Big Data and Analytics Applications in Government: Current Practices and Future Opportunities (Data Analytics Applications)

✍ Scribed by Gregory Richards (editor)

• Publisher: Auerbach Publications
• Year: 2017
• Tongue: English
• Leaves: 267
• Series: Data Analytics Applications
• Edition: 1
• Category: Library

✦ Synopsis

Within this context, big data analytics (BDA) can be an important tool given that many analytic techniques within the big data world have been created specifically to deal with complexity and rapidly changing conditions. The important task for public sector organizations is to liberate analytics from narrow scientific silos and expand it across internally to reap maximum benefit across their portfolios of programs.

This book highlights contextual factors important to better situating the use of BDA within government organizations and demonstrates the wide range of applications of different BDA techniques. It emphasizes the importance of leadership and organizational practices that can improve performance. It explains that BDA initiatives should not be bolted on but should be integrated into the organization’s performance management processes. Equally important, the book includes chapters that demonstrate the diversity of factors that need to be managed to launch and sustain BDA initiatives in public sector organizations.

✦ Table of Contents

Cover
Half Title
Series Page
Title Page
Copyright Page
Dedication
Table of Contents
Foreword
Preface
List of Contributors
Editor
Chapter 1 Unraveling Data Science, Artificial Intelligence, and Autonomy
1.1 The Beginnings of Data Science
1.2 The Beginnings of Artificial Intelligence
1.3 The Beginnings of Autonomy
1.4 The Convergence of Data Availability and Computing
1.5 Machine Learning the Common Bond
1.5.1 Supervised Learning
1.5.2 Unsupervised Learning
1.5.3 Reinforcement Learning
1.6 Data Science Today
1.7 Artificial Intelligence Today
1.8 Autonomy Today
1.9 Summary
References
Chapter 2 Unlock the True Power of Data Analytics with Artificial Intelligence
2.1 Introduction
2.2 Situation Overview
2.2.1 Data Age
2.2.2 Data Analytics
2.2.3 Marriage of Artificial Intelligence and Analytics
2.2.4 AI-Powered Analytics Examples
2.3 The Way Forward
2.4 Conclusion
References
Chapter 3 Machine Intelligence and Managerial Decision-Making
3.1 Managerial Decision-Making
3.1.1 What Is Decision-Making?
3.1.2 The Decision-Making Conundrum
3.1.3 The Decision-Making Process
3.1.4 Types of Decisions and Decision-Making Styles
3.1.5 Intuition and Reasoning in Decision-Making
3.1.6 Bounded Rationality
3.2 Human Intelligence
3.2.1 Defining What Makes Us Human
3.2.2 The Analytical Method
3.2.3 “Data-Driven” Decision-Making
3.3 Are Machines Intelligent?
3.4 Artificial Intelligence
3.4.1 What Is Machine Learning?
3.4.2 How Do Machines Learn?
3.4.3 Weak, General, and Super AI
3.4.3.1 Narrow AI
3.4.3.2 General AI
3.4.3.3 Super AI
3.4.4 The Limitations of AI
3.5 Matching Human and Machine Intelligence
3.5.1 Human Singularity
3.5.2 Implicit Bias
3.5.3 Managerial Responsibility
3.5.4 Semantic Drift
3.6 Conclusion
References
Chapter 4 Measurement Issues in the Uncanny Valley: The Interaction between Artificial Intelligence and Data Analytics
4.1 A Momentous Night in the Cold War
4.2 Cybersecurity
4.3 Measuring AI/ML Performance
4.4 Data Input to AI Systems
4.5 Defining Objectives
4.6 Ethics
4.7 Sharing Data—or Not
4.8 Developing an AI-Aware Culture
4.9 Conclusion
References
Chapter 5 An Overview of Deep Learning in Industry
5.1 Introduction
5.1.1 An Overview of Deep Learning
5.1.1.1 Deep Learning Architectures
5.1.2 Deep Generative Models
5.1.3 Deep Reinforcement Learning
5.2 Applications of Deep Learning
5.2.1 Recognition
5.2.1.1 Recognition in Text
5.2.1.2 Recognition in Audio
5.2.1.3 Recognition in Video and Images
5.2.2 Content Generation
5.2.2.1 Text Generation
5.2.2.2 Audio Generation
5.2.2.3 Image and Video Generation
5.2.3 Decision-Making
5.2.3.1 Autonomous Driving
5.2.3.2 Automatic Game Playing
5.2.3.3 Robotics
5.2.3.4 Energy Consumption
5.2.3.5 Online Advertising
5.2.4 Forecasting
5.2.4.1 Forecasting Physical Signals
5.2.4.2 Forecasting Financial Data
5.3 Conclusion
References
Chapter 6 Chinese AI Policy and the Path to Global Leadership: Competition, Protectionism, and Security
