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Data Science Thinking : The Next Scientific, Technological and Economic Revolution / / by Longbing Cao
| Data Science Thinking : The Next Scientific, Technological and Economic Revolution / / by Longbing Cao |
| Autore | Cao Longbing |
| Edizione | [1st ed. 2018.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 |
| Descrizione fisica | 1 online resource (XX, 390 p. 62 illus., 61 illus. in color.) |
| Disciplina | 006.312 |
| Collana | Data Analytics |
| Soggetto topico |
Data mining
Big data Artificial intelligence Data Mining and Knowledge Discovery Big Data/Analytics Artificial Intelligence |
| ISBN | 3-319-95092-4 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1 The Data Science Era -- 2 What is Data Science -- 3 Data Science Thinking -- 4 Data Science Challenges -- 5 Data Science Discipline -- 6 Data Science Foundations -- 7 Data Science Techniques -- 8 Data Economy and Industrialization -- 9 Data Science Applications -- 10 Data Profession -- 11 Data Science Education -- 12 Prospects and Opportunities in Data Science. |
| Record Nr. | UNINA-9910299164503321 |
Cao Longbing
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Global COVID-19 Research and Modeling : A Historical Record
| Global COVID-19 Research and Modeling : A Historical Record |
| Autore | Cao Longbing |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Singapore : , : Springer Singapore Pte. Limited, , 2024 |
| Descrizione fisica | 1 online resource (409 pages) |
| Disciplina | 614.5924144 |
| Collana | Data Analytics Series |
| ISBN | 981-9999-15-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Acknowledgments -- Contents -- Acronyms -- Notations -- List of Figures -- List of Tables -- 1 COVID-19 Characteristics and Complexities -- 1.1 COVID-19 Pandemic -- 1.2 Coronavirus and COVID-19 Complexities -- 1.3 COVID-19 Data Complexities -- 1.4 COVID-19 Modeling Complexities -- 1.5 Concluding Remarks -- 2 Review Objectives, Questions and Methods -- 2.1 Motivation and Objectives -- 2.1.1 Objectives for Overall Research Profiling -- 2.1.2 Objectives for Modeling Research Profiling -- 2.2 Review Questions -- 2.2.1 Review Questions for Overall Research -- 2.2.2 Review Questions for Modeling Research -- 2.3 Review Methods -- 2.3.1 Review Methods for Overall Research -- 2.3.2 Review Methods for Modeling Research -- 2.3.2.1 Review Scope -- 2.3.2.2 Modeling Methods -- 2.4 Publication Analysis Methods -- 2.4.1 Publication Analysis for Overall Research -- 2.4.2 Publication Analysis for Modeling Research -- 3 Highlights of the Findings -- 3.1 Highlights of the Findings of the Overall Research -- 3.2 Highlights of the Findings on the Modeling Research -- Part I Overall Research Profile -- 4 Overall Publication Collection and Processing -- 4.1 Publication Collection -- 4.1.1 COVID-19 Open Research Data Acquisition -- 4.1.2 Supplementary Information Collection -- 4.1.3 Collection of Publication Impact Metrics -- 4.1.3.1 The Literature Selection on COVID-19 Modeling -- 4.2 Publication Processing -- 4.2.1 Extracting Modeling Keywords -- 4.2.2 Categorizing Publication Disciplines -- 4.2.3 Extracting Modeling Publications -- 4.2.4 Extracting Global GDP and Population Data -- 4.2.5 Extracting COVID-19 Case Data -- 4.3 Evaluation Measures -- 4.3.1 Publication Impact Metrics -- 4.3.2 Composite Indicator (CI) -- 4.3.3 GDP per Capita -- 4.3.4 Publication-GDP Correlation Coefficient -- 4.3.5 Publication-COVID-19 Infection Correlation Coefficient.
