis
Student: Peter
Topic: Privacy and Confidentiality in an e-Commerce World: Data Mining, Data Warehousing, Matching and Disclosure Limitation
Student: Nayeem
Topic: Ensemble Modeling
Student: Shek
Topic: L.Jack & Y.D. Tsai, Using Text Mining of Amazon Reviews to Explore User-Defined Product Highlights and Issues.
Student: Suma
Topic: Deep Neural Networks
REFERENCES:
Olufunke Rebecca Vincent, A. S. (2017). A Cognitive Buying Decision-Making Process in B2B E-Commerce Using Analytic-MLP. Elsevier.
https://www.researchgate.net/publication/319278239_A_Cognitive_Buying_Decision-Making_Process_in_B2B_E-Commerce_Using_Analytic-MLP
Wan, C. C. (2017). Forcasting E-commerce Key Performance Indicators
https://beta.vu.nl/nl/Images/stageverslag-wan_tcm235-867619.pdf
Fienberg, S. (2006). Privacy and Confidentiality in an e-Commerce World: Data Mining, Data Warehousing, Matching and Disclosure Limitation. Statistical Science, .
Presentation to Analytics Network of the OR Society Nov 2020Paul Laughlin
Presentation on 'The Softer Skills that Analysts need' presented by Paul Laughlin at a virtual event run for the Analytics Network group within the UK OR Society. Exploring Paul's 9 Step Model for effective analysis & explaining how Softer Skills are essential throughout that workflow.
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A talk I gave at #DataVizLive online event in Nov 2020. Introducing the Laughlin Consultancy 9-step model for Softer Skills needed by Analysts & previewing some of those steps (beyond data visualisation & storytelling skills).
A fresh new experience
Project offers a redesigned user experience that is simple and intuitive. Teams can quickly add new members and set up tasks, and then easily switch between grids, boards, or timeline (Gantt) charts to track progress. And because Project is part of the Microsoft 365 family, project teams can save time and do more with built-in connections to familiar apps like Microsoft Teams and Office.
Animated image of a timeline being worked on in Microsoft Project.
Collaboration made easy
Designed to do much more than just track progress, Project works with Teams to support collaboration and make it easy to manage all aspects of a team project, including file sharing, chats, meetings, and much more. Team members in scattered locations can even edit tasks simultaneously, so they can get more done together, no matter where they are. To help teams stay on track, Project offers an automated scheduling engine based on effort, duration, and resources.
A presentation delivered by Robert Brooks at the Police Foundation's annual conference 'Policing and Justice for a Digital Age' (December 2016) on using big data and predictive analysis.
Are you getting the most out of your data?SAS Canada
Data is an organizations most valuable asset, but raw data by itself has little value. To drive data’s worth, it must be managed and processed to extract value and information that decision makers can leverage and turn into actionable insights. It is the ways in which a company choses to put that information to use that will determine the true value of its data.
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Childhood Abuse and Delinquency 150 Words
Research regarding spanking children has had mixed results, do you think spanking contributes to delinquency or helps to prevent it? Justify your response.
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Children's Story
Knowing how to address a variety of situations in the early childhood setting and effectively partnering with parents to do so are important skills for all teachers and caregivers. For this assignment, you will choose one of the following scenarios:
Shane has a difficult time separating from his mother each morning. At drop off, he clings to her and screams uncontrollably. After she leaves, Shane continues to scream and cry until you are able to soothe him.
Lisa often gets frustrated when trying to play with other children. She takes toys from their hands and even hits children with the toys.
Next, address each of the following points according to the teaching approach/setting that best reflects your style in your desired classroom setting (e.g. Montessori, Reggio Emilia, Waldorf, traditional preschool, etc.):
Outline a specific plan for addressing the discipline or guidance scenario.
Explain how your plan would support the teaching approach/setting.
Describe how you will create an effective partnership with parents to address the discipline or guidance scenario.
Describe one or two possible obstacles you might encounter when implementing your plan.
Discuss how you will address these obstacles.
The paper should be three to four pages in addition to the title page and the reference page. Use at least two scholarly sources in addition to your text. Your paper should also be formatted according to APA style as outlined in the Ashford Writing Center.
Description
:
Total Possible Score
: 6.00
Outlines a Specific Plan for Addressing the Discipline or Guidance Scenario
Total: 1.25
Distinguished - Outlines in detail a specific plan for addressing the discipline or guidance scenario. The plan is well supported by scholarly sources.
Proficient - Outlines a specific plan for addressing the discipline or guidance scenario. The plan is supported by scholarly sources but is missing minor details.
Basic - Vaguely outlines a plan for addressing the discipline or guidance scenario; however, the plan may not be sufficiently supported by scholarly sources and is missing relevant details.
Below Expectations - Attempts to outline a plan for addressing the scenario; however, the plan is not sufficiently supported by scholarly sources and is missing significant details.
Non-Performance - The outline of a specific plan is either nonexistent or lacks the components described in the assignment instructions.
Explains How the Plan Supports the Teaching Approach/Setting
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Distinguished - Clearly and comprehensively explains how the plan supports the chosen teaching approach/setting. The explanation is well supported by scholarly sources.
Proficient - Explains how the plan supports the chosen teaching approach/setting. The explanation is supported by scholarly sources but is slightly underdeveloped.
Basic - Briefly explains how the plan supports the chosen teaching approach/setting. The explanation may not be sufficiently supported by s.
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Children build their identities based on what they are exposed to, as well as how adults and peers interact with them. After having read this Module's materials, let's discuss this further.
What do you think are the most influential factors in the building of multicultural identities in children?
How do you raise children to be sensitive, multicultural adults
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Child poverty and homelessness are two of the most complex problems .docxTawnaDelatorrejs
Child poverty and homelessness are two of the most complex problems faced by society today. Since 2000, the number of children living in poverty has increased from 11.6 million to 15 million. Today, over 20% of all children live in families with incomes below the federal poverty level. In addition, it is estimated that 1% to 2% of children are homeless, a number that has surged as a result of the recent global recession and the ensuing financial strain it has placed on many families. Because growing up in poverty increases children’s risks of suffering physical, cognitive, emotional, and social problems, reducing rates of child poverty is a priority. However, politicians and policymakers often disagree on causes and solutions to child poverty, sparking vigorous debate. In this Discussion, you will consider your own thoughts on how child poverty might be addressed. Reflect on the following:
Based on what you have learned this week and your past experiences, what specific policies, initiatives, or programs do you think should be implemented to effectively reduce child poverty/homelessness and/or ameliorate its consequences? Consider at least three.
How and to what extent should technology/media be used for educational purposes? For example, should teachers integrate technology as much as possible in their lessons? Should parents encourage children to study using educational software and the Internet? Or are more traditional learning methods preferable?
Are there any policies, initiatives, or programs aimed at combating child poverty and/or homelessness with which you strongly disagree? Why?
Article:
Southwell, P. (2009). The measurement of child poverty in the United States.
Journal of Human Behavior in the Social Environment
,
19
(4), 317
–
329.
Retrieved from the Academic Search Complete database.
Web Resource:
Moore, K. A., Redd, Z., Burkhauser, M., Mbwana, K., & Collins, A. (2009, April).
Children in poverty: Trends, consequences, and policy options
(Publication No. 2009-11). Retrieved from the Child Trends website:
http://www.childtrends.org/wp-content/uploads/2013/03/PovertyRB.pdf
Web Resource:
Valladares, S., & Moore, K. A. (2009, May).
