Federal data science: transforming government and agricultural policy using artificial intelligence
Saved in:
| Other Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
London
Academic Press
[2018]
|
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | xxv, 229 Seiten Illustrationen, Diagramme, Karten |
| ISBN: | 9780128124437 0128124431 |
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MARC
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| 245 | 1 | 0 | |a Federal data science |b transforming government and agricultural policy using artificial intelligence |c edited by Feras A. Batarseh, Ruixin Yang |
| 264 | 1 | |a London |b Academic Press |c [2018] | |
| 300 | |a xxv, 229 Seiten |b Illustrationen, Diagramme, Karten | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
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| 700 | 1 | |a Batarseh, Feras A. |4 edt | |
| 700 | 1 | |a Yang, Ruixin |4 edt | |
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Record in the Search Index
| _version_ | 1804177915140636672 |
|---|---|
| adam_text | Contents
List of Contributors xiii
About the Editors XV
Note From the Editors xvii
Foreword xix
Preface xxiii
Section 1
Injecting Artificial Intelligence Into
Governmental Systems
1. A Day in the Life of a Federal Analyst and a Federal
Contractor
Feras A Batarseh
1. In the Early Morning 3
2. Later in the Afternoon 5
3. Late, Late at Night 8
4. Therefore, This Book 10
References 12
2. Disseminating Government Data Effectively in the
Age of Open Data
Mirvat Sewadeh and Jeffrey Sisson
1. Data Dissemination in the Federal Government: From
Colonial America to Open Data 1 3
2. Open Data Policy: A New Era in Data Dissemination 1 4
3. New Era, New Challenges 1 6
4. Toward a Modern and Effective Data Dissemination
Strategy 23
5. Conclusion 27
Disclaimer 27
References 28
VII
viil Contents
3. Machine Learning for the Government: Challenges and
Statistical Difficulties
Samuel Eisenberg
1. Introduction 29
2. An Introduction to Data Mining 30
2.1 Learning With Orange 31
3. Result Validation, Trust but Verify 34
3.1 Iris Aside 37
4. Model Overfitting, Too Good to Be True 37
5. Statistical Bias, Impacting Results Before Analysis Begins 37
6. Segmentation and Simpson s Paradox 38
7. Outliers and Bad Data 38
8. Nonreproducibility and Statistics Hunting 39
9. Conclusion 39
References 40
4. Making the Case for Artificial Intelligence at
Government: Guidelines to Transforming Federal
Software Systems
Feras A. Batarseh and Ruixin Yang
1. Motivations and Objections 41
2. AI Technologies and Government Use Cases 44
2.1 Knowledge-Based Systems 44
2.2 Big Data 45
2.3 Machine Learning and Data Analytics 46
3. Conclusions 48
References 50
Section 2
Governmental Data Science Solutions
Around the World
5. Agricultural Data Analytics for Environmental Monitoring
in Canada
Ted Huffman, Morten Olesen, Melodie Green, Don Leckie,
Jiangui Liu and Jia/i Shang
Introduction 55
Materials and Methods 58
2.1 Input Data 58
2.2 Input Preparation 59
2.3 Input Accuracy Assessment 60
2.4 Co registration 60
Contents ix
2.5 Rule Development 60
2.6 Contextual Assessment and Rectification 65
2.7 Assessment of Class Distributions 68
2.8 Output Accuracy Assessment 69
3. Results and Discussion 69
3.1 Accuracy Assessment 69
3.2 National-Scale Maps 74
3.3 Land Use Change 76
4. Conclusions 77
Acknowledgments 78
References 78
6. France s Governmental Big Data Analytics: From
Predictive to Prescriptive Using R
Henri Laude
1. Introduction 81
2. Materials and Methods: Parsimonious Modeling for
Prescriptive Data Science/ Applied to French Agriculture 82
2.1 Agricultural Data in France 82
2.2 Open Taxonomies for Agricultural Data Sciences 83
2.3 Big Data and Data Science in France 84
3. Results 84
3.1 A Parsimonious Agricultural Taxonomy for Data Collection,
an Intermediate Result 84
3.2 Agricultural Descriptive and Predictive Data Science
With R 87
3.3 From Descriptive Analytics to Prescriptive Analytics
Through Predictive Analytics 88
4. Conclusion 91
References 92
7. Agricultural Remote Sensing and Data Science
in China
Zhongxin Chen, Haizhu Pan, Changan Liu and Zhiwei Jiang
1. Agricultural Remote Sensing in China 95
1.1 Agricultural Remote Sensing Research
and Applications 95
1.2 China Agricultural Remote Sensing Monitoring
System 99
2. Data Science in China 99
2.1 Data Science Development in China 99
2.2 Science Data Sharing and Services Platforms 103
3. Conclusions 105
Acknowledgments 106
References 106
x Contents
8. Data Visualization of Complex Information Through
Mind Mapping in Spain and the European Union
Jose M. Guerrero
1. Data Science Ecosystem in the European Union 109
1.1 Horizon 2020 109
1.2 The European Data Landscape Monitoring Tool 110
1.3 Open Data Incubator Europe 110
1.4 Data Science Education in the European Union 111
1.5 Other Organizations 114
1.6 Data Science and Big Data in Spain 115
2. Open Data in the European Union and Spain 11 6
2.1 Open Data in the European Union 116
2.2 Open Data in Spain 11 7
3. Visualization of Big Data and Open Data 117
4. Mind Mapping 11 8
4.1 Introduction 118
