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Umfang: 1 Online-Ressource (xxiii, 325 Seiten) , Illustrationen

ISBN: 9783030296650

Weitere Ausg.: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-29664-3

Weitere Ausg.: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-29666-7

Weitere Ausg.: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-29667-4

Sprache: Englisch

Fachgebiete: Informatik

Schlagwort(e): Datenbankverwaltung ; Linked Data

DOI: 10.1007/978-3-030-29665-0

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URL: Volltext  (kostenfrei)

UID:

Umfang: 1 online resource (333 pages)

Ausgabe: 1st ed.

ISBN: 9783030296650

Anmerkung: 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.

Weitere Ausg.: Print version: Curry, Edward Real-Time Linked Dataspaces Cham : Springer International Publishing AG,c2019 ISBN 9783030296643

Sprache: Englisch

Schlagwort(e): Electronic books.

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UID:

Umfang: XXIII, 325 p. 111 illus., 30 illus. in color. , online resource.

Ausgabe: 1st ed. 2020.

ISBN: 9783030296650

Inhalt: 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.

Anmerkung: 1 Real-time Linked Dataspaces: A Data Platform for Intelligent Systems within Internet of Things-based Smart Environments -- 2 Enabling Knowledge Flows in an Intelligent Systems Data Ecosystem -- 3 Dataspaces: Fundamentals, Principles, and Techniques -- 4 Fundamentals of Real-time Linked Dataspaces -- 5 Data Support Services for Real-time Linked Dataspaces -- 6 Catalog and Entity Management Service for Internet of Things-based Smart Environments -- 7 Querying and Searching Heterogeneous Knowledge Graphs in Real-time Linked Dataspaces -- 8 Enhancing the Discovery of Internet of Things-based Data Services in Real-time Linked Dataspaces -- 9 Human-in-the-Loop Tasks for Data Management, Citizen Sensing, and Actuation in Smart Environments -- 10 Stream and Event Processing Services for Real-time Linked Dataspaces -- 11 Quality of Service-Aware Complex Event Service Composition in Real-time Linked Dataspaces -- 12 Dissemination of Internet of Things Streams in a Real-time Linked Dataspace -- 13 Approximate Semantic Event Processing in Real-time Linked Dataspaces -- 14 Enabling Intelligent Systems, Applications, and Analytics for Smart Environments using Real-time Linked Dataspaces -- 15 Autonomic Source Selection for Real-time Predictive Analytics using the Internet of Things and Open Data -- 16 Building Internet of Things-enabled Digital Twins and Intelligent Applications using a Real-time Linked Dataspace -- 17 A Model for Internet of Things Enhanced User Experience in Smart Environments -- 18 Future Research Directions for Dataspaces, Data Ecosystems, and Intelligent Systems.

In: Springer Nature eBook

Weitere Ausg.: Printed edition: ISBN 9783030296643

Weitere Ausg.: Printed edition: ISBN 9783030296667

Weitere Ausg.: Printed edition: ISBN 9783030296674

Sprache: Englisch

DOI: 10.1007/978-3-030-29665-0

URL: https://doi.org/10.1007/978-3-030-29665-0

UID:

Umfang: 1 online resource (333 pages)

Ausgabe: 1st ed.

ISBN: 9783030296650

Anmerkung: 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

Weitere Ausg.: Print version Curry, Edward Real-Time Linked Dataspaces Cham : Springer International Publishing AG,c2019 ISBN 9783030296643

Sprache: Englisch

Schlagwort(e): Electronic books

URL: Full-text  ((OIS Credentials Required))

UID:

Umfang: 1 online resource

ISBN: 9783030296650 , 3030296652 , 9783030296643 , 3030296644

Serie: Online access: OAPEN DOAB Directory of Open Access Books.

Inhalt: 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.