The strengths of poor families
(Publication No. 2009-26). Retrieved from the Child Trends website:
http://childtrends.org/wp-content/uploads/2009/05/Child_Trends-2009_5_14_RB_poorfamstrengths.pdf
.
Child abuse and neglect are critical issues inherent in the field of.docxTawnaDelatorrejs
Child abuse and neglect are critical issues inherent in the field of human services. You will likely encounter clients who are abused and neglected. Review the characteristics of neglected children in Chapter 4, and answer the following questions:
How does the presence of child abuse or neglect affect a child’s normal development?
How might you respond to a child who indicates that he or she is being abused or neglected?
What agencies would you contact and why?
.
Check.DescriptionI need help with this one-page essay Please!Co.docxTawnaDelatorrejs
Check.
Description:
I need help with this one-page essay Please!Compare and contrast the postcolonial elements that define the works of a range of world authors, including Derek Walcott, Chinua Achebe, Deepika Bahri, W.B. Yeats, Seamus Heaney, E. M. Forster, Salman Rushdie, and Arundhati Roy.
.
Check the paper you write and add your perspective I forgot to say s.docxTawnaDelatorrejs
Check the paper you write and add your perspective I forgot to say some instructions. put some opinion about torah
Write a 3 page paper on what you have learned about Judaism that new for you and which is somehow significant to your understanding about this religion and how it affected your thinking.
Could you add some perspectives to paper you wrote...
i dont want you write new paper just add some opinion to paper
.
Check out attachments and read instructions before you make Hand Sh.docxTawnaDelatorrejs
"Check out attachments and read instructions before you make Hand Shake. Otherwise, I can't sign the agreement"
The most
IMPORTANT
things for me:
1)
Use very simple language, I'm an international student
.
2) Follow ALL instructions carefully 100%.
3) Finish it
on time
.
4) Last but not least,
Originality
.
====
I will run the paper through Copyscape that homework market provides, and the result MUST be = ZERO.
Thanks in advance,
.
check out the attachment, it has prompt, use the 4 website to quote .docxTawnaDelatorrejs
check out the attachment, it has prompt, use the 4 website to quote AND paraphrase (both are required) that i pasted on there. 800 words. APA style
download the attachment and follow the requiremen
1. A Swiveling Proxy That Will Even Wear a Tutu
By ROBBIE BROWNJUNE 7, 2013
http://www.nytimes.com/2013/06/08/education/for-homebound-students-a-robot-proxy-in-the-classroom.html?_r=0
2. How One Boy With Autism Became BFF With Apple’s Siri
By JUDITH NEWMANOCT. 17, 2014
http://www.nytimes.com/2014/10/19/fashion/how-apples-siri-became-one-autistic-boys-bff.html
3. The Ethical Frontiers of Robotics
Noel Sharkey*
http://webpages.uncc.edu/~jmconrad/ECGR4161-2011-05/notes/Science_Article_Robotics_Ethics2.pdf
4. THE ROBOTIC MOMENT
sherry turkle
In late November 2005, I took my daughter Rebecca, then fourteen, to the Darwin exhibition
at the American Museum of Natural History in New York. From the moment you step into
the museum and come face-to-face with a full-size dinosaur, you become part of a celebration
of life on Earth, what Darwin called “endless forms most beautiful.” Millions upon millions of
now lifeless specimens represent nature’s invention in every corner of the globe. There could
be no better venue for documenting Darwin’s life and thought and his theory of evolution by
natural selection, the central truth that underpins contemporary biology. The exhibition aimed
to please and, a bit defensively in these days of attacks on the theory of evolution, wanted to
convince.
At the exhibit’s entrance were two giant tortoises from the Galápagos Islands, the bestknown
inhabitants of the archipelago where Darwin did his most famous investigations. The
museum had been advertising these tortoises as wonders, curiosities, and marvels. Here,
among the plastic models at the museum, was the life that Darwin saw more than a century
and a half ago. One tortoise was hidden from view; the other rested in its cage, utterly still.
Rebecca inspected the visible tortoise thoughtfully for a while and then said matter-of-factly,
“They could have used a robot.” I was taken aback and asked what she meant. She said she
thought it was a shame to bring the turtle all this way from its island home in the Pacific, when
it was just going to sit there in the museum, motionless, doing nothing. Rebecca was both
concerned for the imprisoned turtle and unmoved by its authenticity.
It was Thanksgiving weekend. The line was long, the crowd frozen in place. I began to talk
with some of the other parents and children. My question—“Do you care that the turtle is
alive?”—was a welcome diversion from the boredom of the wait. A ten-year-old girl told me
that she would prefer a robot turtle because aliveness comes with aesthetic inconvenience:
“Its water looks dirty. Gross.” More usually, votes for the robots echoed my daughter’s sentiment
that in this setting, aliveness didn’t seem worth the trouble. A twelve-year-old girl was
adam.
Charles Mann is not only interested in how American societies arrive.docxTawnaDelatorrejs
Charles Mann is not only interested in how American societies arrived, developed, and
evolved, but also how they adapted to the multiple environments of the Americas. How
did indigenous Americans find ways to overcome environmental obstacles? What
techniques, attitudes, or actions did indigenous Americans share? What techniques were
unique to certain areas? Why did some communities and societies thrive in the years
before 1492 while others fell apart and disbanded into new groups or the landscape? How did scholars of the eighteenth, nineteenth, and twentieth centuries differ on their ideas of American Indian development?
.
Check out attachments and read instructions before you make Hand Sha.docxTawnaDelatorrejs
Check out attachments and read instructions before you make Hand Shake.
Otherwise
, I can't sign the agreement"
The most
IMPORTANT
things for me:
1)
Use very simple language, I'm an international student
.
2) Follow ALL instructions carefully 100%.
3) Finish it
on time
.
4) Last but not least, Originality.
====
I will run the paper through Copyscape that homework market provides, and the result MUST be = ZERO.
.
Chapters 5-8. One very significant period in Graphic Design History .docxTawnaDelatorrejs
Chapters 5-8. One very significant period in Graphic Design History was the Renaissance. Maybe a person or object of art made you start thinking about how it was done. here's the link for the chaper that u need to look at
https://www.youtube.com/watch?v=3vCNvvQwCos&list=PLxPtyllY6Cx_Xar71rcNFqX2bDB7Wzfll
.
childrens right in Pakistan.6 pagesat least 7 referencesAPA s.docxTawnaDelatorrejs
children's right in Pakistan.
6 pages
at least 7 references
APA style
References, citation needed
outline:
1.
Country in context
2.
Demographics
3.
History
4.
Culture and socio-economic context: official language, religion,
5.
Legislation/policies addressing rights
6.
Health status of child
7.
Education
8.
Well-being and quality of life: human develop index
9.
Status of children with special needs
10.
summary
.
CHAPTER ONEIntroductionLearning Objectives• Be able to concept.docxTawnaDelatorrejs
CHAPTER ONEIntroduction
Learning Objectives
• Be able to conceptualize the “information explosion” and how it relates to the brain sciences.
• Be able to describe pharmacodynamics and pharmacokinetics.
• Be able to articulate the benefits of an integrative approach to psychopharmacology.
