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.