Table of Contents

Part I: Fundamentals and Concepts

Chapter 1: Real-time Linked Dataspaces: A Data Platform for Intelligent Systems within Internet of Things-based Smart Environments

  • Introduction
  • Foundations
    • Intelligent Systems
    • Smart Environments
    • Internet of Things
    • Data Ecosystems
    • Enabling Data Ecosystem for Intelligent Systems
  • Real-time Linked Dataspaces
  • Book Overview
  • Summary

Chapter 2: Enabling Knowledge Flows in an Intelligent Systems Data Ecosystem

  • Introduction
  • Foundations
    • Intelligent Systems Data Ecosystem
    • System of Systems
    • From Deterministic to Probabilistic Decisions in Intelligent Systems
    • Digital Twins
  • Knowledge Exchange between Open Intelligent Systems in Dynamic Environments
  • Knowledge Value Ecosystem (KVE) Framework
  • Knowledge: Transfer and Translation
    • Entity-centric Data Integration
    • Linked Data
    • Knowledge Graphs
    • Smart Environment Example
  • Value: Continuous and Shared
    • Value Disciplines
    • Data Network Effects
  • Ecosystem: Governance and Collaboration
    • From Ecology and Business to Data
    • The Web of Data: A Global Data Ecosystem
    • Ecosystem Coordination
    • Data Ecosystem Design
  • Iterative Boundary Process: Pay-as-you-go
    • Dataspace Incremental Data Management
  • Data Platforms for Intelligent Systems within IoT-based Smart Environment
    • FAIR Data Principles
    • Requirements Analysis
  • Summary

Chapter 3: Dataspaces: Fundamentals, Principles, and Techniques

  • Introduction
  • Big Data and The Long Tail of Data
  • The Changing Cost of Data Management
  • Approximate, Best-Effort, and “Good Enough” Information
  • Fundamentals of Dataspaces
    • Definition and Principles
    • Comparison to Existing Approaches
  • Dataspace Support Platform
    • Support Services
    • Life Cycle
    • Implementations
  • Dataspace Technical Challenges
    • Query Answering
    • Introspection
    • Reusing Human Attention
  • Dataspace Research Challenges
  • Summary

Chapter 4: Real-time Linked Dataspaces: Fundamentals, Principles, and Techniques

  • Introduction
  • Event and Stream Processing for the Internet of Things
    • Timeliness and Real-time Processing
  • Fundamentals of Real-time Linked Dataspaces
    • Foundations
    • Definition and Principles
    • Comparison of Dataspaces
    • Architecture
  • A Principled Approach to Pay-as-you-go Data Management
    • TBL’s Five Star Data
    • 5 Star Pay-as-you-go Model for Dataspace Services
  • Support Platform
    • Data Services
    • Stream and Event Processing Services
  • Suitability as a Data Platform for Intelligent Systems within IoT-based Smart Environments
    • Common Data Platform Requirements
    • Related Work
  • Summary

Part II: Data Support Services

Chapter 5: Data Support Services for Real-time Linked Dataspaces

  • Introduction
  • Pay-as-you-go Data Support Services for Real-time Linked Dataspaces
  • 5 Star Pay-as-you-go Levels for Data Services
  • Summary

Chapter 6: Catalog and Entity Management Service for Internet of Things-based Smart Environments

  • Introduction
  • Working with Entity Data
  • Catalog and Entity Service Requirements for Real-time Linked Dataspaces
    • Real-time Linked Dataspaces
    • Requirements
  • Analysis of Existing Data Catalogs
  • Catalog Service
    • Pay-as-you-go Service Levels
  • Entity Management Service
    • Pay-as-you-go Service Levels
    • Entity Example
  • Access Control Service
    • Pay-as-you-go Service Levels
  • Joining the Real-time Linked Dataspace
  • Summary

Chapter 7: Querying and Searching Heterogeneous Knowledge Graphs in Real-time Linked Dataspaces    