ENCOURAGEMENT TO THE READER
Some of you may begin this book with some anxiety because this is a new area for you. You may imagine that psychopharmacology is exclusively a “hard science,” and perhaps you don't think of yourself as a “hard science” kind of person. You may even feel uncertain about your ability to master basic psychopharmacological concepts. First, let us assure you one more time that our goal is to make this topic accessible to readers who are practicing as or studying to be mental health professionals, many of whom may not have a background in the physical or organic sciences. Second, we recommend to those teaching a course in psychopharmacology that, because of the rapid nature of change in the field, teaching styles that rely on memorization are of limited use in this area. We recommend helping students master basic concepts and then applying these concepts to cases. To facilitate that process, we supply cases and objectives/review questions for main sections of the book. Finally, we invite you students to join us in an incredible journey centering on the most complex organ known to humanity—the human mind and brain. We hope you can revel in the complexity of the brain and the sheer magnitude of its power. We hope you can resist the temptation to want simple and concrete answers to many of the questions this journey will raise. We also hope you learn to appreciate the ambiguous nature of “mind” and its relationship to the brain. As authors and researchers who have traveled this path before us will attest, there are no simple or even known answers to many of the questions that arise (Grilly & Salmone, 2011; Schatzberg & Nemeroff, 1998). We encourage a mixture of trying to comprehend the information while dwelling in the mystery that is the context for the information. Before moving on, we offer a mantra to help you implement this recommendation.
A MANTRA
Even though psychopharmacology is in its embryonic stage, it is a vast and complex topic. Several years ago I (Ingersoll) engaged in some multicultural counseling training with Paul Pederson. In that training, Dr. Pederson commented, “Culture is complex, and complexity is our friend.” We offer a paraphrase as a mantra for psychopharmacology students: “Reality is complex, and complexity is our friend.” We remind the reader of this mantra throughout the book. You might try saying it aloud right now: “Reality is complex, and complexity is our friend.” If you reach a passage in this book that is challenging for you or that arouses anxiety, stop, take a deep breath, and practice the mantra.
The primary audience for this book is mental health clinicians who may not have had much training in biology.
Chapter TenThe Federal JudiciaryBrian M. MurphyLearnin.docxTawnaDelatorrejs
Chapter Ten
The Federal Judiciary
Brian M. Murphy
Learning Objectives
After covering the topic of the federal judiciary, students should
understand:
1. The relationship of state courts to the federal judiciary.
2. The jurisdiction of federal courts.
3. The structure of the federal judicial system.
4. The procedures of the U.S. Supreme Court.
5. The powers of the federal judiciary.
Abstract
The udicial y e i he i ed a e i a ed he d c ri e
federalism. Two court systems exist side-by-side, national and state, and
each has a distinct set of powers. State courts, for the most part, are
responsible for handling the legal issues that arise under their own laws. It
is primarily when a federal uestion is presented that the federal udicial
system can become in ol ed in a state court. therwise, state udiciaries
are generally autonomous even from one another. The Constitution
precisely outlines the types of cases that can be heard by federal courts,
yet it is almost impossible to force a federal court to hear a case that falls
under its urisdiction if the udge s wants to avoid it. The authority of
the U.S. Supreme Court has slowly grown over time, largely through the
power of udicial review. onetheless, federalism has managed to remain
a signi cant barrier against federal courts becoming too powerful. The
udicial system designed by the framers continues to survive and function
after 200 years.
Introduction
The federal judicial system is the least commonly known and least
understood branch of American government. In 2007, 78% could not
name the current Chief Justice of the U.S. Supreme Court but 66% were
able to identify at least one of the judges on the T show American
Idol (Jamieson, 2007). Much of judicial work is conducted out of the
limelight and courts are not considered an important in uence in the daily
lives of people. It is clear the framers believed that the federal judicial
system would be the weakest of the three branches because, as Alexander
amilton wrote, it has no in uence over either the sword or the purse
(Hamilton, 1961, 465). In other words, courts cannot command an army
(or even police) to ensure that decisions are enforced or allocate money to
implement one of their rulings. Judges must depend on the other branches
in order to get anything done. According to an oft-repeated story, President
Andrew Jackson supposedly mocked a decision by Chief Justice John
Marshall with the words, John Marshall has made his decision, now let
him enforce it’’ (Schwartz, 1993, 94).
But times and the role of the federal judiciary have changed. One
scholar even concluded that the United States is now operating under a
government by judiciary’’ because the U.S. Supreme Court can revise
the Constitution by how it interprets the wording (Berger, 1997). As Chief
Justice Charles vans Hughes once uipped, e are under a Constitution,
but the Constitution is what the judges say it is’’ (Hughes, 1916, 185). .
Chapter 9 provides a discussion of the challenges of identifying ELL.docxTawnaDelatorrejs
Chapter 9 provides a discussion of the challenges of identifying ELLs’ as having a learning disability or being gifted with their lower than grade-level proficiency in English. After reading Chapter 9, write a post that addresses the following questions:
What kinds of disabilities might an ELL have?
What are the challenges of determining whether an ELL has a learning ability or is gifted?
What kinds of interventions are used once an ELL has been identified as having a learning disability?
What kinds of interventions are used once an ELL is determined to be gifted?
If you were teaching a class with some ELLs in it, what signals would you look for in the behavior or they ELLs to determine whether they might need to be tested for learning disabilities or being gifted?
How might you adapt your curriculum for an ELL student with a learning disability or who is gifted?
.
Chapter 8 -- Crimes
1. Conduct that may be a misdemeanor in one state may be a felony in another state.
2. A required element for a crime is that the criminal party voluntarily commits the prohibited act (think “gun to head”).
3. A person cannot commit a crime if the person does not know that his or her conduct is criminal (think “Honduran bony fish or short lobster).
4. The Fourth Amendment prohibits ALL government searches of businesses.
5. Traditionally, extortion involves wrongful demands made by public officials.
6. A company cannot be found guilty of a crime that is committed by its agent.
7. If an employee wrongfully keeps money that was entrusted to the employee by his or employer, the employee has committed the crime of embezzlement.
8. Government officers do not need a search warrant in order to inspect property that is in "plain view".
9. The Constitution guarantees individuals the right to a speedy trial in criminal cases.
10. The Digital Millennium Copyright Act allows a person to thwart encryption devices that copy right holders place on copyrighted material if the person has purchased the copyrighted item in question.
Chapter 9 -- Torts
11. One wrongful act may be both a crime and a tort.
12. A person is not entitled to recover for EVERY injury or loss that is caused by another person.
13. In general, tort liability will not be imposed for an involuntary act even if the act harms another.
14. Under tort law, one owes a duty to society to conform his or her conduct to a required standard (think: does society sue the tortfeasor does the “somebody done me wrong” individual plaintiff sue the tortfeasor?).
15. The U.S. government cannot be sued for harm caused by the negligence of federal employees.
16. In some states, a plaintiff may recover for emotional distress that is negligently caused by another.
17. Companies can now make commercial use of the name or likeness of celebrities without first obtaining the celebrities permission to do so because most states do not recognize the tort of invasion of the right to publicity.
Chapter 10
18.
Patents are granted by state governments, not by the federal government.
19.
Trademarks may be protected for up to three years prior to the time that they are actually used.
20. A “term” acquires a secondary meaning when, through prolonged use, the public has come to associate that term with a particular product.
21. In general, mere ideas and concepts cannot be copyrighted or patented.
22.
A trade secret may be disclosed without losing its legal .
chapter 5 Making recommendations for I studied up to this .docxTawnaDelatorrejs
chapter 5
Making recommendations for I studied up to this point, what should now be study after I have written about what I found. All chapter 5 about chapter 4 what all things I discovered, what senses do they make to you what would you have study more if you have more time, what I think about , what I found
.
