Real-Time Linked Dataspaces : : Enabling Data Ecosystems for Intelligent Systems.

• Author: Curry, Edward.
• Place / Publishing House: Cham : : Springer International Publishing AG,, 2019.; ©2020.
• Year of Publication: 2019
• Edition: 1st ed.
• Language: English
• Physical Description: 1 online resource (333 pages)

Table of Contents

• Intro
• Foreword
• Preface
• Acknowledgements
• Contents
• Part I: Fundamentals and Concepts
• Chapter 1: Real-time Linked Dataspaces: A Data Platform for Intelligent Systems Within Internet of Things-Based Smart Environm...
• 1.1 Introduction
• 1.2 Foundations
• 1.2.1 Intelligent Systems
• 1.2.2 Smart Environments
• 1.2.3 Internet of Things
• 1.2.4 Data Ecosystems
• 1.2.5 Enabling Data Ecosystem for Intelligent Systems
• 1.3 Real-time Linked Dataspaces
• 1.4 Book Overview
• 1.5 Summary
• Chapter 2: Enabling Knowledge Flows in an Intelligent Systems Data Ecosystem
• 2.1 Introduction
• 2.2 Foundations
• 2.2.1 Intelligent Systems Data Ecosystem
• 2.2.2 System of Systems
• 2.2.3 From Deterministic to Probabilistic Decisions in Intelligent Systems
• 2.2.4 Digital Twins
• 2.3 Knowledge Exchange Between Open Intelligent Systems in Dynamic Environments
• 2.4 Knowledge Value Ecosystem (KVE) Framework
• 2.5 Knowledge: Transfer and Translation
• 2.5.1 Entity-Centric Data Integration
• 2.5.2 Linked Data
• 2.5.3 Knowledge Graphs
• 2.5.4 Smart Environment Example
• 2.6 Value: Continuous and Shared
• 2.6.1 Value Disciplines
• 2.6.2 Data Network Effects
• 2.7 Ecosystem: Governance and Collaboration
• 2.7.1 From Ecology and Business to Data
• 2.7.2 The Web of Data: A Global Data Ecosystem
• 2.7.3 Ecosystem Coordination
• 2.7.4 Data Ecosystem Design
• 2.8 Iterative Boundary Crossing Process: Pay-As-You-Go
• 2.8.1 Dataspace Incremental Data Management
• 2.9 Data Platforms for Intelligent Systems Within IoT-Based Smart Environment
• 2.9.1 FAIR Data Principles
• 2.9.2 Requirements Analysis
• 2.10 Summary
• Chapter 3: Dataspaces: Fundamentals, Principles, and Techniques
• 3.1 Introduction
• 3.2 Big Data and the Long Tail of Data
• 3.3 The Changing Cost of Data Management.
• 3.4 Approximate, Best-Effort, and ``Good Enough ́́Information
• 3.5 Fundamentals of Dataspaces
• 3.5.1 Definition and Principles
• 3.5.2 Comparison to Existing Approaches
• 3.6 Dataspace Support Platform
• 3.6.1 Support Services
• 3.6.2 Life Cycle
• 3.6.3 Implementations
• 3.7 Dataspace Technical Challenges
• 3.7.1 Query Answering
• 3.7.2 Introspection
• 3.7.3 Reusing Human Attention
• 3.8 Dataspace Research Challenges
• 3.9 Summary
• Chapter 4: Fundamentals of Real-time Linked Dataspaces
• 4.1 Introduction
• 4.2 Event and Stream Processing for the Internet of Things
• 4.2.1 Timeliness and Real-time Processing
• 4.3 Fundamentals of Real-time Linked Dataspaces
• 4.3.1 Foundations
• 4.3.2 Definition and Principles
• 4.3.3 Comparison
• 4.3.4 Architecture
• 4.4 A Principled Approach to Pay-As-You-Go Data Management
• 4.4.1 TBLś 5 Star Data
• 4.4.2 5 Star Pay-As-You-Go Model for Dataspace Services
• 4.5 Support Platform
• 4.5.1 Data Services
• 4.5.2 Stream and Event Processing Services
• 4.6 Suitability as a Data Platform for Intelligent Systems Within IoT-Based Smart Environments
• 4.6.1 Common Data Platform Requirements
• 4.6.2 Related Work
• 4.7 Summary
• Part II: Data Support Services
• Chapter 5: Data Support Services for Real-time Linked Dataspaces
• 5.1 Introduction
• 5.2 Pay-As-You-Go Data Support Services for Real-time Linked Dataspaces
• 5.3 5 Star Pay-As-You-Go Levels for Data Services
• 5.4 Summary
• Chapter 6: Catalog and Entity Management Service for Internet of Things-Based Smart Environments
• 6.1 Introduction
• 6.2 Working with Entity Data
• 6.3 Catalog and Entity Service Requirements for Real-time Linked Dataspaces
• 6.3.1 Real-time Linked Dataspaces
• 6.3.2 Requirements
• 6.4 Analysis of Existing Data Catalogs
• 6.5 Catalog Service
• 6.5.1 Pay-As-You-Go Service Levels.
