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  • 1
    UID:
    almahu_9949846583002882
    Format: 1 online resource (516 pages)
    Edition: 1st ed.
    ISBN: 9783031464522
    Additional Edition: Print version: Soldatos, John Artificial Intelligence in Manufacturing Cham : Springer International Publishing AG,c2024 ISBN 9783031464515
    Language: English
    Keywords: Electronic books. ; Electronic books.
    URL: Volltext  (kostenfrei)
    URL: Volltext  (kostenfrei)
    Library Location Call Number Volume/Issue/Year Availability
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  • 2
    UID:
    almahu_9949319961302882
    Format: 1 online resource (371 pages)
    ISBN: 9783030945909
    Note: Intro -- Preface -- Acknowledgments -- Contents -- Editors and Contributors -- About the Editors -- Contributors -- Abbreviations -- Part I Big Data and AI Technologies for Digital Finance -- 1 A Reference Architecture Model for Big Data Systems in the Finance Sector -- 1 Introduction -- 1.1 Background -- 1.2 Big Data Challenges in Digital Finance -- 1.2.1 Siloed Data and Data Fragmentation -- 1.2.2 Real-Time Computing -- 1.2.3 Mobility -- 1.2.4 Omni-channel Banking: Multiple Channel Management -- 1.2.5 Orchestration and Automation: Toward MLOps and AIOps -- 1.2.6 Transparency and Trustworthiness -- 1.3 Merits of a Reference Architecture (RA) -- 1.4 Chapter Structure -- 2 Related Work: Architectures for Systems in Banking and Digital Finance -- 2.1 IT Vendors' Reference Architectures -- 2.2 Reference Architecture for Standardization Organizations and Industrial Associations -- 2.3 Reference Architectures of EU Projects and Research Initiatives -- 2.4 Architectures for Data Pipelining -- 2.5 Discussion -- 3 The INFINITECH Reference Architecture (INFINITECH-RA) -- 3.1 Driving Principles: INFINITECH-RA Overview -- 3.2 The INFINITECH-RA -- 3.2.1 Logical View of the INFINITECH-RA -- 3.2.2 Development Considerations -- 3.2.3 Deployment Considerations -- 4 Sample Pipelines Based on the INFINITECH-RA -- 4.1 Simple Machine Learning Pipeline -- 4.2 Blockchain Data-Sharing and Analytics -- 4.3 Using the INFINITECH-RA for Pipeline Development and Specification -- 5 Conclusions -- References -- 2 Simplifying and Accelerating Data Pipelines in Digital Finance and Insurance Applications -- 1 Introduction -- 2 Challenges in Data Pipelines in Digital Finance and Insurance -- 2.1 IT Cost Savings -- 2.2 Productivity Improvements -- 2.3 Reduced Regulatory and Operational Risks -- 2.4 Delivery of New Capabilities and Services. , 3 Regular Data Pipeline Steps in Digital Finance and Insurance -- 3.1 Data Intaking -- 3.2 Data Transformation -- 3.3 Generate the Required Output -- 4 How LeanXcale Simplifies and Accelerates Data Pipelines -- 4.1 High Insertion Rates -- 4.2 Bidimensional Partitioning -- 4.3 Online Aggregates -- 4.4 Scalability -- 5 Exploring New Use Cases: The INFINITECH Approach to Data Pipelines -- 6 Conclusion -- References -- 3 Architectural Patterns for Data Pipelines in Digital Finance and Insurance Applications -- 1 Introduction -- 1.1 Motivation -- 1.2 Data Pipelining Architectural Pattern Catalogue and How LeanXcale Simplifies All of Them -- 2 A Taxonomy of Databases for Data Pipelining -- 2.1 Database Taxonomy -- 2.1.1 Operational Databases -- 2.1.2 Data Warehouses -- 2.1.3 Data Lakes -- 2.2 Operational Database Taxonomy -- 2.2.1 Traditional SQL Databases -- 2.2.2 NoSQL Databases -- 2.2.3 NewSQL Databases -- 2.3 NoSQL Database Taxonomy -- 2.3.1 Key-Value Data Stores -- 2.3.2 Document-Oriented