  • Introduction
  • Querying and Searching in Real-time Linked Dataspaces
    • Real-time Linked Dataspaces
    • Knowledge Graphs
    • Searching vs Querying
    • Search and Query Service Pay-as-you-go Service Levels
  • Search and Query over Heterogeneous Data
    • Data Heterogeneity
    • Motivational Scenario
    • Core Requirements for Search and Query
  • State-of-the-Art Analysis
    • Information Retrieval Approaches
    • Natural Language Approaches
    • Discussion
  • Design Features for Schema-agnostic Queries
  • Summary

Chapter 8: Enhancing the Discovery of Internet of Things-based Data Services in Real-time Linked Dataspaces

  • Introduction
  • Discovery of Data Services in Real-time Linked Dataspaces
    • Real-time Linked Dataspaces
    • Data Service Discovery
  • Semantic Approaches for Service Discovery
    • Inheritance Between OWL-S Services
    • Topic Extraction and Formal Concept Analysis
    • Reasoning-based Matching
    • Numerical Encoding of Ontological Concepts
    • Discussion
  • Formal Concept Analysis for Organizing IoT Data Service Descriptions
  • IoT-based Smart Environment Use Case
  • Conclusions and Future Work

Chapter 9: Human-in-the-Loop Tasks for Data Management, Citizen Sensing, and Actuation in Smart Environments        

  • Introduction
  • The Wisdom of the Crowds
    • Crowdsourcing Platform
  • Challenges of Enabling Crowdsourcing
  • Approaches to Human-in-the-Loop
    • Augmented Algorithms & Operators
    • Declarative Programming
    • Generalised Standalone Platforms
  • Comparison of Existing Approaches
  • Human Task Service for Real-time Linked Dataspaces
    • Real-time Linked Dataspaces
    • Human Task Service
    • Pay-as-you-go Service Levels
    • Applications of Human Task Service
    • Data Processing Pipeline
    • Task Data Model for Micro-Tasks and Users
    • Spatial Task Assignment in Smart Environments
  • Summary

Part III: Stream and Event Processing Services

Chapter 10: Stream and Event Processing Services for Real-time Linked Dataspaces

  • Introduction
  • Pay-as-you-go Services for Event and Stream Processing for Real-time Linked Dataspaces
  • Entity-centric Real-time Query Service
    • Lambda Architecture
    • Entity-centric Real-time Query Service
    • Pay-as-you-go Service Levels
    • Service Performance
  • Summary

Chapter 11: Quality of Service-Aware Complex Event Service Composition in Real-time Linked Dataspaces          

  • Introduction
  • Complex Event Processing in Real-time Linked Dataspaces
    • Real-time Linked Dataspaces
    • Complex Event Processing
    • CEP Service Design
    • Pay-as-you-go Service Levels
    • Event Service Life Cycle
  • QoS Model and Aggregation Schema
    • QoS Properties of Event Services
    • QoS Aggregation and Utility Function
    • Event QoS Utility Function
  • Genetic Algorithm for QoS-Aware Event Service Composition Optimisation
    • Population Initialisation
    • Genetic Encodings for Concrete Composition Plans
    • Crossover and Mutation Operations
  • Evaluation
    • Part 1: Performance of the Genetic Algorithm
    • Part 2: Validation of QoS Aggregation Rules
  • Related Work
    • QoS-aware Service Composition
    • On-demand Event/Stream Processing
  • Summary and Future work

Chapter 12: Dissemination of Internet of Things Streams in a Real-time Linked Dataspace

  • Introduction
  • Internet of Things: A Dataspace Perspective
    • Real-time Linked Dataspaces
  • Stream Dissemination Service
    • Pay-as-you-go Service Levels
  • Point-to-Point Linked Data Stream Dissemination
    • TP-automata for Pattern Matching
  • Linked Data Stream Dissemination via Wireless Broadcast
    • The Mapping between Triples and 3D Points
    • 3D Hilbert Curve Index
  • Experimental Evaluation
    • Evaluation of Point-to-point Linked Stream Dissemination
    • Evaluation on Linked Stream Dissemination via Wireless Broadcast
  • Related Work
    • Matching
    • Wireless Broadcast
  • Summary and Future Work

