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, .
Curtis HillTopic 07 Assignment Long-Term Care ChartHA30.docxdorishigh
Curtis Hill
Topic 07 Assignment: Long-Term Care Chart
HA3010 - Introduction to US Healthcare Delivery
Jenifer Henke
May 24, 2020
HA3010 Topic 7 Assignment
Long Term Care Chart
Complete the chart comparing and contrasting long-term care services.
Type of LTC Service
Cost Effectiveness
Efficacy
Patient Satisfaction
Home care
Home care services are cost effective since the costs are flexible depending on one’s ability to pay.
Efficient in helping individuals with daily activities. It also helps patients with healthcare needs.
Relatively high
Community services
This is also considered cost effective since it can be provided by health care programs, social or other related providers.
Efficient to patients requiring help in daily activities.
Not efficient for provision of healthcare needs.
Relatively low
Supportive housing programs
Their cost ranges from low to medium, hence making them cost effective. This is especially the case when such is offered by the government.
Efficient to patients requiring help in daily activities.
Not efficient for provision of healthcare needs.
Relatively low
Continuing care retirement communities
The cost of CCRC is high as compared to the types discussed above. This is because it offers a full range of services.
Efficient for both healthcare and daily activities requirements.
High
Nursing homes
The cost of this type of long term care service is high. This is because the cost includes skilled services such as nursing and rehabilitation, meals and other support activities.
Efficient for both healthcare and daily activities requirements.
High
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 .
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.
Curtis HillTopic 07 Assignment Long-Term Care ChartHA30.docxdorishigh
Curtis Hill
Topic 07 Assignment: Long-Term Care Chart
HA3010 - Introduction to US Healthcare Delivery
Jenifer Henke
May 24, 2020
HA3010 Topic 7 Assignment
Long Term Care Chart
Complete the chart comparing and contrasting long-term care services.
Type of LTC Service
Cost Effectiveness
Efficacy
Patient Satisfaction
Home care
Home care services are cost effective since the costs are flexible depending on one’s ability to pay.
Efficient in helping individuals with daily activities. It also helps patients with healthcare needs.
Relatively high
Community services
This is also considered cost effective since it can be provided by health care programs, social or other related providers.
Efficient to patients requiring help in daily activities.
Not efficient for provision of healthcare needs.
Relatively low
Supportive housing programs
Their cost ranges from low to medium, hence making them cost effective. This is especially the case when such is offered by the government.
Efficient to patients requiring help in daily activities.
Not efficient for provision of healthcare needs.
Relatively low
Continuing care retirement communities
The cost of CCRC is high as compared to the types discussed above. This is because it offers a full range of services.
Efficient for both healthcare and daily activities requirements.
High
Nursing homes
The cost of this type of long term care service is high. This is because the cost includes skilled services such as nursing and rehabilitation, meals and other support activities.
Efficient for both healthcare and daily activities requirements.
High
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 .
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.
Running head PROJECT PLAN INCEPTION1PROJECT PLAN INCEPTION .docxjeanettehully
Running head: PROJECT PLAN INCEPTION 1
PROJECT PLAN INCEPTION 2
Information Technology and Business
Babatunde Ogunade
CIS499: Information System Capston
Professor Reddy Urimindi
October 13, 2019
Information Technology and Business
Project Introduction
The very core operation of this company involves the collection and analysis of data through a currently limited technological infrastructure. The basis of this business may focus on leadership structure, the type of industry, business culture, core vision and mission including objectives. The company has a Chief Executive Officer (CEO) as the highest rank, four Information Technology experts and other employees. Marketing can, therefore, categorize this company as a service industry company with a core vision of a 60 percent growth in the next eighteen months and mission of redesigning its information technology to fulfill its organizational needs.
Product features, new market product, differentiation techniques, and value addition defines the type of business which the company is operating. The assessment of its product features which involves data indicate that the opportunities focus on marketing. In the continued operations of the company, the management is not foreseeing any shift from its original product but is rather fixing a differentiation technique within six months. An addition in product value should be achieved by employing an exclusively new technology based on a hybrid model, hosted solution or on-site solution.
The idea of integrating technologies from other partners to realize cost-effective outcomes and best operations outlines the outsourcing policies as far as new technology is concerned. Consequently, future intentions to acquire services such as Software-as-a-Service (SaaS) and cloud computing technologies may involve the adoption of knowledge and skills from outside the country, therefore, describing offshoring activities. As asserted by Aithal, (2017), the success of fulfilling the effective company operation, these activities are important.
One of the skilled personnel in the company is the Chief Information Officer (CIO) whose basic role is to keep a charge on the computer systems and information technology (IT) necessary in ensuring a company’s goals and objectives. Additionally, the CEO has devolved the responsibility of security protocols to the CIO in the process of more digitized frameworks. Other personnel includes the company CEO tasked with communicating to partners, creating the company mission and vision, and generally heading the implementation of both long term and short term objectives. The other information technician is mandated in both the installation and configuration of computer hardware and software.
Based on the current collection and analysis method, data on the customer, marketing, lifecycle, website engagement, and funnel analytics. In broad-spectrum, funnel analytics provide customer information through registration, ...
[MU630] 004. Business Intelligence & Decision SupportAriantoMuditomo
Copyright Notice:
This presentation is prepared by Author for Perbanas Institute as a part of Author Lecture Series. It is to be used for educational and non-commercial purposes only and is not to be changed, altered, or used for any commercial endeavor without the express written permission from Author and/or Perbanas Institute. Appropriate legal action may be taken against any person, organization, or entity attempting to misrepresent, charge, or profit from the educational materials contained here.
Authors are allowed to use their own articles without seeking permission from any person, organization, or entity.
DMA 2014: 6 Steps to Integrate Your Big DataSameer Khan
The Big Data phenomenon was all about the collection of masses and masses of data: it was a technology challenge. But for most of us, this is no longer a problem – we know how to collect the data – the challenge now is one of processing the data, to make smart data work for us. In this session, IBM’s Sameer Khan will outline an action plan to manage your data and make it smart. He will be ably supported by Andrew Bailey, who will bring his experience with using smart data for integrated marketing campaigns to show you how it is put into action at a company like FedEx.
Empowering your Enterprise with a Self-Service Data Marketplace (ASEAN)Denodo
Watch full webinar here: https://bit.ly/3uqcAN0
Self-service is a major goal of modern data strategists. A successfully implemented self-service initiative means that business users have access to holistic and consistent views of data regardless of its location, source or type. As data unification and data collaboration become key critical success factors for organizations, data catalogs play a key role as the perfect companion for a virtual layer to fully empower those self-service initiatives and build a self-service data marketplace requiring minimal IT intervention.
Denodo’s Data Catalog is a key piece in Denodo’s portfolio to bridge the gap between the technical data infrastructure and business users. It provides documentation, search, governance and collaboration capabilities, and data exploration wizards. It provides business users with the tool to generate their own insights with proper security, governance, and guardrails.
In this session we will cover:
- The role of a virtual semantic layer in self-service initiatives
- Key ingredients of a successful self-service data marketplace Self-service (consumption) vs. inventory catalogs
- Best practices and advanced tips for successful deployment
- A Demonstration: Product Demo
- Examples of customers using Denodo’s Data Catalog to enable self-service initiatives
March 2008 presentation from a BEA Systems webinar about expertise location. Pathways lets users tag content and people, as well as bookmark internal content and external websites. It applies an algorithm to give ratings to users and information in the system.
Data Science - Part I - Sustaining Predictive Analytics CapabilitiesDerek Kane
This is the first lecture in a series of data analytics topics and geared to individuals and business professionals who have no understand of building modern analytics approaches. This lecture provides an overview of the models and techniques we will address throughout the lecture series, we will discuss Business Intelligence topics, predictive analytics, and big data technologies. Finally, we will walk through a simple yet effective example which showcases the potential of predictive analytics in a business context.
A brief introduction to DataScience with explaining of the concepts, algorithms, machine learning, supervised and unsupervised learning, clustering, statistics, data preprocessing, real-world applications etc.
It's part of a Data Science Corner Campaign where I will be discussing the fundamentals of DataScience, AIML, Statistics etc.
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 ...
This presentation is an Introduction to the importance of Data Analytics in Product Management. During this talk Etugo Nwokah, former Chief Product Officer for WellMatch, covered how to define Data Analytics why it should be a first class citizen in any software organization
2/21/2020 Soil Colloids (Chapter 8) Notes - AGRI1050R50: Introduction to Soil Science (2020S)
https://gotoclass.tnecampus.org/d2l/le/content/8094442/viewContent/60403389/View 1/12
Soil Colloids (Chapter 8) Notes
Soil Colloids (Chapter 8) Notes
Did you know ....
Did you know soil fertility or the ability for a soil to provide nutrients is seated in the type of minerals it
contains? Chapter 8 will cover the various types of soil colloids including all the layer and non-layer
silicates, cation exchange, anion exchange, and sorption.
Lecture content notes are accompanied by videos listed below the notes in each submodule (e.g. Soil
Colloids (Chapter 8) Videos A though H). Print or download lecture notes then view videos in
succession alongside lecture content and add additional notes from each video. The start of each
video is noted in parenthesis (e.g. Content for Video A) within each lecture note set and contains
lecture content through the note for the next video (e.g. Content for Video B).
Figures and tables unless specifically referrenced are from the course text, Nature and Property of
Soils, 14th Edition, Brady and Weil.