Chapter 4. Terris, Daniel. (2005) Ethics at Work Creating Virtue at.docxTawnaDelatorrejs
Chapter 4. Terris, Daniel. (2005) Ethics at Work: Creating Virtue at an American Corporation. Brandeis University Press. Apply critical thinking skills
in evaluating Lockheed Martin's efforts.
1. What do you think about the notion presented by Terris that Lockheed's ethics program does little to prevent ethical breaches at the highest level of the organization?
2. Are the efforts put forth—such as making sure higher level executives participate in training—enough to help executives navigate what Terris calls the 'ethical minefield' faced by leadership in such an organization?
3. What are some things that could be done to address the issue related to ethics at higher executive levels of the organization?
4. Terris points out that the company's program is overly focused on individuals and that it doesn't really address group dynamics that can impact ethical situations. For instance, there can be a tendency for groups to ‘go with the flow’ of the group decision making process and overlook ethical issues in the process. What would you recommend that Lockheed Martin do to address this situation?
(Hint: reviewing p. 128 and the following pages – before section headed “Personal Responsibility, Collective Innocence” - of the text might be helpful).
Assignment Expectations: Write a 4- to 5-page paper, not including title page or references page addressing the issue.
Your paper should be double-spaced and in 12-point type size.
Your paper should have a separate cover page and a separate reference page. Make sure you cite your sources.
.
Chapter 41. Read in the text about Alexanders attempt to fuse Gre.docxTawnaDelatorrejs
Chapter 4
1. Read in the text about Alexander's attempt to fuse Greek and Eastern cultures (116-120 -see box Alexander meets an Indian King, 115). Then go to:
Alexander the Great
- a from a BBC documentary. The video will have to be opened in a new window.
Write a brief review after watching the documentary (You don't have to watch the entire hour). What does Wood have to say about the scope of Alexander the Great's accomplishments? Does watching a video set in the actual landscape of Macedonia and Turkey help understand the history of an ancient civilization? How?
2. Go to:
Building of the Parthenon
and
Optical 'tricks' at the Parthenon
to see the accomplishments of Greek architects and politicians. What is the connection between Athenian politics and the building of the Parthenon? What illusions were utlitzed by the architects and engineers to emphasize the grandeur of the Parthenon?
Chapter 5
Select TWO of the following questions and complete the links assignments: Remember to mention source material in your response.
(Select 3 for extra credit
1. Go to:
Roman Writers view their world
and choose 2 authors to write an essay on entertainments and past times of Roman citizens and how eyewitnesses wrote about their world. Who are they? What position did they hold in Roman society? Is this important to their view point?
2. Go to
Christian symbolism
and
Colors in religious art
and write about how a largely illiterate (slave and lower class Romans and client state residents) society could learn about this new "Christian" religion through art, symbolism and color. How would this help the conversion process?
3. Go to
Sights along the Silk Road
. Click on the interactive maps and visit several of the stops along the Silk Road. What did you find? Learn? Then go to :
Silk Road Project
. Click on "Music and Artists." Then "Listen to Music."
Click on a title for ex: "Arabian" to listen to sample of the music and instrument. Write on your findings.
You may have to update your "Flash" player to hear music
.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Business intelligence and analyticssystems for dec
1. Business intelligence
and analytics:
systems for decision support
Boston Columbus Indianapolis New York San Francisco Upper
Saddle River
Amsterdam Cape Town Dubai London Madrid Milan Munich
Paris Montréal Toronto
Delhi Mexico City São Paulo Sydney Hong Kong Seoul
Singapore Taipei Tokyo
T e n T h e d i T i o n
Ramesh Sharda
Oklahoma State University
Dursun Delen
Oklahoma State University
Efraim Turban
University of Hawaii
With contributions by
J. E. Aronson
The University of Georgia
Ting-Peng Liang
2. National Sun Yat-sen University
David King
JDA Software Group, Inc.
Credits and acknowledgments borrowed from other sources and
reproduced, with permission, in this textbook
appear on the appropriate page within text.
Microsoft and/or its respective suppliers make no
representations about the suitability of the information
contained in the documents and related graphics published as
part of the services for any purpose. All such
documents and related graphics are provided “as is” without
warranty of any kind. Microsoft and/or its
respective suppliers hereby disclaim all warranties and
conditions with regard to this information, including
all warranties and conditions of merchantability, whether
express, implied or statutory, fitness for a particular
purpose, title and non-infringement. In no event shall Microsoft
and/or its respective suppliers be liable for
any special, indirect or consequential damages or any damages
whatsoever resulting from loss of use, data or
profits, whether in an action of contract, negligence or other
tortious action, arising out of or in connection
with the use or performance of information available from the
services.
The documents and related graphics contained herein could
include technical inaccuracies or typographical
errors. Changes are periodically added to the information
herein. Microsoft and/or its respective suppliers may
make improvements and/or changes in the product(s) and/or the
program(s) described herein at any time.
4. University of Hawaii; With contributions
by J. E. Aronson, The University of Georgia, Ting-Peng Liang,
National Sun Yat-sen University,
David King, JDA Software Group, Inc.—Tenth edition.
pages cm
ISBN-13: 978-0-13-305090-5
ISBN-10: 0-13-305090-4
1. Management—Data processing. 2. Decision support systems.
3. Expert systems (Computer science)
4. Business intelligence. I. Title.
HD30.2.T87 2014
658.4'038011—dc23
2013028826
10 9 8 7 6 5 4 3 2 1
Editor in Chief: Stephanie Wall
Executive Editor: Bob Horan
Program Manager Team Lead: Ashley Santora
Program Manager: Denise Vaughn
Executive Marketing Manager: Anne Fahlgren
Project Manager Team Lead: Judy Leale
Project Manager: Tom Benfatti
Operations Specialist: Michelle Klein
Creative Director: Jayne Conte
Cover Designer: Suzanne Behnke
Digital Production Project Manager: Lisa
Rinaldi
Full-Service Project Management: George Jacob,
Integra Software
5. Solution
s.
