Federal Data Science serves as a guide for federal software engineers, government analysts, economists, researchers, data scientists, and engineering managers in deploying data analytics methods to governmental processes. Driven by open government (2009) and big data (2012) initiatives, federal agencies have a serious need to implement intelligent data management methods, share their data, and deploy advanced analytics to their processes. Using federal data for reactive decision making is not sufficient anymore, intelligent data systems allow for proactive activities that lead to benefits such as: improved citizen services, higher accountability, reduced delivery inefficiencies, lower costs, enhanced national insights, and better policy making.
No other government-dedicated work has been found in literature that addresses this broad topic. This book provides multiple use-cases, describes federal data science benefits, and fills the gap in this critical and timely area. Written and reviewed by academics, industry experts, and federal analysts, the problems and challenges of developing data systems for government agencies is presented by actual developers, designers, and users of those systems, providing a unique and valuable real-world perspective.
Author(s): Feras A. Batarseh, Ruixin Yang
Publisher: Academic Press
Year: 2017
Language: English
Pages: 256
City: London
Front-Matter_2018_Federal-Data-Science
Copyright_2018_Federal-Data-Science
Copyright
Dedication_2018_Federal-Data-Science
Dedication
List-of-Contributors_2018_Federal-Data-Science
List of Contributors
About-the-Editors_2018_Federal-Data-Science
About the Editors
Note-From-the-Editors_2018_Federal-Data-Science
Note From the Editors
Foreword_2018_Federal-Data-Science
Foreword
Preface_2018_Federal-Data-Science
Preface
Chapter-1---A-Day-in-the-Life-of-a-Federal-Analyst-and-a_2018_Federal-Data-S
1 - A Day in the Life of a Federal Analyst and a Federal Contractor
1. In the Early Morning
2. Later in the Afternoon
3. Late, Late at Night
4. Therefore, This Book
References
Chapter-2---Disseminating-Government-Data-Effectively-in_2018_Federal-Data-S
2 - Disseminating Government Data Effectively in the Age of Open Data
1. Data Dissemination in the Federal Government: From Colonial America to Open Data
2. Open Data Policy: A New Era in Data Dissemination
3. New Era, New Challenges
4. Toward a Modern and Effective Data Dissemination Strategy
5. Conclusion
Disclaimer
References
Chapter-3---Machine-Learning-for-the-Government--Challenge_2018_Federal-Data
3 - Machine Learning for the Government: Challenges and Statistical Difficulties
1. Introduction
2. An Introduction to Data Mining
2.1 Learning With Orange
3. Result Validation, Trust but Verify
3.1 Iris Aside
4. Model Overfitting, Too Good to Be True
5. Statistical Bias, Impacting Results Before Analysis Begins
6. Segmentation and Simpson’s Paradox
7. Outliers and Bad Data
8. Nonreproducibility and Statistics Hunting
9. Conclusion
References
Chapter-4---Making-the-Case-for-Artificial-Intelligence-at-Go_2018_Federal-D
4 - Making the Case for Artificial Intelligence at Government: Guidelines to Transforming Federal Software Systems
1. Motivations and Objections
2. AI Technologies and Government Use Cases
2.1 Knowledge-Based Systems
2.2 Big Data
2.3 Machine Learning and Data Analytics
3. Conclusions
References
Chapter-5---Agricultural-Data-Analytics-for-Environmenta_2018_Federal-Data-S
5 - Agricultural Data Analytics for Environmental Monitoring in Canada
1. Introduction
2. Materials and Methods
2.1 Input Data
2.2 Input Preparation
2.3 Input Accuracy Assessment
2.4 Coregistration
2.5 Rule Development
2.6 Contextual Assessment and Rectification
2.7 Assessment of Class Distributions
2.8 Output Accuracy Assessment
3. Results and Discussion
3.1 Accuracy Assessment
3.2 National-Scale Maps
3.3 Land Use Change
4. Conclusions
Acknowledgments
References
Chapter-6---France-s-Governmental-Big-Data-Analytics--From_2018_Federal-Data
6 - France’s Governmental Big Data Analytics: From Predictive to Prescriptive Using R
1. Introduction
2. Materials and Methods: “Parsimonious Modeling for Prescriptive Data Science,” Applied to French Agriculture
2.1 Agricultural Data in France
2.2 Open Taxonomies for Agricultural Data Sciences
2.3 Big Data and Data Science in France
3. Results
3.1 A Parsimonious Agricultural Taxonomy for Data Collection, an Intermediate Result
3.2 Agricultural Descriptive and Predictive Data Science With R
3.2.1 Crop Monitoring
3.2.2 Evapotranspiration
3.2.3 Prediction of Soil Fertility
3.3 From Descriptive Analytics to Prescriptive Analytics Through Predictive Analytics
3.3.1 Toward Prescriptive Analytics
3.3.2 Three Main Kinds of Aggregation Engine
3.3.2.1 Ensemble Methods
3.3.2.2 Optimization of a Utility Function
3.3.2.3 Heuristic and Fuzzy
3.3.3 Available R Building Blocks for Building an Aggregation Engine (Nonexhaustive Mapping)
4. Conclusion
References
Chapter-7---Agricultural-Remote-Sensing-and-Data-Scien_2018_Federal-Data-Sci
7 - Agricultural Remote Sensing and Data Science in China
1. Agricultural Remote Sensing in China
1.1 Agricultural Remote Sensing Research and Applications
1.2 China Agricultural Remote Sensing Monitoring System
2. Data Science in China
2.1 Data Science Development in China
2.2 Science Data Sharing and Services Platforms
3. Conclusions
Acknowledgments
References
Chapter-8---Data-Visualization-of-Complex-Information-Throu_2018_Federal-Dat
8 - Data Visualization of Complex Information Through Mind Mapping in Spain and the European Union
1. Data Science Ecosystem in the European Union
1.1 Horizon 2020 (Horizon 2020 Documents, 2016)
1.2 The European Data Landscape Monitoring Tool (European Data Landscape Monitoring Tool Documents, 2017)
1.3 Open Data Incubator Europe
1.4 Data Science Education in the European Union
1.4.1 European Data Science Academy
1.4.2 Educational Curriculum for the Usage of Linked Data (EUCLID Documents, 2017)
1.4.3 Master Course in Data Mining and Knowledge Management
1.4.4 European Institute of Innovation & Technology (EIT Documents, 2017) Digital Master School. Data Science Master (EIT Data Sc...