Content Video A
Soil Colloids
Smallest soil particles < 1 µm
Surface area - LARGE
Surface charge - CEC
Adsorb water
AGRI1050R50: Introduction to Soil Science (2020S) LH
https://gotoclass.tnecampus.org/d2l/le/content/8094442/navigateContent/176/Previous?pId=60403304
https://gotoclass.tnecampus.org/d2l/le/content/8094442/navigateContent/176/Next?pId=60403304
https://gotoclass.tnecampus.org/d2l/common/dialogs/quickLink/quickLink.d2l?ou=8094442&type=content&rcode=TBR-23958617
https://gotoclass.tnecampus.org/d2l/home/8094442
2/21/2020 Soil Colloids (Chapter 8) Notes - AGRI1050R50: Introduction to Soil Science (2020S)
https://gotoclass.tnecampus.org/d2l/le/content/8094442/viewContent/60403389/View 2/12
Types of Colloids
Crystalline Silicate clays: ordered, crystalline, layers
Non-crystalline silicate clays: non-ordered, layers, volcanic
Iron/Aluminum Oxides – weathered soils, less CEC
Humus – OM, not mineral or crystalline, high CEC
Soil Colloids
Content Video B
Layer Silicates - Construction
Phyllosillicates
Tetrahedral Sheets
1 Si with 4 Oxygen
Share basal oxygen
Form sheets
Octahedral Sheets
6 Oxygen with Al3+ or Mg 2+
Di T i O t h d l b d # f di ti i
https://gotoclass.tnecampus.org/d2l/common/dialogs/quickLink/quickLink.d2l?ou=8094442&type=content&rcode=TBR-23958618
2/21/2020 Soil Colloids (Chapter 8) Notes - AGRI1050R50: Introduction to Soil Science (2020S)
https://gotoclass.tnecampus.org/d2l/le/content/8094442/viewContent/60403389/View 3/12
Di or Tri Octahedral based on # of coordinating ions
http://web.utk.edu/~drtd0c/Soil%20Colloids.pdf
http://web.utk.edu/~drtd0c/Soil%20Colloids.pdf
2/21/2020 Soil Colloids (Chapter 8) Notes - AGRI1050R50: Introduction to Soil Science (2020S)
https://gotoclass.tnecampus.org/d2l/le/content/8094442/viewContent/60403389/View 4/12
Size .
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.
Running head PROJECT PLAN INCEPTION1PROJECT PLAN INCEPTION .docxjeanettehully
Running head: PROJECT PLAN INCEPTION 1
PROJECT PLAN INCEPTION 2
Information Technology and Business
Babatunde Ogunade
CIS499: Information System Capston
Professor Reddy Urimindi
October 13, 2019
Information Technology and Business
Project Introduction
The very core operation of this company involves the collection and analysis of data through a currently limited technological infrastructure. The basis of this business may focus on leadership structure, the type of industry, business culture, core vision and mission including objectives. The company has a Chief Executive Officer (CEO) as the highest rank, four Information Technology experts and other employees. Marketing can, therefore, categorize this company as a service industry company with a core vision of a 60 percent growth in the next eighteen months and mission of redesigning its information technology to fulfill its organizational needs.
Product features, new market product, differentiation techniques, and value addition defines the type of business which the company is operating. The assessment of its product features which involves data indicate that the opportunities focus on marketing. In the continued operations of the company, the management is not foreseeing any shift from its original product but is rather fixing a differentiation technique within six months. An addition in product value should be achieved by employing an exclusively new technology based on a hybrid model, hosted solution or on-site solution.
The idea of integrating technologies from other partners to realize cost-effective outcomes and best operations outlines the outsourcing policies as far as new technology is concerned. Consequently, future intentions to acquire services such as Software-as-a-Service (SaaS) and cloud computing technologies may involve the adoption of knowledge and skills from outside the country, therefore, describing offshoring activities. As asserted by Aithal, (2017), the success of fulfilling the effective company operation, these activities are important.
One of the skilled personnel in the company is the Chief Information Officer (CIO) whose basic role is to keep a charge on the computer systems and information technology (IT) necessary in ensuring a company’s goals and objectives. Additionally, the CEO has devolved the responsibility of security protocols to the CIO in the process of more digitized frameworks. Other personnel includes the company CEO tasked with communicating to partners, creating the company mission and vision, and generally heading the implementation of both long term and short term objectives. The other information technician is mandated in both the installation and configuration of computer hardware and software.
Based on the current collection and analysis method, data on the customer, marketing, lifecycle, website engagement, and funnel analytics. In broad-spectrum, funnel analytics provide customer information through registration, ...
[MU630] 004. Business Intelligence & Decision SupportAriantoMuditomo
Copyright Notice:
This presentation is prepared by Author for Perbanas Institute as a part of Author Lecture Series. It is to be used for educational and non-commercial purposes only and is not to be changed, altered, or used for any commercial endeavor without the express written permission from Author and/or Perbanas Institute. Appropriate legal action may be taken against any person, organization, or entity attempting to misrepresent, charge, or profit from the educational materials contained here.
Authors are allowed to use their own articles without seeking permission from any person, organization, or entity.
DMA 2014: 6 Steps to Integrate Your Big DataSameer Khan
The Big Data phenomenon was all about the collection of masses and masses of data: it was a technology challenge. But for most of us, this is no longer a problem – we know how to collect the data – the challenge now is one of processing the data, to make smart data work for us. In this session, IBM’s Sameer Khan will outline an action plan to manage your data and make it smart. He will be ably supported by Andrew Bailey, who will bring his experience with using smart data for integrated marketing campaigns to show you how it is put into action at a company like FedEx.
Empowering your Enterprise with a Self-Service Data Marketplace (ASEAN)Denodo
Watch full webinar here: https://bit.ly/3uqcAN0
Self-service is a major goal of modern data strategists. A successfully implemented self-service initiative means that business users have access to holistic and consistent views of data regardless of its location, source or type. As data unification and data collaboration become key critical success factors for organizations, data catalogs play a key role as the perfect companion for a virtual layer to fully empower those self-service initiatives and build a self-service data marketplace requiring minimal IT intervention.
Denodo’s Data Catalog is a key piece in Denodo’s portfolio to bridge the gap between the technical data infrastructure and business users. It provides documentation, search, governance and collaboration capabilities, and data exploration wizards. It provides business users with the tool to generate their own insights with proper security, governance, and guardrails.
In this session we will cover:
- The role of a virtual semantic layer in self-service initiatives
- Key ingredients of a successful self-service data marketplace Self-service (consumption) vs. inventory catalogs
- Best practices and advanced tips for successful deployment
- A Demonstration: Product Demo
- Examples of customers using Denodo’s Data Catalog to enable self-service initiatives
March 2008 presentation from a BEA Systems webinar about expertise location. Pathways lets users tag content and people, as well as bookmark internal content and external websites. It applies an algorithm to give ratings to users and information in the system.
Data Science - Part I - Sustaining Predictive Analytics CapabilitiesDerek Kane
This is the first lecture in a series of data analytics topics and geared to individuals and business professionals who have no understand of building modern analytics approaches. This lecture provides an overview of the models and techniques we will address throughout the lecture series, we will discuss Business Intelligence topics, predictive analytics, and big data technologies. Finally, we will walk through a simple yet effective example which showcases the potential of predictive analytics in a business context.
A brief introduction to DataScience with explaining of the concepts, algorithms, machine learning, supervised and unsupervised learning, clustering, statistics, data preprocessing, real-world applications etc.
It's part of a Data Science Corner Campaign where I will be discussing the fundamentals of DataScience, AIML, Statistics etc.
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 ...
This presentation is an Introduction to the importance of Data Analytics in Product Management. During this talk Etugo Nwokah, former Chief Product Officer for WellMatch, covered how to define Data Analytics why it should be a first class citizen in any software organization
2/21/2020 Soil Colloids (Chapter 8) Notes - AGRI1050R50: Introduction to Soil Science (2020S)
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Soil Colloids (Chapter 8) Notes
Soil Colloids (Chapter 8) Notes
Did you know ....
Did you know soil fertility or the ability for a soil to provide nutrients is seated in the type of minerals it
contains? Chapter 8 will cover the various types of soil colloids including all the layer and non-layer
silicates, cation exchange, anion exchange, and sorption.
Lecture content notes are accompanied by videos listed below the notes in each submodule (e.g. Soil
Colloids (Chapter 8) Videos A though H). Print or download lecture notes then view videos in
succession alongside lecture content and add additional notes from each video. The start of each
video is noted in parenthesis (e.g. Content for Video A) within each lecture note set and contains
lecture content through the note for the next video (e.g. Content for Video B).
Figures and tables unless specifically referrenced are from the course text, Nature and Property of
Soils, 14th Edition, Brady and Weil.
Content Video A
Soil Colloids
Smallest soil particles < 1 µm
Surface area - LARGE
Surface charge - CEC
Adsorb water
AGRI1050R50: Introduction to Soil Science (2020S) LH
https://gotoclass.tnecampus.org/d2l/le/content/8094442/navigateContent/176/Previous?pId=60403304
https://gotoclass.tnecampus.org/d2l/le/content/8094442/navigateContent/176/Next?pId=60403304
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https://gotoclass.tnecampus.org/d2l/home/8094442
2/21/2020 Soil Colloids (Chapter 8) Notes - AGRI1050R50: Introduction to Soil Science (2020S)
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Types of Colloids
Crystalline Silicate clays: ordered, crystalline, layers
Non-crystalline silicate clays: non-ordered, layers, volcanic
Iron/Aluminum Oxides – weathered soils, less CEC
Humus – OM, not mineral or crystalline, high CEC
Soil Colloids
Content Video B
Layer Silicates - Construction
Phyllosillicates
Tetrahedral Sheets
1 Si with 4 Oxygen
Share basal oxygen
Form sheets
Octahedral Sheets
6 Oxygen with Al3+ or Mg 2+
Di T i O t h d l b d # f di ti i
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2/21/2020 Soil Colloids (Chapter 8) Notes - AGRI1050R50: Introduction to Soil Science (2020S)
https://gotoclass.tnecampus.org/d2l/le/content/8094442/viewContent/60403389/View 3/12
Di or Tri Octahedral based on # of coordinating ions
http://web.utk.edu/~drtd0c/Soil%20Colloids.pdf
http://web.utk.edu/~drtd0c/Soil%20Colloids.pdf
2/21/2020 Soil Colloids (Chapter 8) Notes - AGRI1050R50: Introduction to Soil Science (2020S)
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Size .
