Digital Transformation: Core Technologies and Emerging Topics from a Computer Science Perspective
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Berlin, Heidelberg
Springer Berlin / Heidelberg
2023
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| Ausgabe: | 1st ed |
| Schlagworte: | |
| Online-Zugang: | DE-2070s |
| Beschreibung: | Description based on publisher supplied metadata and other sources |
| Beschreibung: | 1 Online-Ressource (522 Seiten) |
| ISBN: | 9783662650042 |
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| 505 | 8 | |a Intro -- Preface to "Digital Transformation: Core Technologies and Emerging Topics from a Computer Science Perspective" -- Contents -- Digital Representation -- 1 Engineering Digital Twins and Digital Shadows as Key Enablers for Industry 4.0 -- 1 Introduction -- 1.1 Engineering Models in Industry 4.0 -- 1.2 Digital Shadows -- 1.3 Digital Twins -- 1.4 Outline -- 2 Challenges in Engineering a Digital Twin and Its Digital Shadows -- 2.1 Challenges in Engineering Digital Twins -- 2.2 Challenges in Operating Digital Twins -- 3 Engineering a Digital Twin and Its Digital Shadows -- 4 From Engineering Models to a Digital Twin -- 4.1 Semantic Data Extraction -- 4.2 Technologies for Connecting Digital Twins and Engineering Models -- 5 Digital Shadows and Data Processing -- 5.1 Data Lakes as a Serving Infrastructure -- 5.2 From Data Processing to Digital Shadows -- 5.3 Artificial Intelligence in Digital Shadows -- 6 The Future of Digital Twins and Digital Shadows -- 2 Designing Strongly-decoupled Industry 4.0 Applications Across the Stack: A Use Case -- 1 Introduction -- 2 Running Example: Factory in a Box -- 3 Architecture-centric Design -- 3.1 Architecture Modeling Elements -- 3.2 The C2myx Architectural Style for Strong Decoupling -- 3.3 Benefits of Using C2myx -- 3.4 Architecture Style Vs Modeling Language -- 3.5 Brief Related Work on Architectures in CPPS -- 4 Building Blocks -- 4.1 Capabilities -- 4.2 Grounding Capabilities in OPC UA -- 4.3 Production Process Modeling -- 5 Designing Behavior with Capabilities -- 5.1 Soft Real-Time Execution within Control Devices -- 5.2 Actor-based Non-time Critical Execution-PLC Level -- 5.3 Process-Based Execution -- 5.4 Scheduler Based Execution and Transport -- 5.5 Interoperability and Composition of Systems-of-Systems -- 6 Discussion and Conclusion -- 3 Variability in Products and Production -- 1 Introduction | |
| 505 | 8 | |a 2 Variability Challenges in Automation -- 2.1 Variability of Different Production Levels -- 2.2 Variability Binding Times -- 2.3 Re-Configurable Production -- 2.4 Verifying and Validating Variable Products and Production Systems -- 3 Injection Molding Machine Example -- 4 Variability Engineering -- 4.1 Variability Modeling -- 4.2 Variability Realization -- 4.3 Variability-Aware V& -- V -- 4.4 Variability Evolution -- 5 Product Line Adoption and Evolution -- 5.1 Extractive Adoption -- 5.2 Reactive Adoption -- 6 Research Challenges -- 7 Conclusions -- Digital Infrastructures -- 4 Reference Architectures for Closing the IT/OT Gap -- 1 Introduction -- 2 Architectural Reference Models -- 2.1 Reference Architecture, Views and Perspectives -- 2.2 Reference Models -- 3 Architectures Before IoT -- 3.1 Internet Protocol Suite and Open Systems Interconnection (OSI) Model -- 3.2 The Service-Oriented Architecture (SOA) -- 3.3 ISA-95: An Early Reference Architecture for Industry -- 4 Architectural Reference Models for IoT and IIoT -- 4.1 Requirements for an IIoT Architecture -- 4.2 Three ARMs for IIoT -- 4.3 Differences between the Three Architectural Reference Models -- 4.4 Connectivity: A Crosscutting Function in IIoT -- 5 Combining Information Technology with Operational Technology -- 5.1 Legacy Systems and Industrial Communication Technologies -- 5.2 Fog Computing -- 6 Applying ARM: An Industrial use Case -- 6.1 Legacy System -- 6.2 Objectives and Suitable Reference Architecture -- 6.3 Technical Implementation -- 6.4 Summary -- 7 Conclusion -- 5 Edge Computing: Use Cases and Research Challenges -- 1 Introduction -- 2 Edge Computing -- 3 Use Cases -- 3.1 Smart Manufacturing Scenario -- 4 Research Challenges -- 4.1 Resource Management -- 4.2 Network Management -- 4.3 Security and Privacy -- 5 Conclusion -- 6 Dynamic Access Control in Industry 4.0 Systems | |