Chapter 13: Approximate Semantic Event Processing in Real-time Linked Dataspaces

  • Introduction
  • Approximate Event Matching in Real-time Linked Dataspaces
    • Real-time Linked Dataspaces
    • Event Processing
  • The Approximate Semantic Matching Service
    • Pay-as-you-go Service Levels
    • Semantic Matching Models
    • Model I- The Approximate Event Matching Model
    • Model II- The Thematic Event Matching Model
  • Elements for Approximate Semantic Matching of Events
    • Elm 1- Sub-symbolic Distributional Event Semantics
    • Elm 2- Free Event Tagging
    • Elm 3- Approximation
    • Elements within the Event Flow Functional Model
  • Instantiation
    • Events
    • Subscriptions
    • Matching
  • Evaluation and Discussion
    • Evaluation of the Approximate Semantic Event Matching Model
    • Evaluation of the Thematic Event Matching Model
  • State-of-the-art Analysis
  • Summary and Future Work

Part IV: Intelligent Systems and Applications         

Chapter 14: Enabling Intelligent Systems, Applications, and Analytics for Smart Environments using Real-time Linked Dataspaces

  • Introduction
  • Intelligent Energy and Water Management
  • Real-time Linked Dataspaces
  • Smart Environment Pilot Deployments
    • Smart Airport (Linate, Milan)
    • Smart Office (Galway, Ireland)
    • Smart Homes (Municipality of Thermi, Greece)
    • Mixed Use (Galway, Ireland)
    • Smart School (Galway, Ireland)
    • Target Users Groups
  • Enabling Intelligent Systems, Applications, and Analytics for Smart Environments
  • Summary

Chapter 15: Autonomic Source Selection for Real-time Predictive Analytics using the Internet of Things and Open Data

  • Introduction
  • Source Selection for Analytics in Dataspaces
    • Real-time Linked Dataspaces
    • Internet of Things Source Selection Challenges
  • Autonomic Source Selection Service for Real-time Predictive Analytics
    • Autonomic Source Selection
  • Architecture
    • Prediction Models
  • Autonomic Source Selection Workflow
    • 4 Step Workflow
    • Re-selection Triggers
  • Evaluation within Intelligent Systems
    • Wind Farm Energy Prediction (Belgium)
    • Building Energy Prediction (Galway, Ireland)
  • Summary

Chapter 16: Building Internet of Things-enabled Digital Twins and Intelligent Applications using a Real-time Linked Dataspace

  • Introduction
  • Digital Twins and Intelligent Applications with a Real-time Linked Dataspace
    • Real-time Linked Dataspaces
    • Digital Twins
    • The OODA Loop
  • Enabling OODA for Digital Twins and Intelligent Applications
    • Observation
    • Orientation
    • Decision
    • Action
  • Smart Energy and Water Pilot Results
    • Energy and Water Savings
    • Human Task Service Evaluation
  • Experiences and Lessons Learnt
  • Summary

Chapter 17: A Model for Internet of Things Enhanced User Experience in Smart Environments

  • Introduction
  • A Model for Internet of Things Enhanced User Experience
    • Digitalisation: IoT and Big Data
    • Human-Computer Interaction: IoT-enhanced User Experience and Behavioural Models
  • An IoT-enhanced Journey for Smart Energy and Water
    • Digital: Real-time Linked Dataspace
    • HCI: A User’s Journey to Sustainability using the Transtheoretical Model
  • TTM Intelligent Applications
    • Promotional Homepage
    • Dashboard Tour
    • Sense Dashboard
  • User Study
    • Methodology
    • Impact
  • Insights and Experience Gained
  • Summary

Part V: Future Directions

Chapter 18: Future Research Directions for Dataspaces, Data Ecosystems, and Intelligent Systems

  • Introduction
  • Dataspaces: From Proof-of-concept to Widespread Adoption
  • Research Directions
    • Large-scale Decentralised Support Services
    • Multimedia/Knowledge-Intensive Event Processing
    • Trusted Data Sharing
    • Ecosystem Governance and Economic Models
    • Incremental Intelligent Systems Engineering: Cognitive Adaptability
    • Towards Human-centric Systems
  • Summary