1.4.5 Erasmus Mundus Joint Master Degree Program in Big Data Management and Analytics (BDMA, 2017)
1.5 Other Organizations
1.5.1 Open Data Institute
1.5.2 European Data Forum (EDF Documents, 2016)
1.5.3 Big Data Value Association (BDV Documents, 2017)
1.6 Data Science and Big Data in Spain
2. Open Data in the European Union and Spain
2.1 Open Data in the European Union
2.2 Open Data in Spain
3. Visualization of Big Data and Open Data
4. Mind Mapping
4.1 Introduction
4.2 Digital Mind Maps
4.3 The Importance of Mind Mapping
4.4 Advantages of Mind Mapping
4.5 Experiments and Surveys Related to Mind Mapping
4.6 Use of Mind Mapping in Governments
4.7 Mind Mapping Automation
4.7.1 Statistical Analysis With R (Infoseg Documents, 2013a,b)
4.7.2 Big Open Data Analysis Using R (Infoseg, 2014a,b)
4.7.3 Open Data (Infoseg, 2013c,d)
4.7.4 Complex Information Systems
4.7.5 Analysis of Information Security Log Files
4.7.6 Mind Mapping Integration in Artificial Intelligence Systems
5. Uses of Mind Mapping in the Federal Government
6. Conclusions
References
Chapter-9---A-Deployment-Life-Cycle-Model-for-Agricultural-D_2018_Federal-Da
9 - A Deployment Life Cycle Model for Agricultural Data Systems Using Kansei Engineering and Association Rules
1. Introduction and Background
1.1 A Measuring Stick
1.2 Motivation
1.3 Systems Life Cycle Models
1.4 Analytical Models for the Government
2. Related Work
2.1 Intelligent Software Testing
2.2 Kansei and Software Deployment (A Review)
3. The Federal Deployment and Adoption Life Cycle
3.1 Association Rules Testing
3.2 Kansei Engineering Deployment and Traceability
4. Experimental Studies on Kansei Engineering Deployment and Traceability and Association Rules Testing
4.1 Code Coverage and Maintenance Costs of Association Rules Testing
4.2 The Agricultural Analyst’s Kansei Survey
5. Conclusions and Future Work
References
Further Reading
Chapter-10---Federal-Big-Data-Analytics-in-the-Health-Domain_2018_Federal-Da
10 - Federal Big Data Analytics in the Health Domain: An Ontological Approach to Data Interoperability
1. Introduction
2. Data Interoperability in the Health Domain
3. Ontologies as the Basis for Interoperability
3.1 Lexicon as the Basis for Semantic Congruity
3.2 Ontological Dimensions
3.3 Ontology Development
3.4 Ontology Integration
3.5 Ontology Operationalization
3.6 Metadata Foundations
4. Conclusion
References
Chapter-11---Geospatial-Data-Discovery--Management--and-Anal_2018_Federal-Da
11 - Geospatial Data Discovery, Management, and Analysis at National Aeronautics and Space Administration (NASA)
1. Introduction
2. Geospatial Data Discovery
3. Big Geospatial Data Management
4. Large-Scale Scientific Simulation
4.1 Spatiotemporal Thinking to Optimize High-Performance Computing
4.2 Cloud Computing to Support Large-Scale Scientific Simulation
5. Spatiotemporal Data Modeling and Analysis
5.1 Spatiotemporal Data Model
5.2 Tracking Changes and Interactions
5.3 Spatiotemporal Analytics
6. Conclusion and Future Directions
Acknowledgments
References
Chapter-12---Intelligent-Automation-Tools-and-Software-Engi_2018_Federal-Dat
12 - Intelligent Automation Tools and Software Engines for Managing Federal Agricultural Data
1. Introduction and Motivation
2. Related Work
2.1 Data Validation Methods
2.2 Data Security and Integrity Methods
3. The Intelligent Federal Math Engine
3.1 Inputs, Outputs, and Process of the Math Engine
3.2 The Seven-Step Math Process
4. Validation and Verification of Federal Agricultural Data
5. The Intelligent Federal Data Management Tool
5.1 Federal Tool Requirements
5.2 Federal Tool Implementation
6. Insights, Experimental Work, and Conclusions
6.1 Experimental Setup and Results
6.2 Lessons Learnt and Keys to Technical Federal Success
References
Further Reading
Chapter-13---Transforming-Governmental-Data-Science-Tea_2018_Federal-Data-Sc
13 - Transforming Governmental Data Science Teams in the Future
1. Introduction
2. Situational Leadership
3. Archetypes
3.1 Mapping Archetypes to Prior Career Paths
3.2 Archetypes in the Federal Government: An Example
3.3 Archetypes, Risk Mitigation, and Growth
4. Best Practices
4.1 Creation of a Best Practice
4.2 Intra- and Interagency Collaboration
5. Conclusion
Acknowledgment
References
Afterword_2018_Federal-Data-Science
Afterword
Index_2018_Federal-Data-Science
Index
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