| 505 | 8 | |a 1 Introduction -- 2 Running Example -- 3 Static Data Flow Analysis -- 4 Dynamic Access Control -- 4.1 Specification of the Running Example -- 4.2 Semantics -- 5 Application Scenarios -- 5.1 Overview of the Combined Approach -- 5.2 Palladio Design-Time Tooling -- 5.3 Runtime Decision Making -- 6 Related Work -- 7 Conclusion and Outlook -- 7 Challenges in OT Security and Their Impacts on Safety-Related Cyber-Physical Production Systems -- 1 Introduction -- 1.1 Production Network: The Automation Pyramid -- 1.2 Cyber-Physical Systems -- 1.3 Cyber-Physical Production System (CPPS) -- 1.4 Motivation -- 2 Vulnerable Assets of a CPPS -- 2.1 Tangible Assets -- 2.2 Intangible Assets -- 3 Threat Modeling and Attack Vectors -- 3.1 Complexity of CPPS Attacker Modeling -- 3.2 CPS-Specific Threat Modeling -- 3.3 Threats Against CPPS Assets -- 3.4 Attack Vectors -- 4 Measures Against Threats -- 4.1 Security Relevant Differences Between IT and OT Systems -- 4.2 IEC 62443 -- 4.3 NIST Special Publication 800-82 -- 4.4 IEC 61784 -- 5 Risk Management -- 6 Challenges of Integrating Safety and Security -- 6.1 Current Status and Objectives -- 6.2 Challenges Relevant for the Physical Layer -- 6.3 Software-Related Challenges -- 7 Conclusion -- 8 Runtime Monitoring for Systems of System -- 1 Introduction -- 2 Systems of Systems and Cyber-Physical Production Systems -- 3 Runtime Monitoring of Industry 4.0 Applications-The Two Perspectives -- 3.1 The Machine Vendor View -- 3.2 The Shop Floor Owner View -- 4 Potential Applications of Monitoring -- 4.1 Monitoring Safety Properties -- 4.2 Condition Monitoring -- 5 Requirements-Based Monitoring for Systems of Systems -- 5.1 Challenges for Monitoring Systems of Systems -- 5.2 A Requirements Monitoring Model -- 5.3 A Domain-Specific Language for SoS Constraint Checking -- 6 Conclusion | |
| 505 | 8 | |a 9 Blockchain Technologies in the Design and Operation of Cyber-Physical Systems -- 1 Introduction -- 2 Starting from the Beginning: What is a Blockchain? -- 2.1 Blockchain Basic Concepts -- 2.2 A Blockchain Under the Microscope -- 3 Manipulating Data in a Blockchain -- 3.1 Smart Contracts, Languages, and Turing Completeness -- 3.2 Smart Contracts in Turing-complete Languages: The Case of Ethereum -- 3.3 Example of a Smart Contract -- 3.4 Challenges in Contracts Lifecycle -- 4 Use Cases -- 4.1 Supply Chain in Maritime Trade -- 4.2 Collaborative Design of CPSs -- 5 Designing My Blockchain -- 5.1 Socio-Technical Challenges -- 5.2 Enterprise Blockchain Platforms -- 6 Conclusions -- Data Management -- Big Data Integration for Industry 4.0 -- 1 Introduction and Related Work -- 2 Data Integration Use Cases -- 3 Knowledge Graphs -- 3.1 Knowledge Graph Foundations -- 3.2 Knowledge Graph Construction -- 4 Entity Resolution -- 4.1 Blocking -- 4.2 Pair-wise Matching -- 4.3 Clustering -- 4.4 Incremental ER -- 4.5 ER Prototypes -- 5 Conclusion & -- Open Problems -- 11 Massive Data Sets - Is Data Quality Still an Issue? -- 1 Introduction -- 2 Outlier Identification -- 3 Robust Modeling -- 4 Variable Selection -- 5 Discussion and Summary -- Modelling the Top Floor: Internal and External Data Integration and Exchange -- 1 Introduction -- 2 Enterprise Resource Planning -- 3 Manufacturing Operations Management -- 4 Vertical Integration -- 4.1 Alignment of Complementary Conceptual Models -- 4.2 Application in the MyYoghurt Use Case -- 5 Horizontal Integration -- 5.1 BPMN+I to Address Multi Team Cooperation -- 5.2 Modelling Supply Chains/Networks by BPMN and UMM -- 6 Conclusion -- Data Analytics -- Conceptualizing Analytics: An Overview of Business Intelligence and Analytics from a Conceptual-Modeling Perspective -- 1 Introduction -- 1.1 What Is Analytics? | |
| 505 | 8 | |a 1.2 The Bigger Picture: Business Intelligence and Analytics -- 1.3 The (Big) Data Analysis Pipeline -- 2 Acquisition and Recording -- 3 Extraction, Cleaning, Integration, and Aggregation -- 3.1 Data Models -- 3.2 Data Preparation -- 4 Analysis and Modeling -- 4.1 Data Analytics -- 4.2 Pattern-Based Approach to Analytics -- 5 Interpretation and Action -- 6 Conclusion -- Discovering Actionable Knowledge for Industry 4.0: From Data Mining to Predictive and Prescriptive Analytics -- 1 Introduction -- 2 Data Collection and Preparation -- 2.1 Data Cleaning, Integration, and Transformation -- 2.2 Data Analytics Infrastructure -- 3 Data Analysis -- 3.1 Association and Correlation -- 3.2 Classification -- 3.3 Clustering and Outlier Detection -- 4 Use Case: Condition-Based Predictive Maintenance -- 5 Use Case: Predictive Quality Control -- 6 Further Reading -- 7 Conclusions and Recommendations for Practice -- Process Mining-Discovery, Conformance, and Enhancement of Manufacturing Processes -- 1 Introduction -- 2 Data Preparation -- 2.1 Data Quality in Manufacturing -- 2.2 Data Sources and Process Mining -- 3 Analysis Model Building -- 4 Analysis Methods -- 5 Visual Analytics and Interpretation -- 6 Conclusion and Open Research Questions -- Symbolic Artificial Intelligence Methods for Prescriptive Analytics -- 1 Introduction -- 2 Running Example: Flexible Job-Shop Scheduling -- 3 Constraint Programming -- 3.1 Flexible Job-Shop Scheduling: CP Formulation -- 3.2 Tools and Application Fields -- 4 Answer Set Programming -- 4.1 Flexible Job-Shop Scheduling: ASP Formulation -- 4.2 Tools and Applications Fields -- 5 Local Search -- 6 Summary -- 6.1 Industrie 4.0, AI, and Analytics -- 6.2 AI-based Problem-Solving in Industrie 4.0 -- Machine Learning for Cyber-Physical Systems -- 1 Introduction -- 2 Application Scenarios | |
| 505 | 8 | |a 2.1 Condition Monitoring and Predictive Maintenance | |
| 650 | 4 | |a Production engineering-Data processing | |
| 650 | 4 | |a Cloud computing | |
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Datensatz im Suchindex
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| author | Vogel-Heuser, Birgit |
| author_facet | Vogel-Heuser, Birgit |
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| author_sort | Vogel-Heuser, Birgit |
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| bvnumber | BV049872899 |
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| contents | Intro -- Preface to "Digital Transformation: Core Technologies and Emerging Topics from a Computer Science Perspective" -- Contents -- Digital Representation -- 1 Engineering Digital Twins and Digital Shadows as Key Enablers for Industry 4.0 -- 1 Introduction -- 1.1 Engineering Models in Industry 4.0 -- 1.2 Digital Shadows -- 1.3 Digital Twins -- 1.4 Outline -- 2 Challenges in Engineering a Digital Twin and Its Digital Shadows -- 2.1 Challenges in Engineering Digital Twins -- 2.2 Challenges in Operating Digital Twins -- 3 Engineering a Digital Twin and Its Digital Shadows -- 4 From Engineering Models to a Digital Twin -- 4.1 Semantic Data Extraction -- 4.2 Technologies for Connecting Digital Twins and Engineering Models -- 5 Digital Shadows and Data Processing -- 5.1 Data Lakes as a Serving Infrastructure -- 5.2 From Data Processing to Digital Shadows -- 5.3 Artificial Intelligence in Digital Shadows -- 6 The Future of Digital Twins and Digital Shadows -- 2 Designing Strongly-decoupled Industry 4.0 Applications Across the Stack: A Use Case -- 1 Introduction -- 2 Running Example: Factory in a Box -- 3 Architecture-centric Design -- 3.1 Architecture Modeling Elements -- 3.2 The C2myx Architectural Style for Strong Decoupling -- 3.3 Benefits of Using C2myx -- 3.4 Architecture Style Vs Modeling Language -- 3.5 Brief Related Work on Architectures in CPPS -- 4 Building Blocks -- 4.1 Capabilities -- 4.2 Grounding Capabilities in OPC UA -- 4.3 Production Process Modeling -- 5 Designing Behavior with Capabilities -- 5.1 Soft Real-Time Execution within Control Devices -- 5.2 Actor-based Non-time Critical Execution-PLC Level -- 5.3 Process-Based Execution -- 5.4 Scheduler Based Execution and Transport -- 5.5 Interoperability and Composition of Systems-of-Systems -- 6 Discussion and Conclusion -- 3 Variability in Products and Production -- 1 Introduction 2 Variability Challenges in Automation -- 2.1 Variability of Different Production Levels -- 2.2 Variability Binding Times -- 2.3 Re-Configurable Production -- 2.4 Verifying and Validating Variable Products and Production Systems -- 3 Injection Molding Machine Example -- 4 Variability Engineering -- 4.1 Variability Modeling -- 4.2 Variability Realization -- 4.3 Variability-Aware V& -- V -- 4.4 Variability Evolution -- 5 Product Line Adoption and Evolution -- 5.1 Extractive Adoption -- 5.2 Reactive Adoption -- 6 Research Challenges -- 7 Conclusions -- Digital Infrastructures -- 4 Reference Architectures for Closing the IT/OT Gap -- 1 Introduction -- 2 Architectural Reference Models -- 2.1 Reference Architecture, Views and Perspectives -- 2.2 Reference Models -- 3 Architectures Before IoT -- 3.1 Internet Protocol Suite and Open Systems Interconnection (OSI) Model -- 3.2 The Service-Oriented Architecture (SOA) -- 3.3 ISA-95: An Early Reference Architecture for Industry -- 4 Architectural Reference Models for IoT and IIoT -- 4.1 Requirements for an IIoT Architecture -- 4.2 Three ARMs for IIoT -- 4.3 Differences between the Three Architectural Reference Models -- 4.4 Connectivity: A Crosscutting Function in IIoT -- 5 Combining Information Technology with Operational Technology -- 5.1 Legacy Systems and Industrial Communication Technologies -- 5.2 Fog Computing -- 6 Applying ARM: An Industrial use Case -- 6.1 Legacy System -- 6.2 Objectives and Suitable Reference Architecture -- 6.3 Technical Implementation -- 6.4 Summary -- 7 Conclusion -- 5 Edge Computing: Use Cases and Research Challenges -- 1 Introduction -- 2 Edge Computing -- 3 Use Cases -- 3.1 Smart Manufacturing Scenario -- 4 Research Challenges -- 4.1 Resource Management -- 4.2 Network Management -- 4.3 Security and Privacy -- 5 Conclusion -- 6 Dynamic Access Control in Industry 4.0 Systems 1 Introduction -- 2 Running Example -- 3 Static Data Flow Analysis -- 4 Dynamic Access Control -- 4.1 Specification of the Running Example -- 4.2 Semantics -- 5 Application Scenarios -- 5.1 Overview of the Combined Approach -- 5.2 Palladio Design-Time Tooling -- 5.3 Runtime Decision Making -- 6 Related Work -- 7 Conclusion and Outlook -- 7 Challenges in OT Security and Their Impacts on Safety-Related Cyber-Physical Production Systems -- 1 Introduction -- 1.1 Production Network: The Automation Pyramid -- 1.2 Cyber-Physical Systems -- 1.3 Cyber-Physical Production System (CPPS) -- 1.4 Motivation -- 2 Vulnerable Assets of a CPPS -- 2.1 Tangible Assets -- 2.2 Intangible Assets -- 3 Threat Modeling and Attack Vectors -- 3.1 Complexity of CPPS Attacker Modeling -- 3.2 CPS-Specific Threat Modeling -- 3.3 Threats Against CPPS Assets -- 3.4 Attack Vectors -- 4 Measures Against Threats -- 4.1 Security Relevant Differences Between IT and OT Systems -- 4.2 IEC 62443 -- 4.3 NIST Special Publication 800-82 -- 4.4 IEC 61784 -- 5 Risk Management -- 6 Challenges of Integrating Safety and Security -- 6.1 Current Status and Objectives -- 6.2 Challenges Relevant for the Physical Layer -- 6.3 Software-Related Challenges -- 7 Conclusion -- 8 Runtime Monitoring for Systems of System -- 1 Introduction -- 2 Systems of Systems and Cyber-Physical Production Systems -- 3 Runtime Monitoring of Industry 4.0 Applications-The Two Perspectives -- 3.1 The Machine Vendor View -- 3.2 The Shop Floor Owner View -- 4 Potential Applications of Monitoring -- 4.1 Monitoring Safety Properties -- 4.2 Condition Monitoring -- 5 Requirements-Based Monitoring for Systems of Systems -- 5.1 Challenges for Monitoring Systems of Systems -- 5.2 A Requirements Monitoring Model -- 5.3 A Domain-Specific Language for SoS Constraint Checking -- 6 Conclusion 9 Blockchain Technologies in the Design and Operation of Cyber-Physical Systems -- 1 Introduction -- 2 Starting from the Beginning: What is a Blockchain? -- 2.1 Blockchain Basic Concepts -- 2.2 A Blockchain Under the Microscope -- 3 Manipulating Data in a Blockchain -- 3.1 Smart Contracts, Languages, and Turing Completeness -- 3.2 Smart Contracts in Turing-complete Languages: The Case of Ethereum -- 3.3 Example of a Smart Contract -- 3.4 Challenges in Contracts Lifecycle -- 4 Use Cases -- 4.1 Supply Chain in Maritime Trade -- 4.2 Collaborative Design of CPSs -- 5 Designing My Blockchain -- 5.1 Socio-Technical Challenges -- 5.2 Enterprise Blockchain Platforms -- 6 Conclusions -- Data Management -- Big Data Integration for Industry 4.0 -- 1 Introduction and Related Work -- 2 Data Integration Use Cases -- 3 Knowledge Graphs -- 3.1 Knowledge Graph Foundations -- 3.2 Knowledge Graph Construction -- 4 Entity Resolution -- 4.1 Blocking -- 4.2 Pair-wise Matching -- 4.3 Clustering -- 4.4 Incremental ER -- 4.5 ER Prototypes -- 5 Conclusion & -- Open Problems -- 11 Massive Data Sets - Is Data Quality Still an Issue? -- 1 Introduction -- 2 Outlier Identification -- 3 Robust Modeling -- 4 Variable Selection -- 5 Discussion and Summary -- Modelling the Top Floor: Internal and External Data Integration and Exchange -- 1 Introduction -- 2 Enterprise Resource Planning -- 3 Manufacturing Operations Management -- 4 Vertical Integration -- 4.1 Alignment of Complementary Conceptual Models -- 4.2 Application in the MyYoghurt Use Case -- 5 Horizontal Integration -- 5.1 BPMN+I to Address Multi Team Cooperation -- 5.2 Modelling Supply Chains/Networks by BPMN and UMM -- 6 Conclusion -- Data Analytics -- Conceptualizing Analytics: An Overview of Business Intelligence and Analytics from a Conceptual-Modeling Perspective -- 1 Introduction -- 1.1 What Is Analytics? 