20 Other Conditions That May Be a Focus of Clinical AttentionV-c.docxRAJU852744
20 Other Conditions That May Be a Focus of Clinical Attention
V-codes and z-codes
V-codes and Z-codes are conditions that may be the focus of clinical attention but are not considered mental disorders. They correspond to International Classification of Diseases, Ninth Revision, Clinical Modification ICD-9-CM (V-codes) and International Classification of Diseases, Tenth Revision, Clinical Modification ICD-10-CM (Z-codes that become effective in 2015. In most instances, third-party payers do not cover charges for delivering services to an individual if the diagnosis is solely a V- or Z-code alone. If the V- or Z-code is not the primary diagnosis then it should be documented following the primary diagnosis. In addition, when writing the psychosocial assessment any psychosocial and cultural factors that might impact the client's diagnosis should be documented. The psychosocial stressors reflected in these diagnoses are widespread across all classes and cultures and have been shown to impact all aspects of an individual's life from the physical and psychological to the financial. Furthermore, these conditions have been shown to significantly impact the diagnosis and outcome for a multitude of mental and medical disorders. V- and Z-codes are grouped into numerous categories including: relational problems, problems related to abuse/neglect, educational and occupational problems, housing and economic problems, problems related to the social environment, problems related to the legal system, other counseling services, other psychosocial, personal and environmental problems, and problems of personal history (APA, 2013).
Broadly speaking, the category “Relational Problems” describes interactional problems between family members (e.g., parent/caregiver-child) or partners that result in significant impairment of family functioning or development of symptoms in the distressed individual, spouses, siblings, or other family members. Relational problems are broken down into two categories, Problems Related to Family Upbringing and Other Problems Related to Primary Support Group. For example, in the first category a Parent-Child Relational Problem involves interactional problems between one or both parents and a child that lead to dysfunction in behavioral (e.g., inadequate protection, overprotection), cognitive (e.g., antagonism toward or blaming of the other) or affective (e.g., feeling sad and angry) realms. Here, the critical factor is the quality of the parent-child relationship or when the dysfunction in this relationship is impacting the course and outcome of a psychological or medical condition. Other examples include Sibling Relational Problem, Upbringing Away from Parents, and Child Affected by Parental Relationship Distress. Similarly, family relationships and interactional patterns leading to problems related to primary support group include Partner Relational Problem, Disruption of Family by Separation/Divorce, High Expressed Emotion Level with.
223 Case 53 Problems in Pasta Land by Andres Sous.docxRAJU852744
223
Case 53
Problems in Pasta Land
by
Andres Sousa-Poza
Old Dominion University
The Food Factory has been operating in an underdeveloped country for approximately 10
years.1 Its parent corporation specializes in wheat milling, and it started the pasta factory as a
“side-line” operation to process lower quality wheat flour, which is a by-product of the
normal milling process. This low-gluten flour is generally not suitable for the production of
bread or for direct sale to consumers.
In 2009, the pasta division is confronted with a major problem. It is too successful!
The factory was designed around the mill. Production capacities matched the amount of
effluent from the mill rather than coming from a sound marketing strategy. As shown in
Table 53-1, by 2006, the pasta plant was no longer able to effectively serve existing
customers. The plant that was designed to produce 600 tons of pasta per month on two
production lines is now facing average monthly orders of approximately 800 tons.
Furthermore, the corporate director of marketing estimates that orders could easily be
increased to 1400 to 1800 tons per month.
1 All monies used in this case are in the local currency, which is one of the more than 40 countries in
the world that use the $ symbol and most of which are called dollars.
Cases in Engineering Economy 2nd by Peterson & Eschenbach
224
Table 53-1 Average Monthly Orders/Production
0
100
200
300
400
500
600
700
800
900
Year
A
ve
ra
ge
m
on
th
ly
o
rd
er
s/
pr
od
uc
tio
n
Orders 200 280 360 490 450 580 620 710 760 800
Production 200 270 365 500 440 575 590 610 580 570
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
Another challenge facing the factory is that the initial equipment was refurbished, not
new, and it is now antiquated and seriously dilapidated. Unless the plant is shut down,
equipment replacement is going to be required. The existing equipment was already a
technological generation behind when it was bought. During the last 10 years a new
generation of equipment has been developed based on high-temperature drying. The new
technology is much more suited for use with low-quality (low-gluten) flour and semolina.
New machinery is significantly more efficient. It requires fewer workers, has lower relative
energy consumption, and produces less waste. The pasta plant still maintains a price lead
through the low cost at which it is able to obtain raw materials from the corporate wheat mill,
but this barely compensates for the plant’s low efficiency.
The new technology is also enabling competitors to use low-quality, low-cost raw
materials and still produce a reasonably high-quality end product. Ultimately, this means that
the cost of higher quality pasta has dropped significantly in price, and the quality of the low-
cost pasta is increasing significantly. The pasta factory’s market is customers wit.
22-6 Reporting the Plight of Depression FamiliesMARTHA GELLHOR.docxRAJU852744
22-6 | Reporting the Plight of Depression Families
MARTHA GELLHORN, Field Report to Harry Hopkins (1934)
1. From Martha Gellhorn to Harry Hopkins, Report, Gaston County, North Carolina, November 11, 1934, Franklin D. Roosevelt Library, Harry Hopkins Papers, Box 66. Online transcript available at http://newdeal.feri.org/hopkins/hop08.htm.
Journalist and novelist Martha Gellhorn’s heartrending field report describing impoverished Gastonia, North Carolina, families vividly captures the desperate hope of depression-era families. Hired by Harry Hopkins, Franklin Roosevelt’s point man for federal relief efforts, Gellhorn detailed the enormous challenge facing the administration. Compounding the epic humanitarian crisis she encountered was the political opposition, which she singled out as one among many obstacles hampering relief efforts.
All during this trip [to North Carolina] I have been thinking to myself about that curious phrase “red menace,” and wondering where said menace hid itself. Every house I visited — mill worker or unemployed — had a picture of the President. These ranged from newspaper clippings (in destitute homes) to large colored prints, framed in gilt cardboard. The portrait holds the place of honour over the mantel. . . . He is at once God and their intimate friend; he knows them all by name, knows their little town and mill, their little lives and problems. And, though everything else fails, he is there, and will not let them down.
I have been seeing people who, according to almost any standard, have practically nothing in life and practically nothing to look forward to or hope for. But there is hope; confidence, something intangible and real: “the president isn’t going to forget us.”
Let me cite cases: I went to see a woman with five children who was living on relief ($3.40 a week). Her picture of the President was a small one, and she told me her oldest daughter had been married some months ago and had cried for the big, coloured picture as a wedding present. The children have no shoes and that woman is terrified of the coming cold as if it were a definite physical entity. There is practically no furniture left in the home, and you can imagine what and how they eat. But she said, suddenly brightening, “I’d give my heart to see the President. I know he means to do everything he can for us; but they make it hard for him; they won’t let him.” I note this case as something special; because here the faith was coupled with a feeling (entirely sympathetic) that the President was not entirely omnipotent.
I have been seeing mill workers; and in every mill when possible, the local Union president. There has been widespread discrimination in the south; and many mills haven’t re-opened since the strike. Those open often run on such curtailment that workers are getting from 2 to 3 days work a week. The price of food has risen (especially the kind of food they eat: fat-back bacon, flour, meal, sorghum) as high as 100%. It is getting cold;.
2018 4th International Conference on Green Technology and Sust.docxRAJU852744
2018 4th International Conference on Green Technology and Sustainable Development (GTSD)
130
�
Abstract - The Vietnamese government have plan to develop the
wind farms with the expected capacity of 6 GW by 2030. With the
high penetration of wind power into power system, wind power
forecasting is essentially needed for a power generation
balancing in power system operation and electricity market.
However, such a tool is currently not available in Vietnamese
wind farms as well as electricity market. Therefore, a short-term
wind power forecasting tool for 24 hours has been created to fill
in this gap, using artificial neural network technique. The neural
network has been trained with past data recorded from 2015 to
2017 at Tuy Phong wind farm in Binh Thuan province of Viet
Nam. It has been tested for wind power prediction with the input
data from hourly weather forecast for the same wind farm. The
tool can be used for short-term wind power forecasting in
Vietnamese power system in a foreseeable future.
Keywords: power system; wind farm; wind power forecasting;
neural network; electricity market.
I. NECESITY OF WIND POWER FORECASTING
Today, the integration of wind power into the existing
grid is a big issue in power system operation. For the system
operators, power generation curve of wind turbines is a
necessary information in the power sources balancing. From
the dispatchers’ point of view, wind power forecast errors
will impact the system net imbalances when the share of
wind power increases, and more accurate forecasts mean less
regulating capacity will be activated from the real time
electricity market [1]. In the deregulated market, day-ahead
electricity spot prices are also affected by day-ahead wind
power forecasting [2]. Wind power forecasting is also
essential in reducing the power curtailment, supporting the
ancillary service. However, due to uncertainty of wind speed
and weather factors, the wind power is not easy to predict.