5 COVID-19 Research Profile and Impact -- 5.1 Global Research Publication Profile -- 5.1.1 Overview of Research Publications -- 5.1.1.1 Total Publications -- 5.1.1.2 Publications by Discipline -- 5.1.1.3 Publications with Publication Date and First-author's Affiliated Country -- 5.1.1.4 Statistics on Publication Impacts -- 5.1.1.5 Mean Publication Impact in Major Disciplines -- 5.1.2 COVID-19 Research Publication Distribution -- 5.1.3 Word Cloud of COVID-19 Research Publications -- 5.2 COVID-19 Research Publication Impact -- 5.2.1 COVID-19 Research Publication Impact by Country's Composite Indicator -- 5.2.2 COVID-19 Research Publication Impact by H5-Index -- 5.2.3 Overall Research Publication Impact Per Impact Factor -- 5.2.4 COVID-19 Research Publication Impact Per CiteScore -- 5.2.5 COVID-19 Research Publication Impact by SNIP -- 5.2.6 COVID-19 Research Publication Impact by SJR -- 5.2.7 Top-10 Most Published Countries' Research Impact -- 5.2.8 Top-10 Most Published Countries' Research Impact by Discipline -- 5.3 Research Collaborations -- 5.4 Global research Co-authorship -- 5.4.1 Most Published Authors -- 5.4.2 Most Published Institutions with Identifiable Author Information -- 5.4.3 Co-authorship by Discipline -- 6 G20 and OECD Research Profile and Impact -- 6.1 G20 Publication Profile -- 6.1.1 G20 Countries/Regions' Publication Impact -- 6.1.2 G20 Countries/Regions' Publication Impact in Computer Science -- 6.1.3 G20 Countries/Regions' Publication Impact in Medical Science -- 6.1.4 G20 Countries/Regions' Publication Impact in Social Science -- 6.1.5 G20 Countries/Regions' Mean Publication Impact -- 6.2 OECD Publication Profile -- 6.2.1 OECD Countries' Mean Publication Impact -- 6.3 Box Plots of G20 and OECD Countries/Regions' Composite Indicator -- 6.3.1 Box Plots of G20 and OECD Countries/Regions' Paper-Averaged Composite Indicator. 6.3.2 Box Plots of G20 and OECD Countries/Regions' Daily-Averaged Cumulative Composite Indicator -- 6.3.3 Box Plots of G20 and OECD Countries/Regions' Monthly Paper-Averaged Composite Indicator -- 6.4 Box Plots of US and China's Monthly Paper-Averaged Composite Indicator -- 7 Correlations Between Research, the Economy and Infection -- 7.1 Correlation Between Research and the Economy -- 7.1.1 Global Correlation Between Publications and GDP Per Capita -- 7.1.2 Correlation Between G20 Publications and GDP Per Capita -- 7.1.3 Correlation Between OECD Publications and GDP Per Capita -- 7.2 Correlation Between Number of Publications and Infections -- 7.2.1 Global Correlation Between Number of Publications and Infections -- 7.2.2 Global Correlation Between Number of Publications and Deaths -- 7.2.3 Correlation Between Monthly Publications by Discipline and Number of Infections -- 7.2.4 Correlation Between Number of G20 Publications and Infections -- 7.2.5 Correlation Between Number of G20 Publications and Deaths -- 7.2.6 Correlation Between Number of Publications and Infections in OECD Countries -- 7.2.7 Correlation Between Number of OECD Publications and Deaths -- Part II Modeling Research Profile -- 8 Modeling Publication Collection and Processing -- 8.1 Meta-Synthetic and Meta-Analytical Review -- 8.2 Objectives of COVID-19 Modeling -- 8.3 Categorization of COVID-19 Modeling -- 9 Modeling Research Profile and Impact -- 9.1 Keyword Word Cloud Distributions -- 9.1.1 Keyword Word Cloud of Modeling Publications -- 9.1.2 Keyword Word Cloud of Modeling Publications in Computer Science -- 9.1.3 Keyword Word Cloud of Modeling Publications in Medical Science -- 9.1.4 Keyword Word Cloud of Modeling Publications in Social Science -- 9.1.5 Keyword Word Cloud of Modeling Publications from the US -- 9.1.6 Keyword Word Cloud of Modeling Publications from China. 