1.2 The Bigger Picture: Business Intelligence and Analytics -- 1.3 The (Big) Data Analysis Pipeline -- 2 Acquisition and Recording -- 3 Extraction, Cleaning, Integration, and Aggregation -- 3.1 Data Models -- 3.2 Data Preparation -- 4 Analysis and Modeling -- 4.1 Data Analytics -- 4.2 Pattern-Based Approach to Analytics -- 5 Interpretation and Action -- 6 Conclusion -- Discovering Actionable Knowledge for Industry 4.0: From Data Mining to Predictive and Prescriptive Analytics -- 1 Introduction -- 2 Data Collection and Preparation -- 2.1 Data Cleaning, Integration, and Transformation -- 2.2 Data Analytics Infrastructure -- 3 Data Analysis -- 3.1 Association and Correlation -- 3.2 Classification -- 3.3 Clustering and Outlier Detection -- 4 Use Case: Condition-Based Predictive Maintenance -- 5 Use Case: Predictive Quality Control -- 6 Further Reading -- 7 Conclusions and Recommendations for Practice -- Process Mining-Discovery, Conformance, and Enhancement of Manufacturing Processes -- 1 Introduction -- 2 Data Preparation -- 2.1 Data Quality in Manufacturing -- 2.2 Data Sources and Process Mining -- 3 Analysis Model Building -- 4 Analysis Methods -- 5 Visual Analytics and Interpretation -- 6 Conclusion and Open Research Questions -- Symbolic Artificial Intelligence Methods for Prescriptive Analytics -- 1 Introduction -- 2 Running Example: Flexible Job-Shop Scheduling -- 3 Constraint Programming -- 3.1 Flexible Job-Shop Scheduling: CP Formulation -- 3.2 Tools and Application Fields -- 4 Answer Set Programming -- 4.1 Flexible Job-Shop Scheduling: ASP Formulation -- 4.2 Tools and Applications Fields -- 5 Local Search -- 6 Summary -- 6.1 Industrie 4.0, AI, and Analytics -- 6.2 AI-based Problem-Solving in Industrie 4.0 -- Machine Learning for Cyber-Physical Systems -- 1 Introduction -- 2 Application Scenarios 2.1 Condition Monitoring and Predictive Maintenance |
| ctrlnum | (ZDB-30-PQE)EBC7192196 (ZDB-30-PAD)EBC7192196 (ZDB-89-EBL)EBL7192196 (OCoLC)1379590549 (DE-599)BVBBV049872899 |
| dewey-full | 658.5 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 658 - General management |
| dewey-raw | 658.5 |
| dewey-search | 658.5 |
| dewey-sort | 3658.5 |
| dewey-tens | 650 - Management and auxiliary services |
| discipline | Wirtschaftswissenschaften |
| edition | 1st ed |
| format | Electronic eBook |
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Application Scenarios -- 5.1 Overview of the Combined Approach -- 5.2 Palladio Design-Time Tooling -- 5.3 Runtime Decision Making -- 6 Related Work -- 7 Conclusion and Outlook -- 7 Challenges in OT Security and Their Impacts on Safety-Related Cyber-Physical Production Systems -- 1 Introduction -- 1.1 Production Network: The Automation Pyramid -- 1.2 Cyber-Physical Systems -- 1.3 Cyber-Physical Production System (CPPS) -- 1.4 Motivation -- 2 Vulnerable Assets of a CPPS -- 2.1 Tangible Assets -- 2.2 Intangible Assets -- 3 Threat Modeling and Attack Vectors -- 3.1 Complexity of CPPS Attacker Modeling -- 3.2 CPS-Specific Threat Modeling -- 3.3 Threats Against CPPS Assets -- 3.4 Attack Vectors -- 4 Measures Against Threats -- 4.1 Security Relevant Differences Between IT and OT Systems -- 4.2 IEC 62443 -- 4.3 NIST Special Publication 800-82 -- 4.4 IEC 61784 -- 5 Risk Management -- 6 Challenges of Integrating Safety and Security -- 6.1 Current Status and Objectives -- 6.2 Challenges Relevant 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Data in a Blockchain -- 3.1 Smart Contracts, Languages, and Turing Completeness -- 3.2 Smart Contracts in Turing-complete Languages: The Case of Ethereum -- 3.3 Example of a Smart Contract -- 3.4 Challenges in Contracts Lifecycle -- 4 Use Cases -- 4.1 Supply Chain in Maritime Trade -- 4.2 Collaborative Design of CPSs -- 5 Designing My Blockchain -- 5.1 Socio-Technical Challenges -- 5.2 Enterprise Blockchain Platforms -- 6 Conclusions -- Data Management -- Big Data Integration for Industry 4.0 -- 1 Introduction and Related Work -- 2 Data Integration Use Cases -- 3 Knowledge Graphs -- 3.1 Knowledge Graph Foundations -- 3.2 Knowledge Graph Construction -- 4 Entity Resolution -- 4.1 Blocking -- 4.2 Pair-wise Matching -- 4.3 Clustering -- 4.4 Incremental ER -- 4.5 ER Prototypes -- 5 Conclusion &amp -- Open Problems -- 11 Massive Data Sets - Is Data Quality Still an Issue? -- 1 Introduction -- 2 Outlier Identification -- 3 Robust Modeling -- 4 Variable Selection -- 5 Discussion and Summary -- Modelling the Top Floor: Internal and External Data Integration and Exchange -- 1 Introduction -- 2 Enterprise Resource Planning -- 3 Manufacturing Operations Management -- 4 Vertical Integration -- 4.1 Alignment of Complementary Conceptual Models -- 4.2 Application in the MyYoghurt Use Case -- 5 Horizontal Integration -- 5.1 BPMN+I to Address Multi Team Cooperation -- 5.2 Modelling Supply Chains/Networks by BPMN and UMM -- 6 Conclusion -- Data Analytics -- Conceptualizing Analytics: An Overview of Business Intelligence and Analytics from a Conceptual-Modeling Perspective -- 1 Introduction -- 1.1 What Is Analytics?