Databases -- 2.3.3 Graph Databases -- 2.3.4 Wide-Column Data Stores -- 3 Architectural Patterns Dealing with Current and Historical Data -- 3.1 Lambda Architecture -- 3.2 Beyond Lambda Architecture -- 3.3 Current Historical Data Splitting -- 3.4 From Current Historical Data Splitting to Real-Time Data Warehousing -- 4 Architectural Patterns for Off-Loading Critical Databases -- 4.1 Data Warehouse Off-Loading -- 4.2 Simplifying Data Warehouse Off-Loading -- 4.3 Operational Database Off-Loading -- 4.4 Operational Database Off-Loading at Any Scale -- 4.5 Database Snapshotting -- 4.6 Accelerating Database Snapshotting -- 5 Architectural Patterns Dealing with Aggregations -- 5.1 In-Memory Application Aggregation -- 5.2 From In-Memory Application Aggregation to Online Aggregation -- 5.3 Detail-Aggregate View Splitting -- 5.4 Avoiding Detail-Aggregate View Splitting. , 6 Architectural Patterns Dealing with Scalability -- 6.1 Database Sharding -- 6.2 Removing Database Sharding -- 7 Data Pipelining in INFINITECH -- 8 Conclusions -- 4 Semantic Interoperability Framework for Digital Finance Applications -- 1 Introduction -- 2 Background: Relevant Concepts and Definitions for the INFINITECH Semantic Interoperability Framework -- 2.1 Interoperability -- 2.1.1 Semantic Interoperability -- 2.1.2 Semantic Models -- 2.1.3 Ontologies -- 2.1.4 Semantic Annotations -- 2.2 Methodologies for Ontology Engineering -- 2.2.1 METHONTOLOGY -- 2.2.2 SAMOD -- 2.2.3 DILIGENT -- 2.2.4 UPON Lite -- 3 INFINITECH Semantic Interoperability Framework -- 3.1 Methodology for Semantic Models, Ontology Engineering, and Prototyping -- 3.1.1 Modeling Method -- 3.1.2 Envisioned Roles and Functions in Semantic Models, Ontology Engineering, and Prototyping -- 4 Applying the Methodology: Connecting the Dots -- 4.1 Workflow and Technological Tools for Validation of the Methodology -- 4.2 Collecting -- 4.3 Building and Merging -- 4.4 Refactoring and Linking -- 4.4.1 Data Ingestion -- 4.4.2 Semantic Alignment: Building and Merging -- 4.4.3 Semantic Transformation: Generating a Queryable Knowledge Graphs -- 4.4.4 Data-Sharing/Provisioning -- 5 Conclusions -- References -- Part II Blockchain Technologies and Digital Currencies for Digital Finance -- 5 Towards Optimal Technological Solutions for Central Bank Digital Currencies -- 1 Understanding CBDCs -- 1.1 A Brief History of Definitions -- 1.2 How CBDCs Differ from Other Forms of Money -- 1.3 Wholesale and Retail CBDCs -- 1.4 Motivations of CBDCs -- 1.4.1 Financial Stability and Monetary Policy -- 1.4.2 Increased Competition in Payments and Threats to Financial Sovereignty -- 2 From Motivations to Design Options -- 2.1 The Design Space of CBDCs -- 2.2 Assessing Design Space Against Desirable Characteristics. , 2.2.1 Instrument Features -- 2.2.2 System Features -- References -- 6 Historic Overview and Future Outlook of Blockchain Interoperability -- 1 Multidimensional Mutually Exclusive Choices as the Source of Blockchain Limitations -- 2 First Attempts at Interoperability -- 2.1 Anchoring -- 2.2 Pegged Sidechains -- 2.3 Cross-Chain Atomic Swaps -- 2.4 Solution Design -- 3 Later Attempts at Interoperability -- 3.1 Polkadot -- 3.2 Cosmos -- 3.3 Interledger -- 3.4 Idealistic Solution Design -- References -- 7 Efficient and Accelerated KYC Using Blockchain Technologies -- 1 Introduction -- 2 Architecture -- 3 Use Case Scenarios -- 4 Sequence Diagrams -- 5 Implementation Solution -- 6 Conclusions and Future Works -- References -- 8 Leveraging Management of Customers' Consent Exploiting the Benefits of