4.2 Digital Mind Maps 119
4.3 The Importance of Mind Mapping 120
4.4 Advantages of Mind Mapping 121
4.5 Experiments and Surveys Related to Mind
Mapping 122
4.6 Use of Mind Mapping in Governments 124
4.7 Mind Mapping Automation 125
5. Uses of Mind Mapping in the Federal Government 128
6. Conclusions 131
References 132
Section 3
Federal Data Science Use Cases at the
US Government
9. A Deployment Life Cycle Model for Agricultural
Data Systems Using Kansei Engineering
and Association Rules
Feras A. Batarseh and Ruixin Yang
1. Introduction and Background 141
1.1 A Measuring Stick 142
1.2 Motivation 142
1.3 Systems Life Cycle Models 143
1.4 Analytical Models for the Government 144
2. Related Work 146
2.1 Intelligent Software Testing 146
2.2 Kansei and Software Deployment (A Review) 150
Contents xi
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
151
151
152
154
155
156
157
158
159
10. Federal Big Data Analytics in the Health Domain:
An Ontological Approach to Data Interoperability
Erik W. Kuilerand Connie L. McNeely
1. Introduction 161
2. Data Interoperability in the Health Domain 162
3. Ontologies as the Basis for Interoperability 166
3.1 Lexicon as the Basis for Semantic Congruity 166
3.2 Ontological Dimensions 166
3.3 Ontology Development 168
3.4 Ontology Integration 171
3.5 Ontology Operationalization 172
3.6 Metadata Foundations 173
4. Conclusion 173
References 174
11. Geospatial Data Discovery, Management, and Analysis
at National Aeronautics and Space Administration (NASA)
Manzhu Yu and Min Sun
1. Introduction 177
2. Geospatial Data Discovery 1 78
3. Big Geospatial Data Management 1 80
4. Large-Scale Scientific Simulation 181
4.1 Spatiotemporal Thinking to Optimize High-Performance
Computing 182
4.2 Cloud Computing to Support Large-Scale Scientific
Simulation 184
5. Spatiotemporal Data Modeling and Analysis 184
5.1 Spatiotemporal Data Model 185
5.2 Tracking Changes and Interactions 185
5.3 Spatiotemporal Analytics 187
6. Conclusion and Future Directions 188
Acknowledgments 189
References 189
xii Contents
12. Intelligent Automation Tools and Software Engines
for Managing Federal Agricultural Data
Feras A. Batarseh, Gowtham Ramamoorthy, Manish Dash ora
and Ruixin Yang
1. Introduction and Motivation 193
2. Related Work 194
2A Data Validation Methods 194
2.2 Data Security and Integrity Methods 195
3. The Intelligent Federal Math Engine 196
3.1 Inputs, Outputs, and Process of the Math Engine 196
3.2 The Seven-Step Math Process 198
4. Validation and Verification of Federal Agricultural Data 1 99
5. The Intelligent Federal Data Management Tool 202
5.1 Federal Tool Requirements 202
5.2 Federal Tool Implementation 203
6. Insights, Experimental Work, and Conclusions 205
6.1 Experimental Setup and Results 205
6.2 Lessons Learnt and Keys to Technical Federal Success 207
References 209
Further Reading 210
13. Transforming Governmental Data Science Teams
in the Future
Jay Gendron, Steve Mortimer, Tammy Crane and
Candace Eshelman-Haynes
1. Introduction 211
2. Situational Leadership 212
3. Archetypes 215
3.1 Mapping Archetypes to Prior Career Paths 215
3.2 Archetypes in the Federal Government: An Example 216
3.3 Archetypes, Risk Mitigation, and Growth 217
4. Best Practices 218
4.1 Creation of a Best Practice 218
4.2 Intra- and Interagency Collaboration 219
5. Conclusion 220
Acknowledgment 221
References 221
Afterword 223
Index 225
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| spelling | Federal data science transforming government and agricultural policy using artificial intelligence edited by Feras A. Batarseh, Ruixin Yang London Academic Press [2018] xxv, 229 Seiten Illustrationen, Diagramme, Karten txt rdacontent n rdamedia nc rdacarrier Batarseh, Feras A. edt Yang, Ruixin edt Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029949833&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Federal data science transforming government and agricultural policy using artificial intelligence |
| title | Federal data science transforming government and agricultural policy using artificial intelligence |
| title_auth | Federal data science transforming government and agricultural policy using artificial intelligence |
| title_exact_search | Federal data science transforming government and agricultural policy using artificial intelligence |
| title_full | Federal data science transforming government and agricultural policy using artificial intelligence edited by Feras A. Batarseh, Ruixin Yang |
| title_fullStr | Federal data science transforming government and agricultural policy using artificial intelligence edited by Feras A. Batarseh, Ruixin Yang |
| title_full_unstemmed | Federal data science transforming government and agricultural policy using artificial intelligence edited by Feras A. Batarseh, Ruixin Yang |
| title_short | Federal data science |
| title_sort | federal data science transforming government and agricultural policy using artificial intelligence |
| title_sub | transforming government and agricultural policy using artificial intelligence |
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