Learning-Driven Data Fabrics for Sustainability : Cloud-to-Thing Continuum Solutions for Global Challenges / / edited by Prithi Samuel, Daniel Arockiam, Balamurugan Balusamy, Liza Macasukit Gernal, Chander Prabha

• Autore: Samuel Prithi
• Pubblicazione: Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2026
• Edizione: 1st ed. 2026.
• Descrizione fisica: 1 online resource (378 pages)
• Disciplina: 304.2
• Soggetto topico: Sustainability; Internet of things; Artificial intelligence; Internet of Things; Artificial Intelligence
• Nota di contenuto: 1. Learning-Driven Data Fabric Evolution: A Thorough Overview -- 2. Cloud-to-Thing Continuum: Connecting IoT Devices and Cloud Infrastructures -- 3. Advances and Considerations for Machine Learning Integration in Data Fabric -- 4. Data Interoperability: Enabling Smooth Cross-Platform Communication -- 5. Security Issues in Data Fabric Driven by Learning -- 6. Scalability Considerations: Fulfilling the Increasing Data Requirements -- 7. Dynamics of Edge Computing in the Cloud-To-Thing Continuum -- 8. Learning-Driven Data Fabric: Innovations and Applications for Real-Time Insights -- 9. Psychological Resilience in Data-Driven Workflows -- 10. Learning-Driven Data Fabric Innovations: Current Advancements -- 11. Adaptive Data Fabric: Flexible Adaptation to Changing Circumstances -- 12. Human-Machine Cooperation in Data Fabric Driven by Learning -- 13. Privacy-Preserving Data Fabric: Judging Between Confidentiality and Utility -- 14. Learning-Driven Data Fabric Governance: Standards and Regulatory Compliance -- 15. Human-Machine Synergy in Learning-Driven Data Fabrics.
• Sommario/riassunto: This book explores the distinct problems, trends, and future trajectories for constructing cohesive, sustainable data infrastructures that correspond with the United Nations Sustainable Development Goals (SDGs). In the contemporary digital ecosystem, the amalgamation of data across diverse platforms and environments—from cloud to edge to IoT—has become imperative for fostering creativity, sustainability, and efficiency. “Learning-Driven Data Fabric for Sustainable Cloud-to-Thing Continuum” examines the optimization of integration through sophisticated data fabrics enhanced by machine learning and AI. This book initiates its exploration by analyzing the fundamental concepts of a learning-driven data fabric that integrates cloud and IoT ecosystems, facilitating real-time decision-making and minimizing energy consumption. It offers comprehensive insights into how intelligent data management throughout the cloud-to-thing continuum may be utilized to enhance resource efficiency, facilitate smart city planning, and promote advancements in sectors such as healthcare, transportation, and agriculture. This book emphasizes how data fabrics may advance objectives related to affordable and clean energy (SDG 7), industrial innovation (SDG 9), and sustainable cities and communities (SDG 11), with sustainability as its central theme. This book illustrates how learning-driven data architectures are revolutionizing businesses and tackling global challenges through real-world case studies and upcoming trends. Subjects encompass edge computing, real-time data analytics, safe data transmission, and the reduction of carbon footprints via effective data processing. This study examines how data fabrics might alleviate the risks associated with cyberattacks and data breaches, while ensuring regulatory compliance and fostering sustainable, ethical AI operations. This book offers a detailed framework for utilizing data fabric technologies to create sustainable, safe, and intelligent cloud-to-thing ecosystems, regardless of whether you are a data scientist, IoT specialist, cloud architect, or policymaker. This book promotes data-driven decision-making throughout the infrastructure, enabling organizations to design scalable and sustainable solutions that advance global development objectives.
• Titolo autorizzato: Learning-Driven Data Fabrics for Sustainability
• ISBN: 3-032-09090-3
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9911066006803321
• Lo trovi qui: Univ. Federico II
• Serie: Sustainable Artificial Intelligence-Powered Applications, IEREK Interdisciplinary Series for Sustainable Development, . 3005-1770