Blockchain Technology Towards SecureData Sharing -- 1 Introduction -- 2 Consent Management for Financial Services -- 3 Related Work -- 4 Methodology -- 4.1 User's Registration -- 4.2 Customer Receives a Request to Provide New Consent for Sharing His/Her Customer Data -- 4.3 Definition of the Consent -- 4.4 Signing of the Consent by the Interested Parties -- 4.5 Consent Form Is Stored in the Consent Management System -- 4.6 Consent Update or Withdrawal -- 4.7 Expiration of the Validity Period -- 4.8 Access Control Based on the Consent Forms -- 4.9 Retrieve Complete History of Consents -- 5 The INFINITECH Consent Management System -- 5.1 Implemented Methods -- 5.1.1 Definition of Consent -- 5.1.2 Consent Update or Withdrawal -- 5.1.3 Consent Expiration -- 5.1.4 Access Control -- 5.1.5 Complete History of Consents -- 6 Conclusions -- References -- Part III Applications of Big Data and AI in Digital Finance -- 9 Addressing Risk Assessments in Real-Time for Forex Trading -- 1 Introduction -- 2 Portfolio Risk -- 3 Risk Models -- 3.1 Value at Risk. , 3.2 Expected Shortfall -- 4 Real-Time Management -- 5 Pre-trade Analysis -- 6 Architecture -- 7 Summary -- References -- 10 Next-Generation Personalized Investment Recommendations -- 1 Introduction to Investment Recommendation -- 2 Understanding the Regulatory Environment -- 3 Formalizing Financial Asset Recommendation -- 4 Data Preparation and Curation -- 4.1 Why Is Data Quality Important? -- 4.2 Data Preparation Principles -- 4.3 The INFINITECH Way Towards Data Preparation -- 5 Approaches to Investment Recommendation -- 5.1 Collaborative Filtering Recommenders -- 5.2 User Similarity Models -- 5.3 Key Performance Indicator Predictors -- 5.4 Hybrid Recommenders -- 5.5 Knowledge-Based Recommenders -- 5.6 Association Rule Mining -- 6 Investment Recommendation within INFINITECH -- 6.1 Experimental Setup -- 6.2 Investment Recommendation Suitability -- 7 Summary and Recommendations -- References -- 11 Personalized Portfolio Optimization Using Genetic(AI) Algorithms -- 1 Introduction to Robo-Advisory and Algorithm-Based Asset Management for the General Public -- 2 Traditional Portfolio Optimization Methods -- 2.1 The Modern Portfolio Theory -- 2.2 Value at Risk (VaR) -- 3 Portfolio Optimization Based on Genetic Algorithms -- 3.1 The Concept of Evolutionary Theory -- 3.2 Artificial Replication Using Genetic Algorithms -- 3.3 Genetic Algorithms for Portfolio Optimization -- 3.3.1 Multiple Input Parameters -- 3.3.2 Data Requirements -- 3.3.3 A Novel and Flexible Optimization Approach Based on Genetic Algorithms -- 3.3.4 Fitness Factors and Fitness Score -- 3.3.5 Phases of the Optimization Process Utilizing Genetic Algorithms -- 3.3.6 Algorithm Verification -- 3.3.7 Sample Use Case "Sustainability" -- 4 Summary and Conclusions -- References -- 12 Personalized Finance Management for SMEs -- 1 Introduction -- 2 Conceptual Architecture of the Proposed Approach. , 3 Datasets Used and Data Enrichment.
    Additional Edition: Print version: Soldatos, John Big Data and Artificial Intelligence in Digital Finance Cham : Springer International Publishing AG,c2022 ISBN 9783030945893
    Language: English
    Keywords: Electronic books.
    URL: Volltext  (kostenfrei)
    URL: Volltext  (kostenfrei)
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  • 3
    Online Resource
    Online Resource
    Taylor & Francis | Alsbjergvej, Gistrup, Denmark ; : River Publishers,
    UID:
    almahu_9949378081602882
    Format: 1 online resource (294 pages) : , color illustrations, tables.