• 6.6 Entity Management Service
• 6.6.1 Pay-As-You-Go Service Levels
• 6.6.2 Entity Example
• 6.7 Access Control Service
• 6.7.1 Pay-As-You-Go Service Levels
• 6.8 Joining the Real-time Linked Dataspace
• 6.9 Summary
• Chapter 7: Querying and Searching Heterogeneous Knowledge Graphs in Real-time Linked Dataspaces
• 7.1 Introduction
• 7.2 Querying and Searching in Real-time Linked Dataspaces
• 7.2.1 Real-time Linked Dataspaces
• 7.2.2 Knowledge Graphs
• 7.2.3 Searching Versus Querying
• 7.2.4 Search and Query Service Pay-As-You-Go Service Levels
• 7.3 Search and Query over Heterogeneous Data
• 7.3.1 Data Heterogeneity
• 7.3.2 Motivational Scenario
• 7.3.3 Core Requirements for Search and Query
• 7.4 State-of-the-Art Analysis
• 7.4.1 Information Retrieval Approaches
• 7.4.2 Natural Language Approaches
• 7.4.3 Discussion
• 7.5 Design Features for Schema-Agnostic Queries
• 7.6 Summary
• Chapter 8: Enhancing the Discovery of Internet of Things-Based Data Services in Real-time Linked Dataspaces
• 8.1 Introduction
• 8.2 Discovery of Data Services in Real-time Linked Dataspaces
• 8.2.1 Real-time Linked Dataspaces
• 8.2.2 Data Service Discovery
• 8.3 Semantic Approaches for Service Discovery
• 8.3.1 Inheritance Between OWL-S Services
• 8.3.2 Topic Extraction and Formal Concept Analysis
• 8.3.3 Reasoning-Based Matching
• 8.3.4 Numerical Encoding of Ontological Concepts
• 8.3.5 Discussion
• 8.4 Formal Concept Analysis for Organizing IoT Data Service Descriptions
• 8.4.1 Definition: Formal Context
• 8.4.2 Definition: Formal Concept
• 8.4.3 Definition: Sub-concept Ordering
• 8.5 IoT-Enabled Smart Environment Use Case
• 8.6 Conclusions and Future Work
• Chapter 9: Human-in-the-Loop Tasks for Data Management, Citizen Sensing, and Actuation in Smart Environments
• 9.1 Introduction
• 9.2 The Wisdom of the Crowds.