In recent years, many wind power forecasting methods
have been proposed. In [3], a review of different approaches
for short-term wind power forecasting has been introduced,
including statistical and physical methods with different
models such as WPMS, WPPT, Prediktor, Zephyr, WPFS,
ANEMOS, ARMINES, Ewind, Sipreolico. In [4], [5], the
methods, models of wind power forecasting and its impact on
*Research supported by Gesellschaft fuer Internationale
Zusammenarbeit GmbH (GIZ).
D. T. Viet is with the University of Danang, Vietnam (email:
[email protected]).
V. V. Phuong is with the University of Danang, Vietnam (email:
[email protected]).
D. M. Quan is with the University of Danang, Vietnam (email:
[email protected]).
A. Kies is with the Frankfurt Institute for Advanced Studies, Germany
(email: [email protected] uni-frankfurt.de).
B. U. Schyska is with the Carl von Ossietzky Universität Oldenburg,
Germany (email: [email protected]).
Y. K. Wu i.
202 S.W.3d 811Court of Appeals of Texas,San Antonio.PROG.docxRAJU852744
202 S.W.3d 811
Court of Appeals of Texas,
San Antonio.
PROGRESSIVE COUNTY MUTUAL INSURANCE
COMPANY, Appellant,
v.
Hector Raul TREVINO and Mario Moyeda,
Appellees.
No. 04–05–00113–CV.
|
June 28, 2006.
|
Rehearing Overruled July 31, 2006.
.
200 wordsResearch Interest Lack of minorities in top level ma.docxRAJU852744
200 words
Research Interest: Lack of minorities in top level management positions
Describe why and how a qualitative approach may be appropriate for your area of interest for your research. Include a rationale for each proposed use of qualitative inquiry.
.
2019 14th Iberian Conference on Information Systems and Tech.docxRAJU852744
2019 14th Iberian Conference on Information Systems and Technologies (CISTI)
19 – 22 June 2019, Coimbra, Portugal
ISBN: 978-989-98434-9-3
How ISO 27001 can help achieve GDPR compliance
Isabel Maria Lopes
Polytechnic Institute of Bragança, Bragança, Portugal
UNIAG, Polytechnic Institute of Bragança, Portugal
ALGORITMI Centre, Minho University, Guimarães,
Portugal
[email protected]
Pedro Oliveira
Polytechnic Institute of Bragança, Bragança, Portugal
[email protected]
Teresa Guarda
Universidad Estatal Península de Santa Elena – UPSE, La
Libertad, Ecuador
Universidad de las Fuerzas Armadas – ESPE, Sangolqui,
Quito, Equador
ALGORITMI Centre, Minho University, Guimarães,
Portugal
[email protected]
Abstract — Personal Data Protection has been among the most
discussed topics lately and a reason for great concern among
organizations. The EU General Data Protection Regulation
(GDPR) is the most important change in data privacy regulation
in 20 years. The regulation will fundamentally reshape the way in
which data is handled across every sector. The organizations had
two years to implement it. As referred by many authors, the
implementation of the regulation has not been an easy task for
companies. The question we aim to answer in this study is how far
the implementation of ISO 27001 standards might represent a
facilitating factor to organizations for an easier compliance with
the regulation. In order to answer this question, several websites
(mostly of consulting companies) were analyzed, and the aspects
considered as facilitating are listed in this paper.
Keywords - regulation (EU) 2016/679; general data protection
regulation; ISO/IEC 27001.
I. INTRODUCTION
In recent years, data protection has become a forefront issue
in cyber security. The issues introduced by recurring
organizational data breaches, social media and the Internet of
Things (IoT) have raised the stakes even further [1, 2]. The EU
GDPR, enforced from May 25 2018, is an attempt to address
such data protection. The GDPR makes for stronger, unified data
protection throughout the EU.
The EU GDPR states that organizations must adopt
appropriate policies, procedures and processes to protect the
personal data they hold.
The International Organization for Standardization (ISO)
/International Electrotechnical Commission (IEC) 27000 series
is a set of information security standards that provide best-
practice recommendations for information security management
[3].
This international standard for information security, ISO
27001, provides an excellent starting point for achieving the
technical and operational requirements necessary to reduce the
risk of a breach.
Not all data is protected by the GDPR, since it is only
applicable to personal data. This is defined in Article 4 as
follows [4]:
“personal data” means any information relating to an
identified or identifiable natural person (’data subject’); an
identifiable.
200520201ORG30002 – Leadership Practice and Skills.docxRAJU852744
20/05/2020
1
ORG30002 – Leadership Practice
and Skills
Topic: Cross-cultural Leadership
Week 10
Readings for this week….
◦ Week 10 Topic: Cross-Cultural Leadership
◦ Chapter 11, Daft
◦ Javidan, M., Dorfman, P.W., De Luque, M.S. & House R.J. (2006). In the eye of the beholder:
Cross cultural lessons in leadership from Project GLOBE - Academy of Management Perspect ive,
20(1), 67-90
http://ezproxy.lib.swin.edu.au/login?url=http://search.ebscohost.com/login.aspx?direct=true&db
=bth&AN=19873410&site=ehost-live&scope=site
◦ Randel, A.E., et al. (2018). Inclusive leadership: Realizing posit ive outcomes through
belongingness and being valued for uniqueness, Human Resource Management Review, 28:190-
203. http://ezproxy.lib.swin.edu.au/login?url=https://doi.org/10.1016/j.hrmr.2017.07.002
◦ Hoffman, R., Yeh, C. & Casnocha, B. (2019). Learn from People, Not Classes Whom do you know,
and what can they teach you? Harvard Business Review, Mar – Apr 2019.
http://ezproxy.lib.swin.edu.au/login?url=http://search.ebscohost.com/login.aspx?direct=true&db
=bth&AN=134875248&site=ehost-live&scope=site
Work Force Trends
With more multi generational workplaces, work forces are becoming more
diverse and cultures of inclusion more common
Women leaders in Global Businesses showing an increasing trend
Globalization is compelling businesses to send more workers to other countries
Leaders are traveling and working abroad in greater numbers
Workers with international experience and skills are increasingly more sought-
after in the workplace
Visualising the Iceberg Model of Culture
(source:http://opengecko.com/interculturalism/visualising-the-iceberg-model-of-
culture/) The iceberg model of culture
has been arrived at through
the work of many theorists,
including those referenced
below:
◦ French, W., & Bell, C. (1995).
Organization development.
(5th Ed.). [Englewood Cliffs,
NJ: Prentice-Hall
International]
◦ Hall, E. T. (1976) Beyond
Culture [New York:
Doubleday]
◦ Selfridge, R., Sokolik, S.
(1975) “A comprehensive
v iew of organizational
management”. MSU
Business Topics, 23(1), 46-61
◦ Weaver, G. R. (1986).
“Understanding and coping
with cross-cultural
adjustment stress”. In Paige
R. M. (Ed.), Cross-Cultural
Orientation, New
Conceptualizations and
Applications. [Lanham, MD:
University Press of America]
https://monash.rl.talis.com/items/C3CF1A2F-948C-AA0D-89D9-8498251A8662.html?referrer=/lists/86EF2F87-E1BB-F832-BEB3-34F354D3DAC6.html?draft#item-C3CF1A2F-948C-AA0D-89D9-8498251A8662
http://ezproxy.lib.swin.edu.au/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=19873410&site=ehost-live&scope=site
http://ezproxy.lib.swin.edu.au/login?url=https://doi.org/10.1016/j.hrmr.2017.07.002
http://ezproxy.lib.swin.edu.au/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=134875248&site=ehost-live&scope=site
20/05/2020
2
Who is a Multicultural Leader?
A leader with skills.
2/18/2020 Sample Content Topic
https://purdueglobal.brightspace.com/d2l/le/content/115691/viewContent/9226875/View 1/1
Trouble at 3Forks
Introduction: The foreclosure process can differ for deeds
versus mortgages. You will conduct research to determine
these differences since it is not only covered in the real estate
exam, but it is important to know this process in professional
practice.
Scenario: Henri and Lila own a restaurant which the
government has caused to close due to widening the road in
front of their establishment. Since this is the main source of
their income, and has caused Lila and Henri to stop payments
on their mortgage, address the following questions.
Checklist:
Explain the action that Henri and Lila should expect from the
bank regarding their property.
Describe how the banks actions would differ if it was a deed of
trust rather than a mortgage.
Respond in a minimum of 600–850-word essay with additional
title and reference pages using APA format and citation style.
Access the Unit 4 Assignment grading rubric.
Submit your response to the Unit 4 Assignment Dropbox.
Assignment Details
https://kapextmediassl-a.akamaihd.net/business/MT431/1904c/rubrics/u4_rubric.pdf
Mitchell, Taylor N.
Donaldson, Jayda N
Recommended Presentation Outline
My Name is …
The title of my article is…
I found it in…
My article is relevant and interesting because….
The Economics Article
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The study of the allocation of scarce resources: implies a cost to every action
Basic assumption
People are rational
People act to maximize their happiness
Economics is predictive
5
Economic Modeling
"The theory of economics does not furnish a body of settled conclusions immediately applicable to policy. It is a method rather than a doctrine, an apparatus of the mind, a technique of thinking which helps its possessor to draw correct conclusions." (John Maynard Keynes)
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Firm Maximizes profits
Max: p = Revenue - Costs
Max: p = P(Q)* Q- C(Q)
First Order Conditions:
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Assumptions of Perfect Competition
Free Entr.