Printer/Binder: Edwards Brothers Malloy-Jackson
Road
Cover Printer: Lehigh/Phoenix-Hagerstown
Text Font: Garamond
ISBN 10: 0-13-305090-4
ISBN 13: 978-0-13-305090-5
iii
Preface xxi
About the Authors xxix
Part I Decision Making and analytics: an Overview 1
Chapter 1 An Overview of Business Intelligence, Analytics,
6. and Decision Support 2
Chapter 2 Foundations and Technologies for Decision Making
37
Part II Descriptive analytics 77
Chapter 3 Data Warehousing 78
Chapter 4 Business Reporting, Visual Analytics, and Business
Performance Management 135
Part III Predictive analytics 185
Chapter 5 Data Mining 186
Chapter 6 Techniques for Predictive Modeling 243
Chapter 7 Text Analytics, Text Mining, and Sentiment Analysis
288
Chapter 8 Web Analytics, Web Mining, and Social Analytics
338
Part IV Prescriptive analytics 391
Chapter 9 Model-Based Decision Making: Optimization and
Multi-
7. Criteria Systems 392
Chapter 10 Modeling and Analysis: Heuristic Search Methods
and
Simulation 435
Chapter 11 Automated Decision Systems and Expert Systems
469
Chapter 12 Knowledge Management and Collaborative Systems
507
Part V Big Data and Future Directions for Business
analytics 541
Chapter 13 Big Data and Analytics 542
Chapter 14 Business Analytics: Emerging Trends and Future
Impacts 592
Glossary 634
Index 648
8. BRIEF ContEnts
iv
Preface xxi
About the Authors xxix
Part I Decision Making and analytics: an Overview 1
Chapter 1 An overview of Business Intelligence, Analytics, and
Decision support 2
1.1 Opening Vignette: Magpie Sensing Employs Analytics to
Manage a Vaccine Supply Chain Effectively and Safely 3
1.2 Changing Business Environments and Computerized
Decision Support 5
The Business Pressures–Responses–Support Model 5
1.3 Managerial Decision Making 7
The Nature of Managers’ Work 7
9. The Decision-Making Process 8
1.4 Information Systems Support for Decision Making 9
1.5 An Early Framework for Computerized Decision
Support 11
The Gorry and Scott-Morton Classical Framework 11
Computer Support for Structured Decisions 12
Computer Support for Unstructured Decisions 13
Computer Support for Semistructured Problems 13
1.6 The Concept of Decision Support Systems (DSS) 13
DSS as an Umbrella Term 13
Evolution of DSS into Business Intelligence 14
1.7 A Framework for Business Intelligence (BI) 14
Definitions of BI 14
A Brief History of BI 14
10. The Architecture of BI 15
Styles of BI 15
The Origins and Drivers of BI 16
A Multimedia Exercise in Business Intelligence 16
▶ ApplicAtion cAse 1.1 Sabre Helps Its Clients Through
Dashboards
and Analytics 17
The DSS–BI Connection 18
1.8 Business Analytics Overview 19
Descriptive Analytics 20
▶ ApplicAtion cAse 1.2 Eliminating Inefficiencies at Seattle
Children’s Hospital 21
▶ ApplicAtion cAse 1.3 Analysis at the Speed of Thought 22
Predictive Analytics 22
ContEnts
11. Contents v
▶ ApplicAtion cAse 1.4 Moneyball: Analytics in Sports and
Movies 23
▶ ApplicAtion cAse 1.5 Analyzing Athletic Injuries 24
Prescriptive Analytics 24
▶ ApplicAtion cAse 1.6 Industrial and Commercial Bank of
China
(ICBC) Employs Models to Reconfigure Its Branch Network 25
Analytics Applied to Different Domains 26
Analytics or Data Science? 26
1.9 Brief Introduction to Big Data Analytics 27
What Is Big Data? 27
▶ ApplicAtion cAse 1.7 Gilt Groupe’s Flash Sales Streamlined
by Big
Data Analytics 29
1.10 Plan of the Book 29
Part I: Business Analytics: An Overview 29
12. Part II: Descriptive Analytics 30
Part III: Predictive Analytics 30
Part IV: Prescriptive Analytics 31
Part V: Big Data and Future Directions for Business Analytics
31
1.11 Resources, Links, and the Teradata University Network
Connection 31
Resources and Links 31
Vendors, Products, and Demos 31
Periodicals 31
The Teradata University Network Connection 32
The Book’s Web Site 32
Chapter Highlights 32 • Key Terms 33
Questions for Discussion 33 • Exercises 33
▶ end-of-chApter ApplicAtion cAse Nationwide Insurance
Used BI
to Enhance Customer Service 34
References 35
Chapter 2 Foundations and technologies for Decision Making
37
2.1 Opening Vignette: Decision Modeling at HP Using
13. Spreadsheets 38
2.2 Decision Making: Introduction and Definitions 40
Characteristics of Decision Making 40
A Working Definition of Decision Making 41
Decision-Making Disciplines 41
Decision Style and Decision Makers 41
2.3 Phases of the Decision-Making Process 42
2.4 Decision Making: The Intelligence Phase 44
Problem (or Opportunity) Identification 45
▶ ApplicAtion cAse 2.1 Making Elevators Go Faster! 45
Problem Classification 46
Problem Decomposition 46
Problem Ownership 46
vi Contents
2.5 Decision Making: The Design Phase 47
Models 47
Mathematical (Quantitative) Models 47
14. The Benefits of Models 47
Selection of a Principle of Choice 48
Normative Models 49
Suboptimization 49
Descriptive Models 50
Good Enough, or Satisficing 51
Developing (Generating) Alternatives 52
Measuring Outcomes 53
Risk 53
Scenarios 54
Possible Scenarios 54
Errors in Decision Making 54
15. 2.6 Decision Making: The Choice Phase 55
2.7 Decision Making: The Implementation Phase 55
2.8 How Decisions Are Supported 56
Support for the Intelligence Phase 56
Support for the Design Phase 57
Support for the Choice Phase 58
Support for the Implementation Phase 58
2.9 Decision Support Systems: Capabilities 59
A DSS Application 59
2.10 DSS Classifications 61
The AIS SIGDSS Classification for DSS 61
Other DSS Categories 63
Custom-Made Systems Versus Ready-Made Systems 63
2.11 Components of Decision Support Systems 64
The Data Management Subsystem 65
16. The Model Management Subsystem 65
▶ ApplicAtion cAse 2.2 Station Casinos Wins by Building
Customer
Relationships Using Its Data 66
▶ ApplicAtion cAse 2.3 SNAP DSS Helps OneNet Make
Telecommunications Rate Decisions 68
The User Interface Subsystem 68
The Knowledge-Based Management Subsystem 69
▶ ApplicAtion cAse 2.4 From a Game Winner to a Doctor! 70
Chapter Highlights 72 • Key Terms 73
Questions for Discussion 73 • Exercises 74
▶ end-of-chApter ApplicAtion cAse Logistics Optimization in a
Major Shipping Company (CSAV) 74
References 75
Contents vii
Part II Descriptive analytics 77
17. Chapter 3 Data Warehousing 78
3.1 Opening Vignette: Isle of Capri Casinos Is Winning with
Enterprise Data Warehouse 79
3.2 Data Warehousing Definitions and Concepts 81
What Is a Data Warehouse? 81
A Historical Perspective to Data Warehousing 81
Characteristics of Data Warehousing 83
Data Marts 84
Operational Data Stores 84
Enterprise Data Warehouses (EDW) 85
Metadata 85
▶ ApplicAtion cAse 3.1 A Better Data Plan: Well-Established
TELCOs
Leverage Data Warehousing and Analytics to Stay on Top in a
Competitive Industry 85