    Edition: 1st ed.
    ISBN: 1-00-333742-2 , 1-003-33742-2 , 1-000-79365-6 , 87-93519-04-4
    Series Statement: River Publishers Series in Signal, Image and Speech Processing
    Content: "The Building Blocks of IoT Analytics is devoted to the presentation the main technology building blocks that comprise advanced IoT analytics systems. It introduces IoT analytics as a special case of BigData analytics and accordingly presents leading edge technologies that can be deployed in order to successfully confront the main challenges of IoT analytics applications. Special emphasis is paid in the presentation of technologies for IoT streaming and semantic interoperability across diverse IoT streams. Furthermore, the role of cloud computing and BigData technologies in IoT analytics are presented, along with practical tools for implementing, deploying and operating non-trivial IoT applications. Along with the main building blocks of IoT analytics systems and applications, the book presents a series of practical applications, which illustrate the use of these technologies in the scope of pragmatic applications."--Provided by Publisher.
    Note: English
    Additional Edition: ISBN 87-93519-03-6
    Language: English
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  • 4
    UID:
    almahu_9949641798902882
    Format: 1 online resource (460 pages)
    ISBN: 9781032632407 , 1032632402 , 9781003812159 , 1003812155 , 100381218X , 9781003812180
    Series Statement: River Publishers series in communications and networking
    Content: This book presents the technologies that empower edge intelligence, along with their use in novel IoT solutions. Specifically, it presents how 5G/6G, Edge AI, and Blockchain solutions enable novel IoT-based decentralized intelligence use cases at the edge of the cloud/edge/IoT continuum. Emphasis is placed on presenting how these technologies support a wide array of functional and non-functional requirements spanning latency, performance, cybersecurity, data protection, real-time performance, energy efficiency, and more. The various chapters of the book are contributed by several EU-funded projects, which have recently developed novel IoT platforms that enable the development and deployment of edge intelligence applications based on the cloud/edge paradigm. Each one of the projects employs its own approach and uses a different mix of networking, middleware, and IoT technologies. Therefore, each of the chapters of the book contributes a unique perspective on the capabilities of enabling technologies and their integration in practical real-life applications in different sectors. The book is structured in five distinct parts. Each one of the first four parts focuses on a specific set of enabling technologies for edge intelligence and smart IoT applications in the cloud/edge/IoT continuum. Furthermore, the fifth part provides information about complementary aspects of next-generation IoT technology, including information about business models and IoT skills. Specifically: The first part focuses on 5G/6G networking technologies and their roles in implementing edge intelligence applications. The second part presents IoT applications that employ machine learning and other forms of Artificial Intelligence at the edge of the network. The third part illustrates decentralized IoT applications based on distributed ledger technologies. The fourth part is devoted to the presentation of novel IoT applications and use cases spanning the cloud/edge/IoT continuum. The fifth part discusses complementary aspects of IoT technologies, including business models and digital skills. The Open Access version of this book, available at http://www.taylorfrancis.com, has been made available under a Creative Commons [Attribution-Non-Commercial (CC-BY-NC)] 4.0 license.
    Additional Edition: Print version: Shaping the future of IoT with edge intelligence. Gistrup : River Publishers, 2023 ISBN 9788770040273
    Language: English
    Keywords: Electronic books.
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  • 5
    UID:
    almahu_9949684653202882
    Format: 1 online resource (XXVII, 505 p. 175 illus., 153 illus. in color.)
    Edition: 1st ed. 2024.
    ISBN: 3-031-46452-4
    Content: This open access book presents a rich set of innovative solutions for artificial intelligence (AI) in manufacturing. The various chapters of the book provide a broad coverage of AI systems for state of the art flexible production lines including both cyber-physical production systems (Industry 4.0) and emerging trustworthy and human-centered manufacturing systems (Industry 5.0). From a technology perspective, the book addresses a wide range of AI paradigms such as deep learning, reinforcement learning, active learning, agent-based systems, explainable AI, industrial robots, and AI-based digital twins. Emphasis is put on system architectures and technologies that foster human-AI collaboration based on trusted interactions between workers and AI systems. From a manufacturing applications perspective, the book illustrates the deployment of these AI paradigms in a variety of use cases spanning production planning, quality control, anomaly detection, metrology, workers’ training, supply chain management, as well as various production optimization scenarios. This is an open access book.