21 hours agoMercy Eke Week 2 Discussion Hamilton Depression.docxRAJU852744
21 hours ago
Mercy Eke
Week 2 Discussion: Hamilton Depression Rating Scale
COLLAPSE
Top of Form
Depression or Major Depressive Disorder is considered as a mental health disorder that negatively impacts how an individual feel, think and behave. Individuals who suffer from depression exhibit feelings of sadness and loss in interest in once enjoyed activities (Parekh. 2017). It can cause different kinds of emotional and physical problems and can minimize an individual’s ability to be functional in their daily routines. Annually, approximately 6.7% of adults are impacted by depression. It is estimated that 16.6% of individuals will experience depression at some time in their life (Parekh. 2017). Depression is said to manifest at any time, but on average, the first manifestation occurs during the late teens to mid-20s. The female population is susceptible to experience depression than the male population. Some research indicated that one-third of the female population would experience a major depressive episode in their lifetime (Parekh. 2017).
Among all the mental disorders, depression is one of the most treatable. It is estimated that between 80-90 % of individuals suffering from depression respond well to treatment and experienced remission of their symptoms (Parekh. 2017). As a mental health professional, prior to deciphering diagnosis and initiating diagnosis, it is paramount to conduct a complete diagnostic evaluation, which includes an interview and, if necessary, a physical examination (Parekh. 2017). Blood tests can be conducted to ascertain that depression is not precipitated by a medical condition like thyroid dysfunction. The evaluation is to identify specific symptoms, medical and family history, cultural factors, and environmental factors to derive a diagnosis and establish a treatment plan (Parekh. 2017). One of the assessment tools for depression is the Hamilton Depression Rating Scale. In this discussion, I will be discussing the psychometric properties of the Hamilton Depression Rating Scale and elaborate on when it is appropriate to utilize this assessment tool with clients, including whether the tool can be utilized to evaluate the efficacy of psychopharmacologic medications.
The Hamilton Depression Rating Scale (HDRS) was introduced in early 1960. It has been considered as a gold standard in depression studies and a preferred scale in the evaluation of depression treatment. It is the most vastly utilized observer-rated depression scale worldwide (Vindbjerg.et.al., 2019). The HDRS was initially created to measure symptoms severity in depressed inpatient; however, the 17-item HAM-D has advanced in over five decades into 11 modified versions that have been administered to various patient populations in an array of psychiatric, medical, and other research settings (Rohan.et.al., 2016). There are two most common versions with either 17 or 21 items and is scored between 0-4 points. Each item assists mental health professionals or c.
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Running Head: SERVER VIRTUALIZATION 1
SERVER VIRTUALIZATION 8
Week 4 Assignment
Technet Case Study for Virtualization Mohana Murali Krishna Karnati
University of the Cumberlands
Technet Case Study for Virtualization
Technet is a hypothetical business in the storage manufacturing industry. This paper intend to elaborate the server virtualization concept using Microsoft
virtualization software from Windows server 2012R2. Organization’s Preparedness for Virtualization. As of now, the IT system design is a mishmash of old
frameworks that were obtained through various acquisitions of different providers in the storage industry. In any case, these old frameworks are aging and will soon
need to be upgraded. Generally, these old frameworks support applications that have been in service for about 10 years. The IT system situated in one of Technet
branch in Asia for instance comprise of old servers that have been in service for the last 5 years. These old servers were launched to support production and
productivity applications. The expense for permit of these old applications are presently being inspected to check whether they can be dropped and the
information moved to current Technet Enterprise Resource Planning (ERP) applications. Consequently, since several IT related components are potential
contender for upgrading, this makes the likelihood of changing over current physical server farms into virtualized computing resources appropriate. Microsoft
Licensing of Virtualized Environments
Datacenter and the Standard edition are the two license version for Windows Server 2012R2 offered by Microsoft. There is likewise a free version called
Hyper-V Server which is an independent system that only contains the Windows hypervisor, a driver model as well as virtualization modules. Every window
version underpins Hyper-V, which is Microsoft's Type-1 hypervisor offering, likewise referred to as a bare-metal installation, and each Hyper-V server is known as a
Host (Portnoy, 2012). The Windows Server.
20810chapter Information Systems Sourcing .docxRAJU852744
208
10
chapter Information Systems
Sourcing
After 13 years, Kellwood, an American apparel maker, ended its soups!to!nuts IS outsourcing
arrangement with EDS . The primary focus of the original outsourcing contract was to integrate
12 individually acquired units with different systems into one system. Kellwood had been satis-
" ed enough with EDS ’ s performance to renegotiate the contract in 2002 and 2008, even though
at each renegotiation point, Kellwood had considered bringing the IS operations back in house,
or backsourcing. The 2008 contract iteration resulted in a more # exible $105 million contract that
EDS estimated would save Kellwood $2 million in the " rst year and $9 million over the remaining
contract years. But the situation at Kellwood had changed drastically. In 2008, Kellwood had been
purchased by Sun Capital Partners and taken private. The chief operating of" cer (COO), who was
facing a mountain of debt and possibly bankruptcy, wanted to consolidate and bring the operations
back in house to give some order to the current situation and reduce costs. Kellwood was suffering
from a lack of IS standardization as a result of its many acquisitions. The chief information of" cer
(CIO) recognized the importance of IS standardization and costs, but she was concerned that the
transition from outsourcing to insourcing would cause serious disruption to IS service levels and
project deadlines if it went poorly. Kellwood hired a third!party consultant to help it explore the
issues and decided that backsourcing would save money and respond to changes caused by both the
market and internal forces. Kellwood decided to backsource and started the process in late 2009. It
carefully planned for the transition, and the implementation went smoothly. By performing stream-
lined operations in house, it was able to report an impressive $3.6 million savings, or about 17% of
annual IS expenses after the " rst year. 1
The Kellwood case demonstrates a series of decisions made in relation to sourcing. Both the
decision to outsource IS operations and then to bring them back in house were based on a series of
This chapter is organized around decisions in the Sourcing Decision Cycle. The ! rst question
regarding information systems (IS) in the cycle relates to the decision to make (insource) or
buy (outsource) them. This chapter ’ s focus is on issues related to outsourcing whereas issues
related to insourcing are discussed in other chapters of this book. Discussed are the critical
decisions in the Sourcing Decision Cycle: how and where (cloud computing, onshoring,
offshoring). When the choice is offshoring, the next decision is where abroad (farshoring,
nearshoring, or captive centers). Explored next in this chapter is the ! nal decision in the
cycle, keep as is or change in which case the current arrangements are assessed and modi-
! cations are made to the outsourcing arrangem.
21720201Chapter 14Eating and WeightHealth Ps.docxRAJU852744
2/17/2020
1
Chapter 14
Eating and Weight
Health Psychology (PSYC 172)
Professor: Andrea Cook, PhD
February 18, 2020
The Digestive System
– Food nourishes the body by providing energy for
activity
– Digestion begins in the mouth
• Salivary glands provide moisture that allows food to
have taste
• Importance of good mastication
The Digestive System
The Digestive System
– Food is swallowed and then moves through the
pharynx and esophagus
– Peristalsis moves food through the digestive
system
– In the stomach, food is mixed with gastric juices
so it can be absorbed by the small intestine
– Most nutrients are digested in the small intestine
– Digestion process is complete when waste is
eliminated
The Digestive System, Continued
2/17/2020
2
Microbiome
4YouTube: What is the human microbiome?
Supporting the Gut Microbiome
Dysbiosis = unbalanced gut microbiome
• associated with weight gain, insulin resistance,
inflammation
Probiotics
• contain live microorganisms
• maintain or improve the "good" bacteria (normal microflora)
in the body
• e.g., fermented foods, yogurt, sauerkraut, kimchi
Prebiotics
• act as food for human microflora
• helps improve microflora balance
• e.g., whole grains, bananas, greens, onions, garlic
5
https://www.mayoclinic.org/healthy-lifestyle/consumer-health/expert-
answers/probiotics/faq-20058065
Supporting the Gut Microbiome
Medication overuse
• anti-inflammatories, antibiotics, acid blocking drugs, and
steroids damage gut or block normal digestive function
Stress
• chronic stress alters the normal bacteria in the gut
Lifestyle
• plenty of fiber, water, exercise and rest
Healthy Defecation
• three bowel movements a day to three each week
• no intestinal pain or bloating
• no straining
6
https://drhyman.com/blog/2014/10/10/tend-inner-garden-gut-flora-may-
making-sick/
2/17/2020
3
Bristol Stool Chart
7
Factors in Weight Maintenance
– Stable weight occurs when calories eaten equal those
expended for body metabolism and physical exercise
[OLD THINKING]
– Complicated interplay of nutrients, hormones, and
inflammation
• Metabolic rates differ from person to person
• Ghrelin, a hormone, stimulates appetite
• Leptin, a protein, signals satiation and fat storage
• Insulin, a hormone produced in pancreas
– unlocks cells for glucose use for energy
– cues hypothalamus for satiation and decreased appetite
Factors in Weight Maintenance
What is obesity?
– Overeating is not the sole cause of obesity
– Various methods to assess body fat
• Skin-fold technique
• Percentage body fat
• Body mass index (BMI)
– Can also be thought of in terms of social and
cultural standards
– ideal body = thinner in past 50 years
What is Obesity?
2/17/2020
4
BMI
10
– Obesity rates have increased, especially
“extreme” obesity
• past 30 years obesity rates have nearly doubled to
600 million
• 37.8% of US adults are obese and an additional 32.6%
are over.