Anmerkung: 1 Real-time Linked Dataspaces: A Data Platform for Intelligent Systems within Internet of Things-based Smart Environments -- 2 Enabling Knowledge Flows in an Intelligent Systems Data Ecosystem -- 3 Dataspaces: Fundamentals, Principles, and Techniques -- 4 Fundamentals of Real-time Linked Dataspaces -- 5 Data Support Services for Real-time Linked Dataspaces -- 6 Catalog and Entity Management Service for Internet of Things-based Smart Environments -- 7 Querying and Searching Heterogeneous Knowledge Graphs in Real-time Linked Dataspaces -- 8 Enhancing the Discovery of Internet of Things-based Data Services in Real-time Linked Dataspaces -- 9 Human-in-the-Loop Tasks for Data Management, Citizen Sensing, and Actuation in Smart Environments -- 10 Stream and Event Processing Services for Real-time Linked Dataspaces -- 11 Quality of Service-Aware Complex Event Service Composition in Real-time Linked Dataspaces -- 12 Dissemination of Internet of Things Streams in a Real-time Linked Dataspace -- 13 Approximate Semantic Event Processing in Real-time Linked Dataspaces -- 14 Enabling Intelligent Systems, Applications, and Analytics for Smart Environments using Real-time Linked Dataspaces -- 15 Autonomic Source Selection for Real-time Predictive Analytics using the Internet of Things and Open Data -- 16 Building Internet of Things-enabled Digital Twins and Intelligent Applications using a Real-time Linked Dataspace -- 17 A Model for Internet of Things Enhanced User Experience in Smart Environments -- 18 Future Research Directions for Dataspaces, Data Ecosystems, and Intelligent Systems.

In: OAPEN (Open Access Publishing in European Networks)., OAPEN

Weitere Ausg.: Print version: Curry, Edward. Real-Time Linked Dataspaces : Enabling Data Ecosystems for Intelligent Systems. Cham : Springer, ©2019 ISBN 9783030296643

Sprache: Englisch

URL: ProQuest Ebook Central

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UID:

Umfang: 1 online resource (XXIII, 325 p. 111 illus., 30 illus. in color.)

Ausgabe: 1st ed. 2020.

ISBN: 3-030-29665-2

Inhalt: 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.

Anmerkung: 1 Real-time Linked Dataspaces: A Data Platform for Intelligent Systems within Internet of Things-based Smart Environments -- 2 Enabling Knowledge Flows in an Intelligent Systems Data Ecosystem -- 3 Dataspaces: Fundamentals, Principles, and Techniques -- 4 Fundamentals of Real-time Linked Dataspaces -- 5 Data Support Services for Real-time Linked Dataspaces -- 6 Catalog and Entity Management Service for Internet of Things-based Smart Environments -- 7 Querying and Searching Heterogeneous Knowledge Graphs in Real-time Linked Dataspaces -- 8 Enhancing the Discovery of Internet of Things-based Data Services in Real-time Linked Dataspaces -- 9 Human-in-the-Loop Tasks for Data Management, Citizen Sensing, and Actuation in Smart Environments -- 10 Stream and Event Processing Services for Real-time Linked Dataspaces -- 11 Quality of Service-Aware Complex Event Service Composition in Real-time Linked Dataspaces -- 12 Dissemination of Internet of Things Streams in a Real-time Linked Dataspace -- 13 Approximate Semantic Event Processing in Real-time Linked Dataspaces -- 14 Enabling Intelligent Systems, Applications, and Analytics for Smart Environments using Real-time Linked Dataspaces -- 15 Autonomic Source Selection for Real-time Predictive Analytics using the Internet of Things and Open Data -- 16 Building Internet of Things-enabled Digital Twins and Intelligent Applications using a Real-time Linked Dataspace -- 17 A Model for Internet of Things Enhanced User Experience in Smart Environments -- 18 Future Research Directions for Dataspaces, Data Ecosystems, and Intelligent Systems. , English

Weitere Ausg.: ISBN 3-030-29664-4

Sprache: Englisch

DOI: 10.1007/978-3-030-29665-0

UID:

Umfang: 1 online resource (XXIII, 325 p. 111 illus., 30 illus. in color.)

Ausgabe: 1st ed. 2020.

ISBN: 3-030-29665-2

Inhalt: 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.