3.3 Data Warehousing Process Overview 87
18. ▶ ApplicAtion cAse 3.2 Data Warehousing Helps MultiCare
Save
More Lives 88
3.4 Data Warehousing Architectures 90
Alternative Data Warehousing Architectures 93
Which Architecture Is the Best? 96
3.5 Data Integration and the Extraction, Transformation, and
Load (ETL) Processes 97
Data Integration 98
▶ ApplicAtion cAse 3.3 BP Lubricants Achieves BIGS
Success 98
Extraction, Transformation, and Load 100
3.6 Data Warehouse Development 102
▶ ApplicAtion cAse 3.4 Things Go Better with Coke’s Data
Warehouse 103
Data Warehouse Development Approaches 103
19. ▶ ApplicAtion cAse 3.5 Starwood Hotels & Resorts Manages
Hotel
Profitability with Data Warehousing 106
Additional Data Warehouse Development Considerations 107
Representation of Data in Data Warehouse 108
Analysis of Data in the Data Warehouse 109
OLAP Versus OLTP 110
OLAP Operations 110
3.7 Data Warehousing Implementation Issues 113
▶ ApplicAtion cAse 3.6 EDW Helps Connect State Agencies
in
Michigan 115
Massive Data Warehouses and Scalability 116
3.8 Real-Time Data Warehousing 117
▶ ApplicAtion cAse 3.7 Egg Plc Fries the Competition in Near
20. Real
Time 118
viii Contents
3.9 Data Warehouse Administration, Security Issues, and
Future
Trends 121
The Future of Data Warehousing 123
3.10 Resources, Links, and the Teradata University Network
Connection 126
Resources and Links 126
Cases 126
Vendors, Products, and Demos 127
Periodicals 127
21. Additional References 127
The Teradata University Network (TUN) Connection 127
Chapter Highlights 128 • Key Terms 128
Questions for Discussion 128 • Exercises 129
▶ end-of-chApter ApplicAtion cAse Continental Airlines Flies
High
with Its Real-Time Data Warehouse 131
References 132
Chapter 4 Business Reporting, Visual Analytics, and Business
Performance Management 135
4.1 Opening Vignette:Self-Service Reporting Environment
Saves Millions for Corporate Customers 136
4.2 Business Reporting Definitions and Concepts 139
What Is a Business Report? 140
▶ ApplicAtion cAse 4.1 Delta Lloyd Group Ensures Accuracy
and
Efficiency in Financial Reporting 141
Components of the Business Reporting System 143
22. ▶ ApplicAtion cAse 4.2 Flood of Paper Ends at FEMA 144
4.3 Data and Information Visualization 145
▶ ApplicAtion cAse 4.3 Tableau Saves Blastrac Thousands of
Dollars
with Simplified Information Sharing 146
A Brief History of Data Visualization 147
▶ ApplicAtion cAse 4.4 TIBCO Spotfire Provides Dana-Farber
Cancer
Institute with Unprecedented Insight into Cancer Vaccine
Clinical
Trials 149
4.4 Different Types of Charts and Graphs 150
Basic Charts and Graphs 150
Specialized Charts and Graphs 151
4.5 The Emergence of Data Visualization and Visual
Analytics 154
Visual Analytics 156
23. High-Powered Visual Analytics Environments 158
4.6 Performance Dashboards 160
▶ ApplicAtion cAse 4.5 Dallas Cowboys Score Big with
Tableau and
Teknion 161
Contents ix
Dashboard Design 162
▶ ApplicAtion cAse 4.6 Saudi Telecom Company Excels with
Information Visualization 163
What to Look For in a Dashboard 164
Best Practices in Dashboard Design 165
Benchmark Key Performance Indicators with Industry Standards
165
24. Wrap the Dashboard Metrics with Contextual Metadata 165
Validate the Dashboard Design by a Usability Specialist 165
Prioritize and Rank Alerts/Exceptions Streamed to the
Dashboard 165
Enrich Dashboard with Business Users’ Comments 165
Present Information in Three Different Levels 166
Pick the Right Visual Construct Using Dashboard Design
Principles 166
Provide for Guided Analytics 166
4.7 Business Performance Management 166
Closed-Loop BPM Cycle 167
▶ ApplicAtion cAse 4.7 IBM Cognos Express Helps Mace for
Faster
and Better Business Reporting 169
4.8 Performance Measurement 170
Key Performance Indicator (KPI) 171
25. Performance Measurement System 172
4.9 Balanced Scorecards 172
The Four Perspectives 173
The Meaning of Balance in BSC 174
Dashboards Versus Scorecards 174
4.10 Six Sigma as a Performance Measurement System 175
The DMAIC Performance Model 176
Balanced Scorecard Versus Six Sigma 176
Effective Performance Measurement 177
▶ ApplicAtion cAse 4.8 Expedia.com’s Customer Satisfaction
Scorecard 178
Chapter Highlights 179 • Key Terms 180
Questions for Discussion 181 • Exercises 181
▶ end-of-chApter ApplicAtion cAse Smart Business Reporting
Helps Healthcare Providers Deliver Better Care 182
References 184
26. Part III Predictive analytics 185
Chapter 5 Data Mining 186
5.1 Opening Vignette: Cabela’s Reels in More Customers with
Advanced Analytics and Data Mining 187
5.2 Data Mining Concepts and Applications 189
▶ ApplicAtion cAse 5.1 Smarter Insurance: Infinity P&C
Improves
Customer Service and Combats Fraud with Predictive Analytics
191
x Contents
Definitions, Characteristics, and Benefits 192
▶ ApplicAtion cAse 5.2 Harnessing Analytics to Combat
Crime:
Predictive Analytics Helps Memphis Police Department
Pinpoint Crime
and Focus Police Resources 196
27. How Data Mining Works 197
Data Mining Versus Statistics 200
5.3 Data Mining Applications 201
▶ ApplicAtion cAse 5.3 A Mine on Terrorist Funding 203
5.4 Data Mining Process 204
Step 1: Business Understanding 205
Step 2: Data Understanding 205
Step 3: Data Preparation 206
Step 4: Model Building 208
▶ ApplicAtion cAse 5.4 Data Mining in Cancer Research 210
Step 5: Testing and Evaluation 211
Step 6: Deployment 211
Other Data Mining Standardized Processes and Methodologies
212
5.5 Data Mining Methods 214
Classification 214
Estimating the True Accuracy of Classification Models 215
Cluster Analysis for Data Mining 220
▶ ApplicAtion cAse 5.5 2degrees Gets a 1275 Percent Boost in
Churn
28. Identification 221
Association Rule Mining 224
5.6 Data Mining Software Tools 228
▶ ApplicAtion cAse 5.6 Data Mining Goes to Hollywood:
Predicting
Financial Success of Movies 231
5.7 Data Mining Privacy Issues, Myths, and Blunders 234
Data Mining and Privacy Issues 234
▶ ApplicAtion cAse 5.7 Predicting Customer Buying
Patterns—The
Target Story 235
Data Mining Myths and Blunders 236
Chapter Highlights 237 • Key Terms 238
Questions for Discussion 238 • Exercises 239
▶ end-of-chApter ApplicAtion cAse Macys.com Enhances Its
Customers’ Shopping Experience with Analytics 241
References 241
Chapter 6 techniques for Predictive Modeling 243
6.1 Opening Vignette: Predictive Modeling Helps Better
29. Understand and Manage Complex Medical
Procedures 244
6.2 Basic Concepts of Neural Networks 247
Biological and Artificial Neural Networks 248
▶ ApplicAtion cAse 6.1 Neural Networks Are Helping to Save
Lives in
the Mining Industry 250
Elements of ANN 251
Contents xi
Network Information Processing 252