• 9.2.1 Crowdsourcing Platform
• 9.3 Challenges of Enabling Crowdsourcing
• 9.4 Approaches to Human-in-the-Loop
• 9.4.1 Augmented Algorithms and Operators
• 9.4.2 Declarative Programming
• 9.4.3 Generalised Stand-alone Platforms
• 9.5 Comparison of Existing Approaches
• 9.6 Human Task Service for Real-time Linked Dataspaces
• 9.6.1 Real-time Linked Dataspaces
• 9.6.2 Human Task Service
• 9.6.3 Pay-As-You-Go Service Levels
• 9.6.4 Applications of Human Task Service
• 9.6.5 Data Processing Pipeline
• 9.6.6 Task Data Model for Micro-tasks and Users
• 9.6.7 Spatial Task Assignment in Smart Environments
• 9.7 Summary
• Part III: Stream and Event Processing Services
• Chapter 10: Stream and Event Processing Services for Real-time Linked Dataspaces
• 10.1 Introduction
• 10.2 Pay-As-You-Go Services for Event and Stream Processing in Real-time Linked Dataspaces
• 10.3 Entity-Centric Real-time Query Service
• 10.3.1 Lambda Architecture
• 10.3.2 Entity-Centric Real-time Query Service
• 10.3.3 Pay-As-You-Go Service Levels
• 10.3.4 Service Performance
• 10.4 Summary
• Chapter 11: Quality of Service-Aware Complex Event Service Composition in Real-time Linked Dataspaces
• 11.1 Introduction
• 11.2 Complex Event Processing in Real-time Linked Dataspaces
• 11.2.1 Real-time Linked Dataspaces
• 11.2.2 Complex Event Processing
• 11.2.3 CEP Service Design
• 11.2.4 Pay-As-You-Go Service Levels
• 11.2.5 Event Service Life Cycle
• 11.3 QoS Model and Aggregation Schema
• 11.3.1 QoS Properties of Event Services
• 11.3.2 QoS Aggregation and Utility Function
• 11.3.3 Event QoS Utility Function
• 11.4 Genetic Algorithm for QoS-Aware Event Service Composition Optimisation
• 11.4.1 Population Initialisation
• 11.4.2 Genetic Encodings for Concrete Composition Plans
• 11.4.3 Crossover and Mutation Operations
• 11.4.3.1 Crossover.
• 11.4.3.2 Mutation and Elitism
• 11.5 Evaluation
• 11.5.1 Part 1: Performance of the Genetic Algorithm
• 11.5.1.1 Datasets
• 11.5.1.2 QoS Utility Results and Scalability
• 11.5.1.3 Fine-Tuning the Parameters
• 11.5.2 Part 2: Validation of QoS Aggregation Rules
• 11.5.2.1 Datasets and Experiment Settings
• 11.5.2.2 Simulation Results
• 11.6 Related Work
• 11.6.1 QoS-Aware Service Composition
• 11.6.2 On-Demand Event/Stream Processing
• 11.7 Summary and Future Work
• Chapter 12: Dissemination of Internet of Things Streams in a Real-time Linked Dataspace
• 12.1 Introduction
• 12.2 Internet of Things: A Dataspace Perspective
• 12.2.1 Real-time Linked Dataspaces
• 12.3 Stream Dissemination Service
• 12.3.1 Pay-As-You-Go Service Levels
• 12.4 Point-to-Point Linked Data Stream Dissemination
• 12.4.1 TP-Automata for Pattern Matching
• 12.5 Linked Data Stream Dissemination via Wireless Broadcast
• 12.5.1 The Mapping Between Triples and 3D Points
• 12.5.2 3D Hilbert Curve Index
• 12.6 Experimental Evaluation
• 12.6.1 Evaluation of Point-to-Point Linked Stream Dissemination
• 12.6.2 Evaluation on Linked Stream Dissemination via Wireless Broadcast
• 12.7 Related Work
• 12.7.1 Matching
• 12.7.2 Wireless Broadcast
• 12.8 Summary and Future Work
• Chapter 13: Approximate Semantic Event Processing in Real-time Linked Dataspaces
• 13.1 Introduction
• 13.2 Approximate Event Matching in Real-time Linked Dataspaces
• 13.2.1 Real-time Linked Dataspaces
• 13.2.2 Event Processing
• 13.3 The Approximate Semantic Matching Service
• 13.3.1 Pay-As-You-Go Service Levels
• 13.3.2 Semantic Matching Models
• 13.3.3 Model I: The Approximate Event Matching Model
• 13.3.4 Model II: The Thematic Event Matching Model
• 13.4 Elements for Approximate Semantic Matching of Events
• 13.4.1 Elm 1: Sub-symbolic Distributional Event Semantics.
• 13.4.2 Elm 2: Free Event Tagging.