9.1.7 Keyword Word Cloud of Modeling Publications from the EU -- 9.2 Top-k Research Trends -- 9.2.1 Top-50 Modeling Problems and Their Publication Impact -- 9.2.2 Top-50 Problems and Their Modeling Methods -- 9.2.3 Top-50 Modeling Methods and Their Publication Impact -- 9.2.4 Top-10 Monthly Problems of Concern in Modeling COVID-19 -- 9.2.5 Top-10 Problems of Concern in the Top-10 Most Published Countries -- 9.2.6 Top-10 Modeling Methods Applied by the Top-10 Most Published Countries -- 9.2.7 Top-10 Problems and Top-10 Modeling Methods by the Top-10 Most Published Countries -- 10 Modeling Methods -- 10.1 Mathematical Modeling -- 10.1.1 Time-series Analysis -- 10.1.1.1 Time-Series Models -- 10.1.1.2 Time-series Modeling -- 10.1.2 Statistical Modeling -- 10.1.2.1 Statistical Models -- 10.1.2.2 Statistical Analysis -- 10.2 Data-Driven Learning -- 10.2.1 Shallow and Deep Learning -- 10.2.2 Shallow Learning -- 10.2.3 Deep Learning -- 10.3 Domain-driven Modeling -- 10.3.1 Epidemic Modeling -- 10.3.1.1 Epidemiological Compartmental Models -- 10.3.1.2 Epidemiological Modeling -- 10.3.2 Medical and Biomedical Analyses -- 10.3.2.1 COVID-19 Infection Diagnosis, Test and Case Identification -- 10.3.2.2 Patient Risk and Prognosis Analyses -- 10.3.2.3 Medical Imaging Analyses -- 10.3.2.4 Pathological and Treatment Analyses and Drug Development -- 10.4 Influence and Impact Modeling -- 10.4.1 Modeling Intervention and Policy Effects -- 10.4.2 Modeling Psychological and Mental Impact -- 10.4.3 Modeling Economic Impact -- 10.4.4 Modeling Social Impact -- 10.5 Simulation Modeling -- 10.6 Hybrid Modeling -- Part III Examples: Modeling Techniques -- 11 Modeling Intervention, Vaccination, Mutation and Ethnic Condition Influence on Resurgence -- 11.1 Introduction -- 11.2 Data and Processing -- 11.3 Interaction and Simulation Models -- 11.4 Simulation and Forecasting Results. 11.4.1 Main Results -- 11.4.2 Additional Results -- 11.4.3 Findings and Insights -- 11.4.4 Discussion -- 11.5 Conclusions and Future Work -- 11.5.1 Concluding Remarks -- 11.5.2 Gaps and Opportunities -- 12 AISDR: AI and Data Science for Crisis and Disaster Resilience -- 12.1 Emergencies, Crises and Disasters -- 12.2 The ECD Landscape -- 12.3 From ECD Management to Resilience -- 12.3.1 Classic ECD Management -- 12.3.2 Smart ECD Resilience: The SDR Landscape -- 12.4 ECD Data and Complexities -- 12.4.1 ECD Data Sources -- 12.4.2 ECD Data Characteristics and Complexities -- 12.5 AISDR Research Landscape -- 12.5.1 AISDR Research Map -- 12.5.2 AISDR Research Tasks -- 13 Making Science Ready for Future Emergencies, Crises and Disasters -- 13.1 COVID-19 Modeling Gap Analyses -- 13.1.1 Limitations in COVID-19 Modeling Research -- 13.1.2 Gaps in Understanding the Nature of the COVID-19 Problem -- 13.1.3 Gaps in Modeling COVID-19 System Complexities -- 13.1.4 Gaps in Actionable COVID-19 Modeling and Validation -- 13.2 ECD Research Gap Analyses -- 13.2.1 ECD Research Review -- 13.2.2 Gap Analyses of ECD Research -- 13.3 Preparing for Future Emergencies, Crises and Disasters -- 13.3.1 Characterizing ECD Ecosystem Complexities -- 13.3.2 Enhancing Deep ECD Analytics and Learning -- 13.3.3 Exploring New Epidemic and ECD Modeling Opportunities -- 13.4 Future of AISDR -- 13.5 Concluding Remarks -- A List of Disciplinary Categorization -- B List of Predefined Modeling Keywords -- C List of COVID-19 Publication Metadata -- D List of Publication Impact Metrics -- E List of COVID-19 Publication Analysis Results -- F List of COVID-19 Public Data -- References -- Index. |
| Record Nr. | UNINA-9910847087103321 |
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Cao Longbing
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| Singapore : , : Springer Singapore Pte. Limited, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Metasynthetic Computing and Engineering of Complex Systems / / by Longbing Cao
| Metasynthetic Computing and Engineering of Complex Systems / / by Longbing Cao |