Neural Network Architectures 254
▶ ApplicAtion cAse 6.2 Predictive Modeling Is Powering the
Power
Generators 256
6.3 Developing Neural Network–Based Systems 258
The General ANN Learning Process 259
30. Backpropagation 260
6.4 Illuminating the Black Box of ANN with Sensitivity
Analysis 262
▶ ApplicAtion cAse 6.3 Sensitivity Analysis Reveals Injury
Severity
Factors in Traffic Accidents 264
6.5 Support Vector Machines 265
▶ ApplicAtion cAse 6.4 Managing Student Retention with
Predictive
Modeling 266
Mathematical Formulation of SVMs 270
Primal Form 271
Dual Form 271
Soft Margin 271
Nonlinear Classification 272
Kernel Trick 272
6.6 A Process-Based Approach to the Use of SVM 273
Support Vector Machines Versus Artificial Neural Networks
274
31. 6.7 Nearest Neighbor Method for Prediction 275
Similarity Measure: The Distance Metric 276
Parameter Selection 277
▶ ApplicAtion cAse 6.5 Efficient Image Recognition and
Categorization with kNN 278
Chapter Highlights 280 • Key Terms 280
Questions for Discussion 281 • Exercises 281
▶ end-of-chApter ApplicAtion cAse Coors Improves Beer
Flavors
with Neural Networks 284
References 285
Chapter 7 text Analytics, text Mining, and sentiment Analysis
288
7.1 Opening Vignette: Machine Versus Men on Jeopardy!: The
Story of Watson 289
7.2 Text Analytics and Text Mining Concepts and
Definitions 291
▶ ApplicAtion cAse 7.1 Text Mining for Patent Analysis 295
7.3 Natural Language Processing 296
32. ▶ ApplicAtion cAse 7.2 Text Mining Improves Hong Kong
Government’s Ability to Anticipate and Address Public
Complaints 298
7.4 Text Mining Applications 300
Marketing Applications 301
Security Applications 301
▶ ApplicAtion cAse 7.3 Mining for Lies 302
Biomedical Applications 304
xii Contents
Academic Applications 305
▶ ApplicAtion cAse 7.4 Text Mining and Sentiment Analysis
Help
Improve Customer Service Performance 306
7.5 Text Mining Process 307
Task 1: Establish the Corpus 308
33. Task 2: Create the Term–Document Matrix 309
Task 3: Extract the Knowledge 312
▶ ApplicAtion cAse 7.5 Research Literature Survey with Text
Mining 314
7.6 Text Mining Tools 317
Commercial Software Tools 317
Free Software Tools 317
▶ ApplicAtion cAse 7.6 A Potpourri of Text Mining Case
Synopses 318
7.7 Sentiment Analysis Overview 319
▶ ApplicAtion cAse 7.7 Whirlpool Achieves Customer Loyalty
and
Product Success with Text Analytics 321
7.8 Sentiment Analysis Applications 323
7.9 Sentiment Analysis Process 325
Methods for Polarity Identification 326
34. Using a Lexicon 327
Using a Collection of Training Documents 328
Identifying Semantic Orientation of Sentences and Phrases 328
Identifying Semantic Orientation of Document 328
7.10 Sentiment Analysis and Speech Analytics 329
How Is It Done? 329
▶ ApplicAtion cAse 7.8 Cutting Through the Confusion: Blue
Cross
Blue Shield of North Carolina Uses Nexidia’s Speech Analytics
to Ease
Member Experience in Healthcare 331
Chapter Highlights 333 • Key Terms 333
Questions for Discussion 334 • Exercises 334
▶ end-of-chApter ApplicAtion cAse BBVA Seamlessly
Monitors
and Improves Its Online Reputation 335
References 336
35. Chapter 8 Web Analytics, Web Mining, and social Analytics
338
8.1 Opening Vignette: Security First Insurance Deepens
Connection with Policyholders 339
8.2 Web Mining Overview 341
8.3 Web Content and Web Structure Mining 344
▶ ApplicAtion cAse 8.1 Identifying Extremist Groups with
Web Link
and Content Analysis 346
8.4 Search Engines 347
Anatomy of a Search Engine 347
1. Development Cycle 348
Web Crawler 348
Document Indexer 348
36. Contents xiii
2. Response Cycle 349
Query Analyzer 349
Document Matcher/Ranker 349
How Does Google Do It? 351
▶ ApplicAtion cAse 8.2 IGN Increases Search Traffic by 1500
Percent 353
8.5 Search Engine Optimization 354
Methods for Search Engine Optimization 355
▶ ApplicAtion cAse 8.3 Understanding Why Customers
Abandon
Shopping Carts Results in $10 Million Sales Increase 357
8.6 Web Usage Mining (Web Analytics) 358
Web Analytics Technologies 359
▶ ApplicAtion cAse 8.4 Allegro Boosts Online Click-Through
Rates by
500 Percent with Web Analysis 360
37. Web Analytics Metrics 362
Web Site Usability 362
Traffic Sources 363
Visitor Profiles 364
Conversion Statistics 364
8.7 Web Analytics Maturity Model and Web Analytics Tools
366
Web Analytics Tools 368
Putting It All Together—A Web Site Optimization Ecosystem
370
A Framework for Voice of the Customer Strategy 372
8.8 Social Analytics and Social Network Analysis 373
Social Network Analysis 374
Social Network Analysis Metrics 375
▶ ApplicAtion cAse 8.5 Social Network Analysis Helps
38. Telecommunication Firms 375
Connections 376
Distributions 376
Segmentation 377
8.9 Social Media Definitions and Concepts 377
How Do People Use Social Media? 378
▶ ApplicAtion cAse 8.6 Measuring the Impact of Social Media
at
Lollapalooza 379
8.10 Social Media Analytics 380
Measuring the Social Media Impact 381
Best Practices in Social Media Analytics 381
▶ ApplicAtion cAse 8.7 eHarmony Uses Social Media to Help
Take the
Mystery Out of Online Dating 383
Social Media Analytics Tools and Vendors 384
Chapter Highlights 386 • Key Terms 387
39. Questions for Discussion 387 • Exercises 388
▶ end-of-chApter ApplicAtion cAse Keeping Students on Track
with
Web and Predictive Analytics 388
References 390
xiv Contents
Part IV Prescriptive analytics 391
Chapter 9 Model-Based Decision Making: optimization and
Multi-Criteria systems 392
9.1 Opening Vignette: Midwest ISO Saves Billions by Better
Planning of Power Plant Operations and Capacity
Planning 393
9.2 Decision Support Systems Modeling 394
▶ ApplicAtion cAse 9.1 Optimal Transport for ExxonMobil
Downstream Through a DSS 395
Current Modeling Issues 396
40. ▶ ApplicAtion cAse 9.2 Forecasting/Predictive Analytics
Proves to Be
a Good Gamble for Harrah’s Cherokee Casino and Hotel 397
9.3 Structure of Mathematical Models for Decision Support
399
The Components of Decision Support Mathematical Models 399
The Structure of Mathematical Models 401
9.4 Certainty, Uncertainty, and Risk 401
Decision Making Under Certainty 402
Decision Making Under Uncertainty 402
Decision Making Under Risk (Risk Analysis) 402
▶ ApplicAtion cAse 9.3 American Airlines Uses
Should-Cost Modeling to Assess the Uncertainty of Bids
for Shipment Routes 403
9.5 Decision Modeling with Spreadsheets 404
▶ ApplicAtion cAse 9.4 Showcase Scheduling at Fred Astaire
East
Side Dance Studio 404
41. 9.6 Mathematical Programming Optimization 407
▶ ApplicAtion cAse 9.5 Spreadsheet Model Helps Assign
Medical
Residents 407
Mathematical Programming 408
Linear Programming 408
Modeling in LP: An Example 409
Implementation 414
9.7 Multiple Goals, Sensitivity Analysis, What-If Analysis,
