Real time Linked Dataspaces Enabling Data Ecosystems for Intelligent Systems 1st edition by Edward Curry ISBN 3030296644  978-3030296643

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ISBN 10: ‎ 3030296644
ISBN 13: 978-3030296643
Author: Edward Curry

This open access book explores the dataspace paradigm as a best-effort approach to data management within data ecosystems. It establishes the theoretical foundations and principles of real-time linked dataspaces as a data platform for intelligent systems. The book introduces a set of specialized best-effort techniques and models to enable loose administrative proximity and semantic integration for managing and processing events and streams.

The book is divided into five major parts: Part I “Fundamentals and Concepts” details the motivation behind and core concepts of real-time linked dataspaces, and establishes the need to evolve data management techniques in order to meet the challenges of enabling data ecosystems for intelligent systems within smart environments. Further, it explains the fundamental concepts of dataspaces and the need for specialization in the processing of dynamic real-time data. Part II “Data Support Services” explores the design and evaluation of critical services, including catalog, entity management, query and search, data service discovery, and human-in-the-loop. In turn, Part III “Stream and Event Processing Services” addresses the design and evaluation of the specialized techniques created for real-time support services including complex event processing, event service composition, stream dissemination, stream matching, and approximate semantic matching. Part IV “Intelligent Systems and Applications” explores the use of real-time linked dataspaces within real-world smart environments. In closing, Part V “Future Directions” outlines future research challenges for dataspaces, data ecosystems, and intelligent systems.

Readers will gain a detailed understanding of how the dataspace paradigm is now being used to enable data ecosystems for intelligent systems within smart environments. The book covers the fundamental theory, the creation of new techniques needed for support services, and lessons learned from real-world intelligent systems and applications focused on sustainability. Accordingly, it will benefit not only researchers and graduate students in the fields of data management, big data, and IoT, but also professionals who need to create advanced data management platforms for intelligent systems, smart environments, and data ecosystems.

Real-time Linked Dataspaces Enabling Data Ecosystems for Intelligent Systems 1st Table of contents:

Preface

• Introduction to real-time linked dataspaces
• Importance of enabling data ecosystems for intelligent systems
• Overview of the book structure

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Part I: Foundations of Linked Dataspaces and Real-time Data

Chapter 1: Introduction to Linked Dataspaces

• Definition and core principles of dataspace models
• Key technologies behind linked data: RDF, SPARQL, and semantic web
• The role of metadata and ontologies in linked dataspaces

Chapter 2: Real-time Data and Its Challenges

• Characteristics of real-time data streams
• Managing and processing real-time data
• Latency, scalability, and fault tolerance in real-time systems

Chapter 3: Data Integration and Interoperability

• Techniques for integrating heterogeneous data sources
• Interoperability challenges and solutions
• The role of APIs and data connectors in data ecosystems

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Part II: Architectures and Technologies for Data Ecosystems

Chapter 4: Architecting Data Ecosystems

• Overview of data ecosystem architectures
• Role of cloud computing, edge computing, and hybrid solutions
• Use cases and examples of data ecosystem applications

Chapter 5: Data Modeling for Real-time Systems

• Designing real-time data models for intelligent systems
• Use of ontologies and knowledge graphs
• Data structuring for effective real-time analytics

Chapter 6: Enabling Real-time Linked Data

• Methods and tools for linking real-time data in dynamic environments
• Real-time data processing frameworks (e.g., Apache Kafka, Flink)
• Integrating linked data technologies with real-time systems

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Part III: Applications in Intelligent Systems

Chapter 7: Intelligent Systems and Decision Support

• Role of linked data in decision support systems
• Real-time analytics for improving decision-making
• Case studies: healthcare, smart cities, and autonomous vehicles

Chapter 8: Internet of Things (IoT) and Data Ecosystems

• Using real-time linked dataspace models in IoT environments
• Data integration and analysis in IoT-driven systems
• Real-time sensing, monitoring, and control in IoT ecosystems

Chapter 9: Machine Learning and AI in Data Ecosystems

• Leveraging real-time data for machine learning applications
• AI techniques for enhancing data-driven decision-making
• Integrating AI models with real-time data ecosystems

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Part IV: Future Trends and Challenges

Chapter 10: Challenges in Real-time Data Ecosystems

• Data privacy, security, and governance issues in real-time linked dataspaces
• Addressing scalability, robustness, and latency challenges
• Managing data quality and consistency across diverse systems

Chapter 11: Emerging Trends in Linked Data and Intelligent Systems

• Innovations in real-time data processing and linked data technologies
• Future directions for AI and IoT integration with data ecosystems
• Vision for the evolution of data ecosystems in intelligent systems

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Appendices

• A. Glossary of Terms
• B. Tools and Frameworks for Real-time Linked Data
• C. References

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