| Autore | Cao Longbing |
| Edizione | [1st ed. 2015.] |
| Pubbl/distr/stampa | London : , : Springer London : , : Imprint : Springer, , 2015 |
| Descrizione fisica | 1 online resource (360 p.) |
| Disciplina | 006.3 |
| Collana | Advanced Information and Knowledge Processing |
| Soggetto topico |
Software engineering
Robotics Automation Artificial intelligence Software Engineering/Programming and Operating Systems Robotics and Automation Artificial Intelligence |
| ISBN | 1-4471-6551-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Preface; Contents; Chapter 1: Complex Systems; 1.1 Introduction; 1.2 System Complexities; 1.3 System Transparency; 1.3.1 Black Boxes; 1.3.2 White Boxes; 1.3.3 Glass Boxes; 1.3.4 Grey Boxes; 1.4 System Classification; 1.5 Complex Agent Systems; 1.5.1 Multiagent Systems; 1.5.1.1 What Are Multiagent Systems; 1.5.1.2 Multiagent System Research Map; 1.5.2 Large-Scale Systems; 1.5.3 Large-Scale Multiagent Systems; 1.5.3.1 Concepts and Issues; 1.5.3.2 How Are ULS Systems Different? [40]; 1.5.3.3 Major Research Issues; 1.5.4 Open Complex Agent Systems; 1.5.4.1 Multiagent System Classification
1.5.4.2 Open Complex Agent Systems1.6 Hybrid Intelligent Systems; 1.6.1 Concept; 1.6.2 Hybridization Strategies; 1.6.3 Design Strategies; 1.6.4 Typical Hybrid Applications; 1.7 Evolution of Intelligent Systems; 1.8 Open Giant Intelligent Systems; 1.9 Computing and Engineering Complex Systems; 1.10 Summary; References; Chapter 2: Ubiquitous Intelligence; 2.1 Introduction; 2.2 Data Intelligence; 2.2.1 What Is Data Intelligence?; 2.2.2 Aims of Involving Data Intelligence; 2.2.3 Aspects of Data Intelligence; 2.3 Domain Intelligence; 2.3.1 What Is Domain Intelligence? 2.3.2 Aims of Involving Domain Intelligence2.3.3 Aspects of Domain Intelligence; 2.4 Network Intelligence; 2.4.1 What Is Network Intelligence?; 2.4.2 Aims of Involving Network Intelligence; 2.4.3 Aspects of Network Intelligence; 2.5 Human Intelligence; 2.5.1 What Is Human Intelligence?; 2.5.2 Aims of Involving Human Intelligence; 2.5.3 Aspects of Human Intelligence; 2.6 Organizational Intelligence; 2.6.1 What Is Organizational Intelligence?; 2.6.2 Aims of Involving Organizational Intelligence; 2.6.3 Aspects of Organizational Intelligence; 2.7 Social Intelligence 2.7.1 What Is Social Intelligence?2.7.2 Aims of Involving Social Intelligence; 2.7.3 Aspects of Social Intelligence; 2.8 Metasynthesis of Ubiquitous Intelligence; 2.9 Summary; References; Chapter 3: System Methodologies; 3.1 Introduction; 3.2 Reductionism; 3.3 Holism; 3.4 Systematology; 3.5 Summary; References; Chapter 4: Computing Paradigms; 4.1 Introduction; 4.2 Objects and Object-Oriented Methodology; 4.3 Components and Component-Based Methodology; 4.4 Services and Service-Oriented Methodology; 4.5 Agents and Agent-Oriented Methodology; 4.5.1 Goal-Oriented Requirements Analysis 4.5.2 Agent-Oriented Software Engineering4.5.2.1 MaSE; 4.5.2.2 MESSAGE; 4.5.2.3 TROPOS; 4.5.2.4 GAIA; 4.5.3 Issues in Agent-Oriented Software Engineering; 4.6 Relations Among Agents, Objects, Components, and Services; 4.7 Autonomic Computing; 4.8 Organizational Computing; 4.9 Behavior Computing; 4.10 Social Computing; 4.11 Cloud/Service Computing; 4.12 Metasynthetic Computing; References; Chapter 5: Metasynthesis; 5.1 Introduction; 5.2 Open Complex Giant Systems; 5.3 OCGS System Complexities; 5.4 Knowledge and Intelligence Emergence; 5.5 Theoretical Framework of Metasynthesis 5.6 Problem-Solving Process in M-Space |
| Record Nr. | UNINA-9910299253203321 |
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Cao Longbing
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| London : , : Springer London : , : Imprint : Springer, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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