and Goal Seeking 416
Multiple Goals 416
Sensitivity Analysis 417
What-If Analysis 418
Goal Seeking 418
9.8 Decision Analysis with Decision Tables and Decision
Trees 420
Decision Tables 420
Decision Trees 422
42. 9.9 Multi-Criteria Decision Making With Pairwise
Comparisons 423
The Analytic Hierarchy Process 423
Contents xv
▶ ApplicAtion cAse 9.6 U.S. HUD Saves the House by Using
AHP for Selecting IT Projects 423
Tutorial on Applying Analytic Hierarchy Process Using Web-
HIPRE 425
Chapter Highlights 429 • Key Terms 430
Questions for Discussion 430 • Exercises 430
▶ end-of-chApter ApplicAtion cAse Pre-Positioning of
Emergency
Items for CARE International 433
References 434
Chapter 10 Modeling and Analysis: Heuristic search Methods
and
simulation 435
43. 10.1 Opening Vignette: System Dynamics Allows Fluor
Corporation to Better Plan for Project and Change
Management 436
10.2 Problem-Solving Search Methods 437
Analytical Techniques 438
Algorithms 438
Blind Searching 439
Heuristic Searching 439
▶ ApplicAtion cAse 10.1 Chilean Government Uses Heuristics
to
Make Decisions on School Lunch Providers 439
10.3 Genetic Algorithms and Developing GA Applications 441
Example: The Vector Game 441
Terminology of Genetic Algorithms 443
How Do Genetic Algorithms Work? 443
44. Limitations of Genetic Algorithms 445
Genetic Algorithm Applications 445
10.4 Simulation 446
▶ ApplicAtion cAse 10.2 Improving Maintenance Decision
Making in
the Finnish Air Force Through Simulation 446
▶ ApplicAtion cAse 10.3 Simulating Effects of Hepatitis B
Interventions 447
Major Characteristics of Simulation 448
Advantages of Simulation 449
Disadvantages of Simulation 450
The Methodology of Simulation 450
Simulation Types 451
Monte Carlo Simulation 452
Discrete Event Simulation 453
10.5 Visual Interactive Simulation 453
Conventional Simulation Inadequacies 453
Visual Interactive Simulation 453
Visual Interactive Models and DSS 454
▶ ApplicAtion cAse 10.4 Improving Job-Shop Scheduling
45. Decisions
Through RFID: A Simulation-Based Assessment 454
Simulation Software 457
xvi Contents
10.6 System Dynamics Modeling 458
10.7 Agent-Based Modeling 461
▶ ApplicAtion cAse 10.5 Agent-Based Simulation Helps
Analyze
Spread of a Pandemic Outbreak 463
Chapter Highlights 464 • Key Terms 464
Questions for Discussion 465 • Exercises 465
▶ end-of-chApter ApplicAtion cAse HP Applies Management
Science Modeling to Optimize Its Supply Chain and Wins a
Major
Award 465
References 467
46. Chapter 11 Automated Decision systems and Expert systems
469
11.1 Opening Vignette: InterContine ntal Hotel Group Uses
Decision Rules for Optimal Hotel Room Rates 470
11.2 Automated Decision Systems 471
▶ ApplicAtion cAse 11.1 Giant Food Stores Prices the Entire
Store 472
11.3 The Artificial Intelligence Field 475
11.4 Basic Concepts of Expert Systems 477
Experts 477
Expertise 478
Features of ES 478
▶ ApplicAtion cAse 11.2 Expert System Helps in Identifying
Sport
Talents 480
11.5 Applications of Expert Systems 480
▶ ApplicAtion cAse 11.3 Expert System Aids in Identification
of
47. Chemical, Biological, and Radiological Agents 481
Classical Applications of ES 481
Newer Applications of ES 482
Areas for ES Applications 483
11.6 Structure of Expert Systems 484
Knowledge Acquisition Subsystem 484
Knowledge Base 485
Inference Engine 485
User Interface 485
Blackboard (Workplace) 485
Explanation Subsystem (Justifier) 486
Knowledge-Refining System 486
▶ ApplicAtion cAse 11.4 Diagnosing Heart Diseases by Signal
Processing 486
11.7 Knowledge Engineering 487
Knowledge Acquisition 488
Knowledge Verification and Validation 490
Knowledge Representation 490
Inferencing 491
Explanation and Justification 496
48. Contents xvii
11.8 Problem Areas Suitable for Expert Systems 497
11.9 Development of Expert Systems 498
Defining the Nature and Scope of the Problem 499
Identifying Proper Experts 499
Acquiring Knowledge 499
Selecting the Building Tools 499
Coding the System 501
Evaluating the System 501
▶ ApplicAtion cAse 11.5 Clinical Decision Support System for
Tendon
Injuries 501
11.10 Concluding Remarks 502
Chapter Highlights 503 • Key Terms 503
Questions for Discussion 504 • Exercises 504
▶ end-of-chApter ApplicAtion cAse Tax Collections
Optimization
for New York State 504
References 505
49. Chapter 12 Knowledge Management and Collaborative systems
507
12.1 Opening Vignette: Expertise Transfer System to Train
Future Army Personnel 508
12.2 Introduction to Knowledge Management 512
Knowledge Management Concepts and Definitions 513
Knowledge 513
Explicit and Tacit Knowledge 515
12.3 Approaches to Knowledge Management 516
The Process Approach to Knowledge Management 517
The Practice Approach to Knowledge Management 517
Hybrid Approaches to Knowledge Management 518
Knowledge Repositories 518
12.4 Information Technology (IT) in Knowledge
Management 520
The KMS Cycle 520
Components of KMS 521
Technologies That Support Knowledge Management 521
12.5 Making Decisions in Groups: Characteristics, Process,
50. Benefits, and Dysfunctions 523
Characteristics of Groupwork 523
The Group Decision-Making Process 524
The Benefits and Limitations of Groupwork 524
12.6 Supporting Groupwork with Computerized Systems 526
An Overview of Group Support Systems (GSS) 526
Groupware 527
Time/Place Framework 527
12.7 Tools for Indirect Support of Decision Making 528
Groupware Tools 528
xviii Contents
Groupware 530
Collaborative Workflow 530
Web 2.0 530
Wikis 531
Collaborative Networks 531
12.8 Direct Computerized Support for Decision Making:
From Group Decision Support Systems to Group Support
51. Systems 532
Group Decision Support Systems (GDSS) 532
Group Support Systems 533
How GDSS (or GSS) Improve Groupwork 533
Facilities for GDSS 534
Chapter Highlights 535 • Key Terms 536
Questions for Discussion 536 • Exercises 536
▶ end-of-chApter ApplicAtion cAse Solving Crimes by Sharing
Digital Forensic Knowledge 537
References 539
Part V Big Data and Future Directions for Business
analytics 541
Chapter 13 Big Data and Analytics 542
13.1 Opening Vignette: Big Data Meets Big Science at CERN
543
13.2 Definition of Big Data 546
The Vs That Define Big Data 547
▶ ApplicAtion cAse 13.1 Big Data Analytics Helps Luxottica
Improve
Its Marketing Effectiveness 550
52. 13.3 Fundamentals of Big Data Analytics 551
Business Problems Addressed by Big Data Analytics 554
▶ ApplicAtion cAse 13.2 Top 5 Investment Bank Achieves
Single
Source of Truth 555
13.4 Big Data Technologies 556
MapReduce 557
Why Use MapReduce? 558
Hadoop 558
How Does Hadoop Work? 558
Hadoop Technical Components 559
Hadoop: The Pros and Cons 560
NoSQL 562
▶ ApplicAtion cAse 13.3 eBay’s Big Data