</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">1.2 The Bigger Picture: Business Intelligence and Analytics -- 1.3 The (Big) Data Analysis Pipeline -- 2 Acquisition and Recording -- 3 Extraction, Cleaning, Integration, and Aggregation -- 3.1 Data Models -- 3.2 Data Preparation -- 4 Analysis and Modeling -- 4.1 Data Analytics -- 4.2 Pattern-Based Approach to Analytics -- 5 Interpretation and Action -- 6 Conclusion -- Discovering Actionable Knowledge for Industry 4.0: From Data Mining to Predictive and Prescriptive Analytics -- 1 Introduction -- 2 Data Collection and Preparation -- 2.1 Data Cleaning, Integration, and Transformation -- 2.2 Data Analytics Infrastructure -- 3 Data Analysis -- 3.1 Association and Correlation -- 3.2 Classification -- 3.3 Clustering and Outlier Detection -- 4 Use Case: Condition-Based Predictive Maintenance -- 5 Use Case: Predictive Quality Control -- 6 Further Reading -- 7 Conclusions and Recommendations for Practice -- Process Mining-Discovery, Conformance, and Enhancement of Manufacturing Processes -- 1 Introduction -- 2 Data Preparation -- 2.1 Data Quality in Manufacturing -- 2.2 Data Sources and Process Mining -- 3 Analysis Model Building -- 4 Analysis Methods -- 5 Visual Analytics and Interpretation -- 6 Conclusion and Open Research Questions -- Symbolic Artificial Intelligence Methods for Prescriptive Analytics -- 1 Introduction -- 2 Running Example: Flexible Job-Shop Scheduling -- 3 Constraint Programming -- 3.1 Flexible Job-Shop Scheduling: CP Formulation -- 3.2 Tools and Application Fields -- 4 Answer Set Programming -- 4.1 Flexible Job-Shop Scheduling: ASP Formulation -- 4.2 Tools and Applications Fields -- 5 Local Search -- 6 Summary -- 6.1 Industrie 4.0, AI, and Analytics -- 6.2 AI-based Problem-Solving in Industrie 4.0 -- Machine Learning for Cyber-Physical Systems -- 1 Introduction -- 2 Application Scenarios</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">2.1 Condition Monitoring and Predictive Maintenance</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Production engineering-Data processing</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Cloud computing</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wimmer, 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| id | DE-604.BV049872899 |
| illustrated | Not Illustrated |
| indexdate | 2024-09-19T05:21:46Z |
| institution | BVB |
| isbn | 9783662650042 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-035212357 |
| oclc_num | 1379590549 |
| open_access_boolean | |
| owner | DE-2070s |
| owner_facet | DE-2070s |
| physical | 1 Online-Ressource (522 Seiten) |
| psigel | ZDB-30-PQE ZDB-30-PQE HWR_PDA_PQE |
| publishDate | 2023 |
| publishDateSearch | 2023 |
| publishDateSort | 2023 |
| publisher | Springer Berlin / Heidelberg |
| record_format | marc |
| spelling | Vogel-Heuser, Birgit Verfasser aut Digital Transformation Core Technologies and Emerging Topics from a Computer Science Perspective 1st ed Berlin, Heidelberg Springer Berlin / Heidelberg 2023 ©2023 1 Online-Ressource (522 Seiten) txt rdacontent c rdamedia cr rdacarrier Description based on publisher supplied metadata and other sources Intro -- Preface to "Digital Transformation: Core Technologies and Emerging Topics from a Computer Science Perspective" -- Contents -- Digital Representation -- 1 Engineering Digital Twins and Digital Shadows as Key Enablers for Industry 4.0 -- 1 Introduction -- 1.1 Engineering Models in Industry 4.0 -- 1.2 Digital Shadows -- 1.3 Digital Twins -- 1.4 Outline -- 2 Challenges in Engineering a Digital Twin and Its Digital Shadows -- 2.1 Challenges in Engineering Digital Twins -- 2.2 Challenges in Operating Digital Twins -- 3 Engineering a Digital Twin and Its Digital Shadows -- 4 From Engineering Models to a Digital Twin -- 4.1 Semantic Data Extraction -- 4.2 Technologies for Connecting Digital Twins and Engineering Models -- 5 Digital Shadows and Data Processing -- 5.1 Data Lakes as a Serving Infrastructure -- 5.2 From Data Processing to Digital Shadows -- 5.3 Artificial Intelligence in Digital Shadows -- 6 The Future of Digital Twins and Digital Shadows -- 2 Designing Strongly-decoupled Industry 4.0 Applications Across the Stack: A Use Case -- 1 Introduction -- 2 Running Example: Factory in a Box -- 3 Architecture-centric Design -- 3.1 Architecture Modeling Elements -- 3.2 The C2myx Architectural Style for Strong Decoupling -- 3.3 Benefits of Using C2myx -- 3.4 Architecture Style Vs Modeling Language -- 3.5 Brief Related Work on Architectures in CPPS -- 4 Building Blocks -- 4.1 Capabilities -- 4.2 Grounding Capabilities in OPC UA -- 4.3 Production Process Modeling -- 5 Designing Behavior with Capabilities -- 5.1 Soft Real-Time Execution within Control Devices -- 5.2 Actor-based Non-time Critical Execution-PLC Level -- 5.3 Process-Based Execution -- 5.4 Scheduler Based Execution and Transport -- 5.5 Interoperability and Composition of Systems-of-Systems -- 6 Discussion and Conclusion -- 3 Variability in Products and Production -- 1 Introduction 2 Variability Challenges in Automation -- 2.1 Variability of Different Production Levels -- 2.2 Variability Binding Times -- 2.3 Re-Configurable Production -- 2.4 Verifying and Validating Variable Products and Production Systems -- 3 Injection Molding Machine Example -- 4 Variability Engineering -- 4.1 Variability Modeling -- 4.2 Variability Realization -- 4.3 Variability-Aware V& -- V -- 4.4 Variability Evolution -- 5 Product Line Adoption and Evolution -- 5.1 Extractive Adoption -- 5.2 Reactive Adoption -- 6 Research Challenges -- 7 Conclusions -- Digital Infrastructures -- 4 Reference Architectures for Closing the IT/OT Gap -- 1 Introduction -- 2 Architectural Reference Models -- 2.1 Reference Architecture, Views and Perspectives -- 2.2 Reference Models -- 3 Architectures Before IoT -- 3.1 Internet Protocol Suite and Open Systems Interconnection (OSI) Model -- 3.2 The Service-Oriented Architecture (SOA) -- 3.3 ISA-95: An Early Reference Architecture for Industry -- 4 Architectural Reference Models for IoT and IIoT -- 4.1 Requirements for an IIoT Architecture -- 4.2 Three ARMs for IIoT -- 4.3 Differences between the Three Architectural Reference Models -- 4.4 Connectivity: A Crosscutting Function in IIoT -- 5 Combining Information Technology with Operational Technology -- 5.1 Legacy Systems and Industrial Communication Technologies -- 5.2 Fog Computing -- 6 Applying ARM: An Industrial use Case -- 6.1 Legacy System -- 6.2 Objectives and Suitable Reference Architecture -- 6.3 Technical Implementation -- 6.4 Summary -- 7 Conclusion -- 5 Edge Computing: Use Cases and Research Challenges -- 1 Introduction -- 2 Edge Computing -- 3 Use Cases -- 3.1 Smart Manufacturing Scenario -- 4 Research Challenges -- 4.1 Resource Management -- 4.2 Network Management -- 4.3 Security and Privacy -- 5 Conclusion -- 6 Dynamic Access Control in Industry 4.0 Systems 1 Introduction -- 2 Running Example -- 3 Static Data Flow Analysis -- 4 Dynamic Access Control -- 4.1 Specification of the Running Example -- 4.2 Semantics -- 5 Application Scenarios -- 5.1 Overview of the Combined Approach -- 5.2 Palladio Design-Time Tooling -- 5.3 Runtime Decision Making -- 6 Related Work -- 7 Conclusion and Outlook -- 7 Challenges in OT Security and Their Impacts on Safety-Related Cyber-Physical Production Systems -- 1 Introduction -- 1.1 Production Network: The Automation Pyramid -- 1.2 Cyber-Physical Systems -- 1.3 Cyber-Physical Production System (CPPS) -- 1.4 Motivation -- 2 Vulnerable Assets of a CPPS -- 2.1 Tangible Assets -- 2.2 Intangible Assets -- 3 Threat Modeling and Attack Vectors -- 3.1 Complexity of CPPS Attacker Modeling -- 3.2 CPS-Specific Threat Modeling -- 3.3 Threats Against CPPS Assets -- 3.4 Attack Vectors -- 4 Measures Against Threats -- 4.1 Security Relevant Differences Between IT and OT Systems -- 4.2 IEC 62443 -- 4.3 NIST Special Publication 800-82 -- 4.4 IEC 61784 -- 5 Risk Management -- 6 Challenges of Integrating Safety and Security -- 6.1 Current Status and Objectives -- 6.2 Challenges Relevant for the Physical Layer -- 6.3 Software-Related Challenges -- 7 Conclusion -- 8 Runtime Monitoring for Systems of System -- 1 Introduction -- 2 Systems of Systems and Cyber-Physical Production Systems -- 3 Runtime Monitoring of Industry 4.0 Applications-The Two Perspectives -- 3.1 The Machine Vendor View -- 3.2 The Shop Floor Owner View -- 4 Potential Applications of Monitoring -- 4.1 Monitoring Safety Properties -- 4.2 Condition Monitoring -- 5 Requirements-Based Monitoring for Systems of Systems -- 5.1 Challenges for Monitoring Systems of Systems -- 5.2 A Requirements Monitoring Model -- 5.3 A Domain-Specific Language for SoS Constraint Checking -- 6 Conclusion 9 Blockchain Technologies in the Design and Operation of Cyber-Physical Systems -- 1 Introduction -- 2 Starting from the Beginning: What is a Blockchain? -- 2.1 Blockchain Basic Concepts -- 2.2 A Blockchain Under the Microscope -- 3 Manipulating Data in a Blockchain -- 3.1 Smart Contracts, Languages, and Turing Completeness -- 3.2 Smart Contracts in Turing-complete Languages: The Case of