2020/2/21 Critical Review #2 - WebCOM™ 2.0
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Santa Monica College Democracy and Di�erence Through the Aesthetics
of Film
Tahvildaran
Assignment Objectives: Enhance and/or improve critical thinking and
media literacy skills by:
1. Developing a clear and concise thesis statement (an
argument) in response to the
following question: Does the �lm have the power to
transform political sensibilities?
2. Writing an outline for a �ve paragraph analytical essay
building on a clear and
concise thesis statement, including topic sentences and
secondary supports.
3. Identifying and explaining three scenes from the �lm text in
support of the thesis
statement/argument.
4. Writing an introductory paragraph for the outlined analytical
essay
Be sure to read thoroughly the writing conventions below before beginning this
assignment.
Note: You are NOT writing a full essay; rather, you are outlining an analytical
essay by completing the dialogue in the boxes below.
Writing a Critical Review (analytical) Essay
2020/2/21 Critical Review #2 - WebCOM™ 2.0
https://smc.grtep.com/index.cfm/smcc/page/2criticalreviews 2/10
1. Every essay that you write for this course must have a clear thesis, placed
(perhaps) somewhere near the end of the introductory paragraph. Simply
stated, a THESIS (or ARGUMENT) expresses, preferably in a single sentence,
the point you want to make about the text that is the subject of your essay. A
THESIS should be an opinion or interpretation of the text, not merely a fact or
observation. The best possible THESIS will answer some speci�c questions
about the text. Very often the THESIS contains an outline of the major points
to be covered in the essay. A possible thesis for an essay on character in
Perry Henzell’s The Harder They Come might read somewhat as follows:
The protagonist of THTC is not a hero in the epic sense of the word, but a
self-centered young man bred of economic oppression and cultural
dependency. The characters in this �lm have no real psychological depth, but
are markers for a society of consumption and momentary glory.
(You might then go on to exemplify from the text and argue in favor or
against this interpretation: your essay need not hold to only one perspective.)
What single, clear QUESTION does the above THESIS attempt to answer?
2. Each essay should be organized into �ve (5) paragraphs, each based on one
of two to four major ideas, which will comprise the BODY of the essay. Each
paragraph must have a topic sentence, often (but not always) towards the
beginning of the paragraph, which clearly states the ARGUMENT or point to
be made in the paragraph. Following the thesis set forth.
2020422 Take Test Learning Assessment for Week Four – GENDE.docxRAJU852744
2020/4/22 Take Test: Learning Assessment for Week Four – GENDER ...
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Take Test: Learning Assessment for Week FourTake Test: Learning Assessment for Week Four
Test Information
Description
Instructions
Multiple Attempts Not allowed. This test can only be taken once.
Force Completion Once started, this test must be completed in one sitting. Do not leave the test before clicking Save and Submit.
To avoid issues with iLearn, do NOT click on this assessment until you are prepared to finish it in one continuous session.
Question Completion Status:
A.
B.
C.
D.
Q U E S T I O N 1
Gayle Rubin’s charmed circle, which we talked about in a lecture video, identifies
sexual practices that are privileged in our patriarchal society versus those
considered deviant.
relationship types that are privileged by society versus those considered deviant.
racial, class, and sexuality categories associated with privilege versus those that are
associated with oppression.
practices of masculinity and femininity that are privileged by society versus those
that are considered deviant.
1 points Save AnswerSave Answer
-
-
-
-
A.
B.
C.
D.
Q U E S T I O N 2
Please match the concept with a summary/definition of it
Theoretical, research, and activist
perspective focused on how race,
class, and gender are interconnected
and mutually constitutive systems of
social inequality that interact
differently in specific contexts
Theoretical perspective that severs sex
from gender from sexuality
Perspective on sexuality in which
intimate and sexual relations are
primary sites for women’s domination
and subordination in patriarchal
societies
Perspective on sexuality in which
sexual norms and practices express the
current needs of capital
Queer theory
Feminism
Intersectionality
Marxism
2 points Save AnswerSave Answer
A.
B.
Q U E S T I O N 3
According to AnnaLouise Keating, Gloria Anzaldúa negotiated many social statuses that
marked her as an outsider or as different and which subsequently informed her work on
mestiza consciousness. These social statuses included that Anzaldúa was lesbian,
Chicana, Tejana, a woman, and
did not speak Spanish.
i d l b t
1 points Save AnswerSave Answer
Click Save and Submit to save and submit. Click Save All Answers to save all answers.
2020/4/22 Take Test: Learning Assessment for Week Four – GENDER ...
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B.
C.
D.
experienced early puberty.
devoutly Catholic.
overweight.
A.
B.
C.
D.
Q U E S T I O N 4
In "Oppression," Marilyn Frye calls situations in which all of one's options expose one to penalty,
censure, or deprivation
double binds.
birdcages.
the glass ceiling.
stonewalling.
1 points Save AnswerSave Answer
Click .
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
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.
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.
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
Home assignment II on Spectroscopy 2024 Answers.pdf
222111Organization N.docx
1. 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
2. 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
3. Decision-Making Process in B2B E-Commerce Using
Analytic-MLP. Elsevier.
https://www.researchgate.net/publication/319278239_A_Cogniti
ve_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, 21(2), 143-154.
Retrieved June 13, 2020, from www.jstor.org/stable/27645745
https://www.researchgate.net/profile/Yi_Fang_Tsai2/publication
/284188657_Using_Text_Mining_of_Amazon_Reviews_to_Expl
ore_User-
Defined_Product_Highlights_and_Issues/links/564f69eb08aefe6
19b11de8b/Using-Text-Mining-of-Amazon-Reviews-to-Explore-
User-Defined-Product-Highlights-and-Issues.pdf
Research Paper Topic
Enterprise Risk Management
4. Introduction
All research reports begin with an introduction. (1 – 2 Pages)
Background
Provide your reader with a broad base of understanding of the
research topic. The goal is to give the reader an overview of the
topic, and its context within the real world, research literature,
and theory. (3 – 5 Pages)
Problem Statement
This section should clearly articulate how the study will relate
to the current literature. This is done by describing findings
from the research literature that define the gap. Should be very
clear what the research problem is and why it should be solved.
Provide a general/board problem and a specific problem (150 –
200 Words)
Literature Review
Using your annotated bibliography, construct a literature
5. review. (5-10 pages)
Discussion
Provide a discussion about your specific topic findings. Using
the literature, you found, how do you solve your problem? How
does it affect your general/board problem? (3-5 pages)
References
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
6. Microsoft and/or its respective suppliers make no
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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
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from loss of use, data or profits, whether in an
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Management: Andrew Gilfillan
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Library of Congress Cataloging-in-Publication Data
Library of Congress Cataloging in Publication Control Number:
2018051774
http://www.pearsoned.com/permissions
iii
Preface xxv
About the Authors xxxiv
PART I Introduction to Analytics and AI 1
Chapter 1 Overview of Business Intelligence, Analytics,
9. Data Science, and Artificial Intelligence: Systems
for Decision Support 2
Chapter 2 Artificial Intelligence: Concepts, Drivers, Major
Technologies, and Business Applications 73
Chapter 3 Nature of Data, Statistical Modeling, and
Visualization 117
PART II Predictive Analytics/Machine Learning 193
Chapter 4 Data Mining Process, Methods, and Algorithms 194
Chapter 5 Machine-Learning Techniques for Predictive
Analytics 251
Chapter 6 Deep Learning and Cognitive Computing 315
Chapter 7 Text Mining, Sentiment Analysis, and Social
Analytics 388
PART III Prescriptive Analytics and Big Data 459
Chapter 8 Prescriptive Analytics: Optimization and
Simulation 460
Chapter 9 Big Data, Cloud Computing, and Location Analytics:
Concepts and Tools 509
PART IV Robotics, Social Networks, AI and IoT 579
Chapter 10 Robotics: Industrial and Consumer Applications 580
