Data Privacy and Trust in Cloud Computing : Building trust in the cloud through assurance and accountability / / edited by Theo Lynn, John G. Mooney, Lisa van der Werff, Grace Fox |
Autore | Lynn Theo |
Edizione | [1st ed. 2021.] |
Pubbl/distr/stampa | Springer Nature, 2021 |
Descrizione fisica | 1 online resource (XXI, 149 p. 2 illus.) |
Disciplina |
658.514
004.6782 |
Collana | Palgrave Studies in Digital Business & Enabling Technologies |
Soggetto topico |
Management
Industrial management Big data Data protection Electronic commerce Innovation/Technology Management Big Data/Analytics Security e-Commerce/e-business |
Soggetto non controllato |
Innovation/Technology Management
Big Data/Analytics Security e-Commerce/e-business Business and Management IT in Business Computer Science e-Commerce and e-Business GDPR Data regulation accountability ethics in computing HIPAA it information management internet open access Research & development management Industrial applications of scientific research & technological innovation Business mathematics & systems Computer security Business applications E-commerce: business aspects |
ISBN | 3-030-54660-8 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | Chapter 1: Understanding Trust and Cloud Computing: An Integrated Framework for Assurance and Accountability in the Cloud -- Chapter 2: Dear Cloud, I think we have trust issues: Cloud Computing Contracts and Trust -- Chapter 3: Competing Jurisdictions – Data Privacy Across the Border -- Chapter 4: Understanding and Enhancing Consumer Privacy Perceptions in the Cloud -- Chapter 5: Justice vs Control in Cloud Computing: A Conceptual Framework for Positioning a Cloud Service Provider’s Privacy Orientation -- Chapter 6: Ethics and Cloud Computing -- Chapter 7: Trustworthy Cloud Computing. |
Record Nr. | UNINA-9910424948903321 |
Lynn Theo
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Springer Nature, 2021 | ||
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Lo trovi qui: Univ. Federico II | ||
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Data Science for Economics and Finance [[electronic resource] ] : Methodologies and Applications / / edited by Sergio Consoli, Diego Reforgiato Recupero, Michaela Saisana |
Autore | Consoli Sergio |
Edizione | [1st ed. 2021.] |
Pubbl/distr/stampa | Springer Nature, 2021 |
Descrizione fisica | 1 online resource (XIV, 355 p. 56 illus., 44 illus. in color.) |
Disciplina | 006.312 |
Soggetto topico |
Data mining
Machine learning Management information systems Big data Application software Information storage and retrieval Data Mining and Knowledge Discovery Machine Learning Business Information Systems Big Data/Analytics Computer Appl. in Administrative Data Processing Information Storage and Retrieval |
Soggetto non controllato |
Data Mining and Knowledge Discovery
Machine Learning Business Information Systems Big Data/Analytics Computer Appl. in Administrative Data Processing Information Storage and Retrieval IT in Business Computer and Information Systems Applications Open Access Data Mining Big Data Data Analytics Decision Support Systems Semantics and Reasoning Expert systems / knowledge-based systems Business mathematics & systems Public administration Information technology: general issues Information retrieval Data warehousing |
ISBN | 3-030-66891-6 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | Data Science Technologies in Economics and Finance: A Gentle Walk-In -- Supervised Learning for the Prediction of Firm Dynamics -- Opening the Black Box: Machine Learning Interpretability and Inference Tools with an Application to Economic Forecasting -- Machine Learning for Financial Stability -- Sharpening the Accuracy of Credit Scoring Models with Machine Learning Algorithms -- Classifying Counterparty Sector in EMIR Data -- Massive Data Analytics for Macroeconomic Nowcasting -- New Data Sources for Central Banks -- Sentiment Analysis of Financial News: Mechanics and Statistics -- Semi-supervised Text Mining for Monitoring the News About the ESG Performance of Companies -- Extraction and Representation of Financial Entities from Text -- Quantifying News Narratives to Predict Movements in Market Risk -- Do the Hype of the Benefits from Using New Data Science Tools Extend to Forecasting Extremely Volatile Assets? -- Network Analysis for Economics and Finance: An application to Firm Ownership. |
Record Nr. | UNISA-996464413703316 |
Consoli Sergio
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Springer Nature, 2021 | ||
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Lo trovi qui: Univ. di Salerno | ||
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Data Science for Economics and Finance : Methodologies and Applications / / edited by Sergio Consoli, Diego Reforgiato Recupero, Michaela Saisana |