6.1 The Chinese Perspective on Innovation and AI
6.2 AI with Chinese Characteristics
6.3 National Security in AI
6.4 “Security” or “Protection”
6.5 A(Eye)
6.6 Conclusions
Bibliography
Chapter 7 Natural Language Processing in Data Analytics
7.1 Background and Introduction: Era of Big Data
7.1.1 Use Cases of Unstructured Data
7.1.2 The Challenge of Unstructured Data
7.1.3 Big Data and Artificial Intelligence
7.2 Data Analytics and AI
7.2.1 Data Analytics: Descriptive vs. Predictive vs. Prescriptive
7.2.2 Advanced Analytics toward Machine Learning and Artificial Intelligence
7.2.2.1 Machine Learning Approaches
7.3 Natural Language Processing in Data Analytics
7.3.1 Introduction to Natural Language Processing
7.3.2 Sentiment Analysis
7.3.3 Information Extraction
7.3.4 Other NLP Applications in Data Analytics
7.3.5 NLP Text Preprocessing
7.3.6 Basic NLP Text Enrichment Techniques
7.4 Summary
References
Chapter 8 AI in Smart Cities Development: A Perspective of Strategic Risk Management
8.1 Introduction
8.2 Concepts and Definitions
8.2.1 How Are AI, Smart Cities, and Strategic Risk Connected?
8.3 Methodology and Approach
8.4 Examples of Creating KPIs and KRIs Based on Open Data
8.4.1 Stakeholder Perspective
8.4.2 Financial Resources Management Perspective
8.4.3 Internal Process Perspective
8.4.4 Trained Public Servant Perspective
8.5 Discussion
8.6 Conclusion
References
Chapter 9 Predicting Patient Missed Appointments in the Academic Dental Clinic
9.1 Introduction
9.2 Electronic Dental Records and Analytics
9.3 Impact of Missed Dental Appointments
9.4 Patient Responses to Fear and Pain
9.4.1 Dental Anxiety
9.4.2 Dental Avoidance
9.5 Potential Data Sources
9.5.1 Dental Anxiety Assessments
9.5.2 Clinical Notes
9.5.3 Staff and Patient Reporting
9.6 Conclusions
References
Chapter 10 Machine Learning in Cognitive Neuroimaging
10.1 Introduction
10.1.1 Overview of AI, Machine Learning, and Deep Learning in Neuroimaging
10.1.2 Cognitive Neuroimaging
10.1.3 Functional Near-Infrared Spectroscopy
10.2 Machine Learning and Cognitive Neuroimaging
10.2.1 Challenges
10.3 Identifying Functional Biomarkers in Traumatic Brain Injury Patients Using fNIRS and Machine Learning
10.4 Finding the Correlation between Addiction Behavior in Gaming and Brain Activation Using fNIRS
10.5 Current Research on Machine Learning Applications in Neuroimaging
10.6 Summary
References
Chapter 11 People, Competencies, and Capabilities Are Core Elements in Digital Transformation: A Case Study of a Digital Transformation Project at ABB
11.1 Introduction
11.1.1 Objectives and Research Approach
11.1.2 Challenges Related to the Use of Digitalization and AI
11.2 Theoretical Framework
11.2.1 From Data Collection into Knowledge Management and Learning Agility
11.2.2 Knowledge Processes in Organizations
11.2.3 Framework for Competency, Capability, and Organizational Development
11.2.4 Management of Transient Advantages Is a Core Capability in Digital Solution Launch and Ramp-Up
11.3 Digital Transformation Needs an Integrated Model for Knowledge Management and Transformational Leadership
11.4 Case Study of the ABB Takeoff Program: Innovation, Talent, and Competence Development for Industry 4.0
11.4.1 Background for the Digital Transformation at ABB
11.4.2 The Value Framework for IIoT and Digital Solutions
11.4.3 Takeoff for Intelligent Industry: Innovation, Talent, and Competence Development for Industry 4.0
11.4.4 Case 1: ABB Smartsensor: An Intelligent Concept for Monitoring
11.4.5 Case 2: Digital Powertrain: Optimization of Industrial System Operations
11.4.6 Case 3: Autonomous Ships: Remote Diagnostics and Collaborative Operations for Ships
11.5 Conclusions and Future Recommendations
11.5.1 Conclusions
11.5.2 Future Recommendations
11.5.3 Critical Roles of People, Competency, and Capability Development
References
Chapter 12 AI-Informed Analytics Cycle: Reinforcing Concepts
12.1 Decision-Making
12.1.1 Data, Knowledge, and Information
12.1.2 Decision-Making and Problem-Solving
12.2 Artificial Intelligence
12.2.1 The Three Waves of AI
12.3 Analytics
12.3.1 Analytics Cycle
12.4 The Role of AI in Analytics
12.5 Applications in Scholarly Data
12.5.1 Query Refinement
12.5.2 Complex Task and AI Method
12.6 Concluding Remarks
References
Index