Chapter 11 Group Decision Making, Collaborative Systems,
and
AI Support 610
Chapter 12 Knowledge Systems: Expert Systems,
10. Recommenders,
Chatbots, Virtual Personal Assistants, and Robo
Advisors 648
Chapter 13 The Internet of Things as a Platform for Intelligent
Applications 687
PART V Caveats of Analytics and AI 725
Chapter 14 Implementation Issues: From Ethics and Privacy to
Organizational and Societal Impacts 726
Glossary 770
Index 785
BRIEF CONTENTS
iv
CONTENTS
Preface xxv
About the Authors xxxiv
PART I Introduction to Analytics and AI 1
Chapter 1 Overview of Business Intelligence, Analytics, Data
Science, and Artificial Intelligence: Systems for Decision
Support 2
1.1 Opening Vignette: How Intelligent Systems Work for
KONE Elevators and Escalators Company 3
11. 1.2 Changing Business Environments and Evolving Needs for
Decision Support and Analytics 5
Decision-Making Process 6
The Influence of the External and Internal Environments on the
Process 6
Data and Its Analysis in Decision Making 7
Technologies for Data Analysis and Decision Support 7
1.3 Decision-Making Processes and Computerized Decision
Support Framework 9
Simon’s Process: Intelligence, Design, and Choice 9
The Intelligence Phase: Problem (or Opportunity) Identification
10
0 APPLICATION CASE 1.1 Making Elevators Go Faster! 11
The Design Phase 12
The Choice Phase 13
The Implementation Phase 13
The Classical Decision Support System Framework 14
A DSS Application 16
Components of a Decision Support System 18
The Data Management Subsystem 18
12. The Model Management Subsystem 19
0 APPLICATION CASE 1.2 SNAP DSS Helps OneNet Make
Telecommunications Rate Decisions 20
The User Interface Subsystem 20
The Knowledge-Based Management Subsystem 21
1.4 Evolution of Computerized Decision Support to Business
Intelligence/Analytics/Data Science 22
A Framework for Business Intelligence 25
The Architecture of BI 25
The Origins and Drivers of BI 26
Data Warehouse as a Foundation for Business Intelligence 27
Transaction Processing versus Analytic Processing 27
A Multimedia Exercise in Business Intelligence 28
Contents v
1.5 Analytics Overview 30
Descriptive Analytics 32
0 APPLICATION CASE 1.3 Silvaris Increases Business with
Visual
Analysis and Real-Time Reporting Capabilities 32
0 APPLICATION CASE 1.4 Siemens Reduces Cost with the
13. Use of Data
Visualization 33
Predictive Analytics 33
0 APPLICATION CASE 1.5 Analyzing Athletic Injuries 34
Prescriptive Analytics 34
0 APPLICATION CASE 1.6 A Specialty Steel Bar Company
Uses Analytics
to Determine Available-to-Promise Dates 35
1.6 Analytics Examples in Selected Domains 38
Sports Analytics—An Exciting Frontier for Learning and
Understanding
Applications of Analytics 38
Analytics Applications in Healthcare—Humana Examples 43
0 APPLICATION CASE 1.7 Image Analysis Helps Estimate
Plant Cover 50
1.7 Artificial Intelligence Overview 52
What Is Artificial Intelligence? 52
The Major Benefits of AI 52
The Landscape of AI 52
0 APPLICATION CASE 1.8 AI Increases Passengers’ Comfort
and
Security in Airports and Borders 54
The Three Flavors of AI Decisions 55
14. Autonomous AI 55
Societal Impacts 56
0 APPLICATION CASE 1.9 Robots Took the Job of Camel-
Racing Jockeys
for Societal Benefits 58
1.8 Convergence of Analytics and AI 59
Major Differences between Analytics and AI 59
Why Combine Intelligent Systems? 60
How Convergence Can Help? 60
Big Data Is Empowering AI Technologies 60
The Convergence of AI and the IoT 61
The Convergence with Blockchain and Other Technologies 62
0 APPLICATION CASE 1.10 Amazon Go Is Open for Business
62
IBM and Microsoft Support for Intelligent Systems
Convergence 63
1.9 Overview of the Analytics Ecosystem 63
1.10 Plan of the Book 65
1.11 Resources, Links, and the Teradata University Network
Connection 66
Resources and Links 66
15. Vendors, Products, and Demos 66
Periodicals 67
The Teradata University Network Connection 67
vi Contents
The Book’s Web Site 67
Chapter Highlights 67 • Key Terms 68
Questions for Discussion 68 • Exercises 69
References 70
Chapter 2 Artificial Intelligence: Concepts, Drivers, Major
Technologies, and Business Applications 73
2.1 Opening Vignette: INRIX Solves Transportation
Problems 74
2.2 Introduction to Artificial Intelligence 76
Definitions 76
Major Characteristics of AI Machines 77
Major Elements of AI 77
AI Applications 78
Major Goals of AI 78
16. Drivers of AI 79
Benefits of AI 79
Some Limitations of AI Machines 81
Three Flavors of AI Decisions 81
Artificial Brain 82
2.3 Human and Computer Intelligence 83
What Is Intelligence? 83
How Intelligent Is AI? 84
Measuring AI 85
0 APPLICATION CASE 2.1 How Smart Can a Vacuum Cleaner
Be? 86
2.4 Major AI Technologies and Some Derivatives 87
Intelligent Agents 87
Machine Learning 88
0 APPLICATION CASE 2.2 How Machine Learning Is
Improving Work
in Business 89
Machine and Computer Vision 90
Robotic Systems 91
Natural Language Processing 92
17. Knowledge and Expert Systems and Recommenders 93
Chatbots 94
Emerging AI Technologies 94
2.5 AI Support for Decision Making 95
Some Issues and Factors in Using AI in Decision Making 96
AI Support of the Decision-Making Process 96
Automated Decision Making 97
0 APPLICATION CASE 2.3 How Companies Solve Real-
World Problems
Using Google’s Machine-Learning Tools 97
Conclusion 98
Contents vii
2.6 AI Applications in Accounting 99
AI in Accounting: An Overview 99
AI in Big Accounting Companies 100
Accounting Applications in Small Firms 100
0 APPLICATION CASE 2.4 How EY, Deloitte, and PwC Are
Using AI 100
Job of Accountants 101
18. 2.7 AI Applications in Financial Services 101
AI Activities in Financial Services 101
AI in Banking: An Overview 101
Illustrative AI Applications in Banking 102
Insurance Services 103
0 APPLICATION CASE 2.5 US Bank Customer Recognition
and
Services 104
2.8 AI in Human Resource Management (HRM) 105
AI in HRM: An Overview 105
AI in Onboarding 105
0 APPLICATION CASE 2.6 How Alexander Mann
Solution
s (AMS) Is
Using AI to Support the Recruiting Process 106
Introducing AI to HRM Operations 106
2.9 AI in Marketing, Advertising, and CRM 107
19. Overview of Major Applications 107
AI Marketing Assistants in Action 108
Customer Experiences and CRM 108
0 APPLICATION CASE 2.7 Kraft Foods Uses AI for
Marketing
and CRM 109
Other Uses of AI in Marketing 110
2.10 AI Applications in Production-Operation
Management (POM) 110
AI in Manufacturing 110
Implementation Model 111
Intelligent Factories 111
Logistics and Transportation 112
Chapter Highlights 112 • Key Terms 113
20. Questions for Discussion 113 • Exercises 114
References 114
Chapter 3 Nature of Data, Statistical Modeling, and
Visualization 117
3.1 Opening Vignette: SiriusXM Attracts and Engages a
New Generation of Radio Consumers with Data-Driven
Marketing 118
3.2 Nature of Data 121
3.3 Simple Taxonomy of Data 125
0 APPLICATION CASE 3.1 Verizon Answers the Call for
Innovation: The
Nation’s Largest Network Provider uses Advanced Analytics to
Bring
the Future to its Customers 127
viii Contents
21. 3.4 Art and Science of Data Preprocessing 129
0 APPLICATION CASE 3.2 Improving Student Retention with
Data-Driven Analytics 133
3.5 Statistical Modeling for Business Analytics 139
Descriptive Statistics for Descriptive Analytics 140
Measures of Centrality Tendency (Also Called Measures of
Location or
Centrality) 140
Arithmetic Mean 140
Median 141
Mode 141
Measures of Dispersion (Also Called Measures of Spread or
Decentrality) 142
Range 142
Variance 142
22. Standard Deviation 143
Mean Absolute Deviation 143
Quartiles and Interquartile Range 143
Box-and-Whiskers Plot 143
Shape of a Distribution 145
0 APPLICATION CASE 3.3 Town of Cary Uses Analytics to
Analyze Data
from Sensors, Assess Demand, and Detect Problems 150
3.6 Regression Modeling for Inferential Statistics 151
How Do We Develop the Linear Regression Model? 152
How Do We Know If the Model Is Good Enough? 153
What Are the Most Important Assumptions in Linear
Regression? 154
Logistic Regression 155
23. Time-Series Forecasting 156
0 APPLICATION CASE 3.4 Predicting NCAA Bowl Game
Outcomes 157
3.7 Business Reporting 163
0 APPLICATION CASE 3.5 Flood of Paper Ends at FEMA 165
3.8 Data Visualization 166
Brief History of Data Visualization 167
0 APPLICATION CASE 3.6 Macfarlan Smith Improves
Operational
Performance Insight with Tableau Online 169
3.9 Different Types of Charts and Graphs 171
Basic Charts and Graphs 171
Specialized Charts and Graphs 172
Which Chart or Graph Should You Use? 174
3.10 Emergence of Visual Analytics 176
24. Visual Analytics 178
High-Powered Visual Analytics Environments 180
3.11 Information Dashboards 182
Contents ix
0 APPLICATION CASE 3.7 Dallas Cowboys Score Big with
Tableau
and Teknion 184
Dashboard Design 184
0 APPLICATION CASE 3.8 Visual Analytics Helps Energy
Supplier Make
Better Connections 185
What to Look for in a Dashboard 186
Best Practices in Dashboard Design 187
25. Benchmark Key Performance Indicators with Industry Standards
187
Wrap the Dashboard Metrics with Contextual Metadata 187
Validate the Dashboard Design by a Usability Specialist 187
Prioritize and Rank Alerts/Exceptions Streamed to the