    Note: Introduction -- Part I Architectures and Knowledge Modelling for AI in Manufacturing -- Reference Architecture for AI-based Industry 5.0 Applications -- Designing a Marketplace to Exchange AI Models for Industry 4.0 -- Domain Ontology Enrichment through Human-AI Interaction -- Survey of Knowledge Graphs in Industrial Settings -- From Knowledge to Wisdom: Leveraging Semantic Representations via Knowledge Graph Embeddings -- Advancing high value-added networked production through Decentralized Technical Intelligence -- Part II AI-based Digital Twins for Manufacturing Applications -- Digital-Twin enabled framework for training and deploying AI agents for production scheduling -- Digital Twin for Human Machine Interaction -- Learning-based Collaborative Digital Twins -- A Manufacturing Digital Twin Framework -- Part III Agent based Approaches for AI in Manufacturing -- Reinforcement Learning based approaches in manufacturing environments -- A participatory modelling approach to Agents in Industry using AAS -- 4.0 Holonic Multi-Agent Testbed Enabling Shared Production -- Application of a Multi agent system on production and scheduling optimization -- Integrating Knowledge to Conversational Agents for Worker Upskilling -- Part IV Trusted AI for Industry 5.0 Applications -- Wearable sensor-based human activity recognition for worker safety in manufacturing line -- Object detection for human-robot interaction and worker assistance systems -- Application of autoML, XAI and differential privacy method into manufacturing -- Anomaly Detection in Manufacturing -- Towards Industry 5.0 by incorporation of Trustworthy and Human-Centric approaches -- How AI changes human roles in Industry 5.0-enabled environments: Human in the AI loop via xAI and Active Learning for Manufacturing Quality Control -- Multi-Stakeholder Perspective on Human-AI Collaboration in Industry 5.0 -- Conclusion.
    Additional Edition: ISBN 9783031464515
    Language: English
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  • 6
    Online Resource
    Online Resource
    Aalborg : River Publishers,
    UID:
    gbv_1870233042
    Format: 1 online resource (294 pages)
    ISBN: 8793519044 , 9788793519046 , 9781003337423 , 1003337422 , 9781000796810 , 1000796817 , 9781000793659 , 1000793656
    Series Statement: River Publishers Series in Signal, Image and Speech Processing Ser
    Content: Front Cover -- Half Title Page -- RIVER PUBLISHERS SERIES IN SIGNAL, IMAGE AND SPEECH PROCESSING -- Full Title Page -- Building Blocks for IoT AnalyticsI nternet-of-Things Analytics -- Copyright Page -- Contents -- Preface -- List of Contributors -- List of Figures -- List of Tables -- List of Abbreviations -- PART I IoT Analytics Enablers -- Chapter 1 -- Introducing IoT Analytics -- 1.1 Introduction -- 1.2 IoT Data and BigData -- 1.3 Challenges of IoT Analytics Applications -- 1.4 IoT Analytics Lifecycle and Techniques -- 1.5 Conclusions -- References -- Chapter 2 -- IoT, Cloud and BigData Integration for IoT Analytics -- 2.1 Introduction -- 2.2 Cloud-based IoT Platform -- 2.2.1 IaaS, PaaS and SaaS Paradigms -- 2.2.2 Requirements of IoT BigData Analytics Platform -- 2.2.3 Functional Architecture -- 2.3 Data Analytics for the IoT -- 2.3.1 Characteristics of IoT Generated Data -- 2.3.2 Data Analytic Techniques and Technologies -- 2.4 Data Collection Using Low-power, Long-range Radios -- 2.4.1 Architecture and Deployment -- 2.4.2 Low-cost LoRa Implementation -- 2.5 WAZIUP Software Platform -- 2.5.1 Main Challenges -- 2.5.2 PaaS for IoT -- 2.5.3 Architecture -- 2.5.4 Deployment -- 2.6 iKaaS Software Platform -- 2.6.1 Service Orchestration and Resources Provisioning -- 2.6.2 Advanced Data Processing and Analytics -- 2.6.3 Service Composition and Decomposition -- 2.6.4 Migration and Portability in Multi-cloud Environment -- 2.6.5 Cost Function of Service Migration -- 2.6.6 Dynamic Selection of Devices in Multi-cloud Environment -- Acknowledgement -- References -- Chapter 3 -- Searching the Internet of Things -- 3.1 Introduction -- 3.2 A Search Architecture for Social and Physical Sensors -- 3.2.1 Search engine for MultimediA enviRonment generated contenT (SMART) -- 3.2.2 Challenges in Building an IoT Search Engine -- 3.3 Local Event Retrieval.