Autore | Consoli Sergio |
Edizione | [1st ed. 2021.] |
Pubbl/distr/stampa | Springer Nature, 2021 |
Descrizione fisica | 1 online resource (XIV, 355 p. 56 illus., 44 illus. in color.) |
Disciplina | 006.312 |
Soggetto topico |
Data mining
Machine learning Management information systems Big data Application software Information storage and retrieval Data Mining and Knowledge Discovery Machine Learning Business Information Systems Big Data/Analytics Computer Appl. in Administrative Data Processing Information Storage and Retrieval |
Soggetto non controllato |
Data Mining and Knowledge Discovery
Machine Learning Business Information Systems Big Data/Analytics Computer Appl. in Administrative Data Processing Information Storage and Retrieval IT in Business Computer and Information Systems Applications Open Access Data Mining Big Data Data Analytics Decision Support Systems Semantics and Reasoning Expert systems / knowledge-based systems Business mathematics & systems Public administration Information technology: general issues Information retrieval Data warehousing |
ISBN | 3-030-66891-6 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | Data Science Technologies in Economics and Finance: A Gentle Walk-In -- Supervised Learning for the Prediction of Firm Dynamics -- Opening the Black Box: Machine Learning Interpretability and Inference Tools with an Application to Economic Forecasting -- Machine Learning for Financial Stability -- Sharpening the Accuracy of Credit Scoring Models with Machine Learning Algorithms -- Classifying Counterparty Sector in EMIR Data -- Massive Data Analytics for Macroeconomic Nowcasting -- New Data Sources for Central Banks -- Sentiment Analysis of Financial News: Mechanics and Statistics -- Semi-supervised Text Mining for Monitoring the News About the ESG Performance of Companies -- Extraction and Representation of Financial Entities from Text -- Quantifying News Narratives to Predict Movements in Market Risk -- Do the Hype of the Benefits from Using New Data Science Tools Extend to Forecasting Extremely Volatile Assets? -- Network Analysis for Economics and Finance: An application to Firm Ownership. |
Record Nr. | UNINA-9910484567403321 |
Consoli Sergio
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Springer Nature, 2021 | ||
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Lo trovi qui: Univ. Federico II | ||
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Measuring the business value of cloud computing / / Theo Lynn, John G. Mooney, Pierangelo Rosati, Grace Fox, editors |
Autore | Lynn Theo |
Pubbl/distr/stampa | Springer Nature, 2020 |
Descrizione fisica | 1 online resource (xxiii, 125 pages) : illustrations; digital, PDF file(s) |
Disciplina | 658.514 |
Collana | Palgrave Studies in Digital Business & Enabling Technologies |
Soggetto topico |
Management
Industrial management Big data Management information systems E-commerce Innovation/Technology Management Big Data/Analytics Enterprise Architecture e-Commerce/e-business |
Soggetto non controllato |
Innovation/Technology Management
Big Data/Analytics Enterprise Architecture e-Commerce/e-business Business and Management IT in Business e-Commerce and e-Business open access business value models Infrastructure-as-a-Service Platform-as-a-Service microservice Software-as-a-Service Business Process-as-a-Service brokerage cloud marketplace digital ecosystem deployment model edge fog mist Research & development management Industrial applications of scientific research & technological innovation Business mathematics & systems Business applications E-commerce: business aspects |
ISBN | 3-030-43198-3 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | Chapter 1 – Measuring the Business Value of IT -- Chapter 2 –Measuring the Business Value of Infrastructure Migration to the Cloud.-Chapter 3 - The SaaS Payoff: Measuring the Business Value of Provisioning Software-as-a-Service Technologies Chapter 4 – Cloud service brokerage: Exploring characteristics and benefits of B2B cloud application marketplaces -- Chapter 5 – Economic Models for Federated Clouds: An Extension of Cost Models for Cloud Deployments -- Chapter 6 – Value creation and power asymmetries in digital ecosystems: A study of a cloud gaming provider -- Chapter 7 - Measuring the Business Value of Cloud Computing: Emerging Paradigms and Future Directions for Research. |
Record Nr. | UNINA-9910418354103321 |
Lynn Theo
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Springer Nature, 2020 | ||
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Lo trovi qui: Univ. Federico II | ||
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