Anmerkung: 1 Real-time Linked Dataspaces: A Data Platform for Intelligent Systems within Internet of Things-based Smart Environments -- 2 Enabling Knowledge Flows in an Intelligent Systems Data Ecosystem -- 3 Dataspaces: Fundamentals, Principles, and Techniques -- 4 Fundamentals of Real-time Linked Dataspaces -- 5 Data Support Services for Real-time Linked Dataspaces -- 6 Catalog and Entity Management Service for Internet of Things-based Smart Environments -- 7 Querying and Searching Heterogeneous Knowledge Graphs in Real-time Linked Dataspaces -- 8 Enhancing the Discovery of Internet of Things-based Data Services in Real-time Linked Dataspaces -- 9 Human-in-the-Loop Tasks for Data Management, Citizen Sensing, and Actuation in Smart Environments -- 10 Stream and Event Processing Services for Real-time Linked Dataspaces -- 11 Quality of Service-Aware Complex Event Service Composition in Real-time Linked Dataspaces -- 12 Dissemination of Internet of Things Streams in a Real-time Linked Dataspace -- 13 Approximate Semantic Event Processing in Real-time Linked Dataspaces -- 14 Enabling Intelligent Systems, Applications, and Analytics for Smart Environments using Real-time Linked Dataspaces -- 15 Autonomic Source Selection for Real-time Predictive Analytics using the Internet of Things and Open Data -- 16 Building Internet of Things-enabled Digital Twins and Intelligent Applications using a Real-time Linked Dataspace -- 17 A Model for Internet of Things Enhanced User Experience in Smart Environments -- 18 Future Research Directions for Dataspaces, Data Ecosystems, and Intelligent Systems. , English

Weitere Ausg.: ISBN 3-030-29664-4

Sprache: Englisch

DOI: 10.1007/978-3-030-29665-0

UID:

Umfang: 1 online resource (XXIII, 325 p. 111 illus., 30 illus. in color.)

Ausgabe: 1st ed. 2020.

ISBN: 3-030-29665-2

Inhalt: 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.

Anmerkung: 1 Real-time Linked Dataspaces: A Data Platform for Intelligent Systems within Internet of Things-based Smart Environments -- 2 Enabling Knowledge Flows in an Intelligent Systems Data Ecosystem -- 3 Dataspaces: Fundamentals, Principles, and Techniques -- 4 Fundamentals of Real-time Linked Dataspaces -- 5 Data Support Services for Real-time Linked Dataspaces -- 6 Catalog and Entity Management Service for Internet of Things-based Smart Environments -- 7 Querying and Searching Heterogeneous Knowledge Graphs in Real-time Linked Dataspaces -- 8 Enhancing the Discovery of Internet of Things-based Data Services in Real-time Linked Dataspaces -- 9 Human-in-the-Loop Tasks for Data Management, Citizen Sensing, and Actuation in Smart Environments -- 10 Stream and Event Processing Services for Real-time Linked Dataspaces -- 11 Quality of Service-Aware Complex Event Service Composition in Real-time Linked Dataspaces -- 12 Dissemination of Internet of Things Streams in a Real-time Linked Dataspace -- 13 Approximate Semantic Event Processing in Real-time Linked Dataspaces -- 14 Enabling Intelligent Systems, Applications, and Analytics for Smart Environments using Real-time Linked Dataspaces -- 15 Autonomic Source Selection for Real-time Predictive Analytics using the Internet of Things and Open Data -- 16 Building Internet of Things-enabled Digital Twins and Intelligent Applications using a Real-time Linked Dataspace -- 17 A Model for Internet of Things Enhanced User Experience in Smart Environments -- 18 Future Research Directions for Dataspaces, Data Ecosystems, and Intelligent Systems. , English

Weitere Ausg.: ISBN 3-030-29664-4

Sprache: Englisch

DOI: 10.1007/978-3-030-29665-0

UID:

Umfang: 1 Online-Ressource (xxiii, 325 Seiten) : , Illustrationen.

ISBN: 978-3-030-29665-0

Weitere Ausg.: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-29664-3

Weitere Ausg.: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-29666-7

Weitere Ausg.: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-29667-4

Sprache: Englisch

Fachgebiete: Informatik

Schlagwort(e): Datenbankverwaltung ; Linked Data

DOI: 10.1007/978-3-030-29665-0

URL: Volltext  (kostenfrei)

URL: Volltext  (kostenfrei)

UID:

Umfang: 1 Online-Ressource (xxiii, 325 Seiten) : , Illustrationen.

ISBN: 978-3-030-29665-0

Weitere Ausg.: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-29664-3

Weitere Ausg.: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-29666-7

Weitere Ausg.: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-29667-4

Sprache: Englisch

Fachgebiete: Informatik

Schlagwort(e): Datenbankverwaltung ; Linked Data

DOI: 10.1007/978-3-030-29665-0

URL: Volltext  (kostenfrei)

URL: Volltext  (kostenfrei)