    Additional Edition: ISBN 9788793519039
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 9788793519039
    Language: English
    Keywords: Conference papers and proceedings
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  • 7
    UID:
    almafu_9960727419902883
    Format: 1 online resource : $b illustrations (some color)
    ISBN: 3-030-94590-1
    Content: This open access book presents how cutting-edge digital technologies like Machine Learning, Artificial Intelligence (AI), and Blockchain are set to disrupt the financial sector. The book illustrates how recent advances in these technologies facilitate banks, FinTechs, and financial institutions to collect, process, analyze, and fully leverage the very large amounts of data that are nowadays produced and exchanged in the sector. To this end, the book also introduces some of the most popular Big Data, AI and Blockchain applications in the sector, including novel applications in the areas of Know Your Customer (KYC), Personalized Wealth Management and Asset Management, Portfolio Risk Assessment, as well as variety of novel Usage-based Insurance applications based on Internet-of-Things data. Most of the presented applications have been developed, deployed and validated in real-life digital finance settings in the context of the European Commission funded INFINITECH project, which is a flagship innovation initiative for Big Data and AI in digital finance.
    Note: Description based upon print version of record. , Introduction -- Part I: Novel Big Data and AI Technologies for Digital Finance -- A Reference Architecture for Big Data Systems and AI Applications in the Finance Sector -- Hybrid Transactional and Analytical Processing for Integrated Data Management in Digital Finance and FinTech Applications -- Semantic Interoperability Modelling for Digital Finance Applications -- Semantic Streaming for Digital Finance Applications -- Part II: Blockchain Technologies and Digital Currencies for Digital Finance Applications -- Central Bank Digital Currencies and a Euro for the Future -- Efficient and Accelerated KYC Using Blockchain Technologies -- Part III: Applications of Big Data and AI in Digital Finance -- AI-based platform for Intelligent and Automated Accounting -- Addressing Intra-Day Volatility in Risk Assessments for Forex Trading -- Next-Generation Personalized Investment Recommendations -- Personalized Finance Management for Small Medium Businesses -- AML/CF supervision empowered by AI -- Analyzing Large Scale Blockchain Transaction Graphs for Fraudulent Activities -- Cybersecurity and Fraud Detection in Financial Transactions -- Part IV: Applications of Big Data and AI in Insurance -- Risk assessment for personalized health insurance products -- Alternative Data for Personalized Insurance products -- Part V: Technologies for Regulatory Compliance in the Finance Sector -- AI Governance -Trustworthy and Responsible use of AI -- Large Scale Data Anonymization for GDPR Compliance -- Conclusion. , English
    Additional Edition: ISBN 3-030-94589-8
    Language: English
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  • 8
    UID:
    gbv_1794553363
    Format: 1 Online-Ressource (602 p.)
    ISBN: 9781680838237 , 9781680838220
    Series Statement: NowOpen
    Content: Modern critical infrastru ...
    Note: English
    Language: English
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  • 9
    UID:
    gbv_1794551611
    Format: 1 Online-Ressource (240 p.)
    ISBN: 9781680838770 , 9781680838763
    Series Statement: NowOpen
    Content: The successful deployment ...
    Note: English
    Language: English
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  • 10
    UID:
    gbv_1832231542
    Format: 1 Online-Ressource (494 p.)