Dashboard 188
Enrich the Dashboard with Business-User Comments 188
Present Information in Three Different Levels 188
Pick the Right Visual Construct Using Dashboard Design
Principles 188
Provide for Guided Analytics 188
Chapter Highlights 188 • Key Terms 189
Questions for Discussion 190 • Exercises 190
References 192
PART II Predictive Analytics/Machine Learning 193
26. Chapter 4 Data Mining Process, Methods, and Algorithms 194
4.1 Opening Vignette: Miami-Dade Police Department Is
Using
Predictive Analytics to Foresee and Fight Crime 195
4.2 Data Mining Concepts 198
0 APPLICATION CASE 4.1 Visa Is Enhancing the Customer
Experience while Reducing Fraud with Predictive Analytics
and Data Mining 199
Definitions, Characteristics, and Benefits 201
How Data Mining Works 202
0 APPLICATION CASE 4.2 American Honda Uses Advanced
Analytics to
Improve Warranty Claims 203
Data Mining Versus Statistics 208
4.3 Data Mining Applications 208
0 APPLICATION CASE 4.3 Predictive Analytic and Data
27. Mining Help
Stop Terrorist Funding 210
4.4 Data Mining Process 211
Step 1: Business Understanding 212
Step 2: Data Understanding 212
Step 3: Data Preparation 213
Step 4: Model Building 214
0 APPLICATION CASE 4.4 Data Mining Helps in
Cancer Research 214
Step 5: Testing and Evaluation 217
x Contents
Step 6: Deployment 217
Other Data Mining Standardized Processes and Methodologies
28. 217
4.5 Data Mining Methods 220
Classification 220
Estimating the True Accuracy of Classification Models 221
Estimating the Relative Importance of Predictor Variables 224
Cluster Analysis for Data Mining 228
0 APPLICATION CASE 4.5 Influence Health Uses Advanced
Predictive
Analytics to Focus on the Factors That Really Influence
People’s
Healthcare Decisions 229
Association Rule Mining 232
4.6 Data Mining Software Tools 236
0 APPLICATION CASE 4.6 Data Mining goes to Hollywood:
Predicting
Financial Success of Movies 239
29. 4.7 Data Mining Privacy Issues, Myths, and Blunders 242
0 APPLICATION CASE 4.7 Predicting Customer Buying
Patterns—The
Target Story 243
Data Mining Myths and Blunders 244
Chapter Highlights 246 • Key Terms 247
Questions for Discussion 247 • Exercises 248
References 250
Chapter 5 Machine-Learning Techniques for Predictive
Analytics 251
5.1 Opening Vignette: Predictive Modeling Helps
Better Understand and Manage Complex Medical
Procedures 252
5.2 Basic Concepts of Neural Networks 255
Biological versus Artificial Neural Networks 256
0 APPLICATION CASE 5.1 Neural Networks are Helping to
30. Save
Lives in the Mining Industry 258
5.3 Neural Network Architectures 259
Kohonen’s Self-Organizing Feature Maps 259
Hopfield Networks 260
0 APPLICATION CASE 5.2 Predictive Modeling Is Powering
the Power
Generators 261
5.4 Support Vector Machines 263
0 APPLICATION CASE 5.3 Identifying Injury Severity Risk
Factors in
Vehicle Crashes with Predictive Analytics 264
Mathematical Formulation of SVM 269
Primal Form 269
Dual Form 269
31. Soft Margin 270
Nonlinear Classification 270
Kernel Trick 271
Contents xi
5.5 Process-Based Approach to the Use of SVM 271
Support Vector Machines versus Artificial Neural Networks 273
5.6 Nearest Neighbor Method for Prediction 274
Similarity Measure: The Distance Metric 275
Parameter Selection 275
0 APPLICATION CASE 5.4 Efficient Image Recognition and
Categorization with knn 277
5.7 Naïve Bayes Method for Classification 278
32. Bayes Theorem 279
Naïve Bayes Classifier 279
Process of Developing a Naïve Bayes Classifier 280
Testing Phase 281
0 APPLICATION CASE 5.5 Predicting Disease Progress in
Crohn’s
Disease Patients: A Comparison of Analytics Methods 282
5.8 Bayesian Networks 287
How Does BN Work? 287
How Can BN Be Constructed? 288
5.9 Ensemble Modeling 293
Motivation—Why Do We Need to Use Ensembles? 293
Different Types of Ensembles 295
33. Bagging 296
Boosting 298
Variants of Bagging and Boosting 299
Stacking 300
Information Fusion 300
Summary—Ensembles are not Perfect! 301
0 APPLICATION CASE 5.6 To Imprison or Not to Imprison:
A Predictive Analytics-Based Decision Support System for
Drug Courts 304
Chapter Highlights 306 • Key Terms 308
Questions for Discussion 308 • Exercises 309
Internet Exercises 312 • References 313
Chapter 6 Deep Learning and Cognitive Computing 315
6.1 Opening Vignette: Fighting Fraud with Deep Learning
34. and Artificial Intelligence 316
6.2 Introduction to Deep Learning 320
0 APPLICATION CASE 6.1 Finding the Next Football Star
with
Artificial Intelligence 323
6.3 Basics of “Shallow” Neural Networks 325
0 APPLICATION CASE 6.2 Gaming Companies Use Data
Analytics to
Score Points with Players 328
0 APPLICATION CASE 6.3 Artificial Intelligence Helps
Protect Animals
from Extinction 333
xii Contents
6.4 Process of Developing Neural Network–Based
Systems 334
35. Learning Process in ANN 335
Backpropagation for ANN Training 336
6.5 Illuminating the Black Box of ANN 340
0 APPLICATION CASE 6.4 Sensitivity Analysis Reveals
Injury Severity
Factors in Traffic Accidents 341
6.6 Deep Neural Networks 343
Feedforward Multilayer Perceptron (MLP)-Type Deep Networks
343
Impact of Random Weights in Deep MLP 344
More Hidden Layers versus More Neurons? 345
0 APPLICATION CASE 6.5 Georgia DOT Variable Speed
Limit Analytics
Help Solve Traffic Congestions 346
6.7 Convolutional Neural Networks 349
36. Convolution Function 349
Pooling 352
Image Processing Using Convolutional Networks 353
0 APPLICATION CASE 6.6 From Image Recognition to Face
Recognition 356
Text Processing Using Convolutional Networks 357
6.8 Recurrent Networks and Long Short-Term Memory
Networks 360
0 APPLICATION CASE 6.7 Deliver Innovation by
Understanding
Customer Sentiments 363
LSTM Networks Applications 365
6.9 Computer Frameworks for Implementation of Deep
Learning 368
Torch 368
37. Caffe 368
TensorFlow 369
Theano 369
Keras: An Application Programming Interface 370
6.10 Cognitive Computing 370
How Does Cognitive Computing Work? 371
How Does Cognitive Computing Differ from AI? 372
Cognitive Search 374
IBM Watson: Analytics at Its Best 375
0 APPLICATION CASE 6.8 IBM Watson Competes against
the
Best at Jeopardy! 376
How Does Watson Do It? 377
What Is the Future for Watson? 377
38. Chapter Highlights 381 • Key Terms 383
Questions for Discussion 383 • Exercises 384
References 385
Contents xiii
Chapter 7 Text Mining, Sentiment Analysis, and Social
Analytics 388
7.1 Opening Vignette: Amadori Group Converts Consumer
Sentiments into Near-Real-Time Sales 389
7.2 Text Analytics and Text Mining Overview 392
0 APPLICATION CASE 7.1 Netflix: Using Big Data to Drive
Big
Engagement: Unlocking the Power of Analytics to Drive
Content and Consumer Insight 395
7.3 Natural Language Processing (NLP) 397
0 APPLICATION CASE 7.2 AMC Networks Is Using
39. Analytics to
Capture New Viewers, Predict Ratings, and Add Value for
Advertisers
in a Multichannel World 399
7.4 Text Mining Applications 402
Marketing Applications 403
Security Applications 403
Biomedical Applications 404
0 APPLICATION CASE 7.3 Mining for Lies 404
Academic Applications 407
0 APPLICATION CASE 7.4 The Magic Behind the Magic:
Instant Access
to Information Helps the Orlando Magic Up their Game and the
Fan’s
Experience 408
7.5 Text Mining Process 410
40. Task 1: Establish the Corpus 410
Task 2: Create the Term–Document Matrix 411
Task 3: Extract the Knowledge 413
0 APPLICATION CASE 7.5 Research Literature Survey with
Text
Mining 415
7.6 Sentiment Analysis 418
0 APPLICATION CASE 7.6 Creating a Unique Digital
Experience to
Capture Moments That Matter at Wimbledon 419
Sentiment Analysis Applications 422
Sentiment Analysis Process 424
Methods for Polarity Identification 426
Using a Lexicon 426
Using a Collection of Training Documents 427
41. Identifying Semantic Orientation of Sentences and Phrases 428
Identifying Semantic Orientation of Documents 428
7.7 Web Mining Overview 429
Web Content and Web Structure Mining 431
7.8 Search Engines 433
Anatomy of a Search Engine 434
1. Development Cycle 434
2. Response Cycle 435
Search Engine Optimization 436
Methods for Search Engine Optimization 437
xiv Contents
42. 0 APPLICATION CASE 7.7 Delivering Individualized Content
and
Driving Digital Engagement: How Barbour Collected More
Than 49,000
New Leads in One Month with Teradata Interactive 439
7.9 Web Usage Mining (Web Analytics) 441
Web Analytics Technologies 441
Web Analytics Metrics 442
Web Site Usability 442
Traffic Sources 443
Visitor Profiles 444
Conversion Statistics 444
7.10 Social Analytics 446
Social Network Analysis 446
Social Network Analysis Metrics 447
43. 0 APPLICATION CASE 7.8 Tito’s Vodka Establishes Brand
Loyalty with
an Authentic Social Strategy 447
Connections 450
Distributions 450
Segmentation 451
Social Media Analytics 451
How Do People Use Social Media? 452
Measuring the Social Media Impact 453
Best Practices in Social Media Analytics 453
Chapter Highlights 455 • Key Terms 456
Questions for Discussion 456 • Exercises 456
References 457
PART III Prescriptive Analytics and Big Data 459
44. Chapter 8 Prescriptive Analytics: Optimization and Simulation
460
8.1 Opening Vignette: School District of Philadelphia Uses
Prescriptive Analytics to Find Optimal