    ISBN: 9781003339717 , 9781000795042 , 9788770220415
    Content: In today's competitive global environment, manufacturers are offered with unprecedented opportunities to build hyper-efficient and highly flexible plants, towards meeting variable market demand, while at the same time supporting new production models such as make-to-order (MTO), configure-to-order (CTO) and engineer-to-order (ETO). During the last couple of years, the digital transformation of industrial processes is propelled by the emergence and rise of the fourth industrial revolution (Industry4.0). The latter is based on the extensive deployment of Cyber-Physical Production Systems (CPPS) and Industrial Internet of Things (IIoT) technologies in the manufacturing shopfloor, as well as on the seamless and timely exchange of digital information across supply chain participants. The benefits of Industry 4.0 have been already proven in the scope of pilot and production deployments in a number of different use cases including flexibility in automation, predictive maintenance, zero defect manufacturing and more. Despite early implementations and proof-of-concepts, CPPS/IIoT deployments are still in their infancy for a number of reasons, including:• Manufacturers' poor awareness about digital manufacturing solutions and their business value potential, as well as the lack of relevant internal CPPS/IIoT knowledge.• The high costs that are associated with the deployment, maintenance and operation of CPPS systems in the manufacturing shopfloors, which are particularly challenging in the case of SME (Small Medium Enterprises) manufacturers that lack the equity capital needed to invest in Industry 4.0.• The time needed to implement CPPS/IIoT and the lack of a smooth and proven migration path from existing OT solutions.• The uncertainty over the business benefits and impacts of IIoT and CPPS technologies, including the lack of proven methods for the techno-economic evaluation of Industry4.0 systems. • Manufacturers' increased reliance on external integrators, consultants and vendors. • The absence of a well-developed value chain needed to sustain the acceptance of these new technologies for digital automation.In order to alleviate these challenges, three European Commission funded projects (namely H2020 FAR-EDGE (http://www.far-edge.eu/), H2020 DAEDALUS (http://daedalus.iec61499.eu) and H2020 AUTOWARE (http://www.autoware-eu.org/)) have recently joined forces towards a "Digital Shopfloor Alliance". The Alliance aims at providing leading edge and standards based digital automation solutions, along with guidelines and blueprints for their effective deployment, validation and evaluation. The present book provides a comprehensive description of some of the most representative solutions that offered by these three projects, along with the ways these solutions can be combined in order to achieve multiplier effects and maximize the benefits of their use. The presented solutions include standards-based digital automation solutions, following different deployment paradigms, such as cloud and edge computing systems. Moreover, they also comprise a rich set of digital simulation solutions, which are explored in conjunction with the H2020 MAYA project (http://www.maya-euproject.com/). The latter facilitate the testing and evaluation of what-if scenarios at low risk and cost, but also without disrupting shopfloor operations. As already outlined, beyond leading edge scientific and technological development solutions, the book comprises a rich set of complementary assets that are indispensable to the successful adoption of IIoT/CPPS in the shopfloor. The book is structured in three parts as follows: • The first part of the book is devoted to digital automation platforms. Following an introduction to Industry 4.0 in general and digital automation platforms in particular, this part presents the digital automation platforms of the FAR-EDGE, AUTOWARE and DAEDALUS projects. • The second part of the book focuses on the presentation of digital simulation and digital twins' functionalities. These include information about the models that underpin digital twins, as well as the simulators that enable experimentation with these processes over these digital models. • The third part of the book provides information about complementary assets and supporting services that boost the adoption of digital automation functionalities in the Industry4.0 era. Training services, migration services and ecosystem building services are discussed based on the results of the three projects of the Digital Shopfloor Alliance. The target audience of the book includes:• Researchers in the areas of Digital Manufacturing and more specifically in the areas of digital automation and simulation, who wish to be updated about latest Industry4.0 developments in these areas.• Manufacturers, with an interest in the next generation of digital automation solutions based on Cyber-Physical systems.• Practitioners and providers of Industrial IoT solutions, which are interested in the implementation of use cases in automation, simulation and supply chain management.• Managers wishing to understand technologies and solutions that underpin Industry4.0, along with representative applications in the shopfloor and across the supply chain
    Note: English
    Language: Undetermined
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