Ethereum -- 3.3 Example of a Smart Contract -- 3.4 Challenges in Contracts Lifecycle -- 4 Use Cases -- 4.1 Supply Chain in Maritime Trade -- 4.2 Collaborative Design of CPSs -- 5 Designing My Blockchain -- 5.1 Socio-Technical Challenges -- 5.2 Enterprise Blockchain Platforms -- 6 Conclusions -- Data Management -- Big Data Integration for Industry 4.0 -- 1 Introduction and Related Work -- 2 Data Integration Use Cases -- 3 Knowledge Graphs -- 3.1 Knowledge Graph Foundations -- 3.2 Knowledge Graph Construction -- 4 Entity Resolution -- 4.1 Blocking -- 4.2 Pair-wise Matching -- 4.3 Clustering -- 4.4 Incremental ER -- 4.5 ER Prototypes -- 5 Conclusion & -- Open Problems -- 11 Massive Data Sets - Is Data Quality Still an Issue? -- 1 Introduction -- 2 Outlier Identification -- 3 Robust Modeling -- 4 Variable Selection -- 5 Discussion and Summary -- Modelling the Top Floor: Internal and External Data Integration and Exchange -- 1 Introduction -- 2 Enterprise Resource Planning -- 3 Manufacturing Operations Management -- 4 Vertical Integration -- 4.1 Alignment of Complementary Conceptual Models -- 4.2 Application in the MyYoghurt Use Case -- 5 Horizontal Integration -- 5.1 BPMN+I to Address Multi Team Cooperation -- 5.2 Modelling Supply Chains/Networks by BPMN and UMM -- 6 Conclusion -- Data Analytics -- Conceptualizing Analytics: An Overview of Business Intelligence and Analytics from a Conceptual-Modeling Perspective -- 1 Introduction -- 1.1 What Is Analytics? 1.2 The Bigger Picture: Business Intelligence and Analytics -- 1.3 The (Big) Data Analysis Pipeline -- 2 Acquisition and Recording -- 3 Extraction, Cleaning, Integration, and Aggregation -- 3.1 Data Models -- 3.2 Data Preparation -- 4 Analysis and Modeling -- 4.1 Data Analytics -- 4.2 Pattern-Based Approach to Analytics -- 5 Interpretation and Action -- 6 Conclusion -- Discovering Actionable Knowledge for Industry 4.0: From Data Mining to Predictive and Prescriptive Analytics -- 1 Introduction -- 2 Data Collection and Preparation -- 2.1 Data Cleaning, Integration, and Transformation -- 2.2 Data Analytics Infrastructure -- 3 Data Analysis -- 3.1 Association and Correlation -- 3.2 Classification -- 3.3 Clustering and Outlier Detection -- 4 Use Case: Condition-Based Predictive Maintenance -- 5 Use Case: Predictive Quality Control -- 6 Further Reading -- 7 Conclusions and Recommendations for Practice -- Process Mining-Discovery, Conformance, and Enhancement of Manufacturing Processes -- 1 Introduction -- 2 Data Preparation -- 2.1 Data Quality in Manufacturing -- 2.2 Data Sources and Process Mining -- 3 Analysis Model Building -- 4 Analysis Methods -- 5 Visual Analytics and Interpretation -- 6 Conclusion and Open Research Questions -- Symbolic Artificial Intelligence Methods for Prescriptive Analytics -- 1 Introduction -- 2 Running Example: Flexible Job-Shop Scheduling -- 3 Constraint Programming -- 3.1 Flexible Job-Shop Scheduling: CP Formulation -- 3.2 Tools and Application Fields -- 4 Answer Set Programming -- 4.1 Flexible Job-Shop Scheduling: ASP Formulation -- 4.2 Tools and Applications Fields -- 5 Local Search -- 6 Summary -- 6.1 Industrie 4.0, AI, and Analytics -- 6.2 AI-based Problem-Solving in Industrie 4.0 -- Machine Learning for Cyber-Physical Systems -- 1 Introduction -- 2 Application Scenarios 2.1 Condition Monitoring and Predictive Maintenance Production engineering-Data processing Cloud computing Wimmer, Manuel Sonstige oth Erscheint auch als Druck-Ausgabe Vogel-Heuser, Birgit Digital Transformation Berlin, Heidelberg : Springer Berlin / Heidelberg,c2023 9783662650035 |
| spellingShingle | Vogel-Heuser, Birgit Digital Transformation Core Technologies and Emerging Topics from a Computer Science Perspective Intro -- Preface to "Digital Transformation: Core Technologies and Emerging Topics from a Computer Science Perspective" -- Contents -- Digital Representation -- 1 Engineering Digital Twins and Digital Shadows as Key Enablers for Industry 4.0 -- 1 Introduction -- 1.1 Engineering Models in Industry 4.0 -- 1.2 Digital Shadows -- 1.3 Digital Twins -- 1.4 Outline -- 2 Challenges in Engineering a Digital Twin and Its Digital Shadows -- 2.1 Challenges in Engineering Digital Twins -- 2.2 Challenges in Operating Digital Twins -- 3 Engineering a Digital Twin and Its Digital Shadows -- 4 From Engineering Models to a Digital Twin -- 4.1 Semantic Data Extraction -- 4.2 Technologies for Connecting Digital Twins and Engineering Models -- 5 Digital Shadows and Data Processing -- 5.1 Data Lakes as a Serving Infrastructure -- 5.2 From Data Processing to Digital Shadows -- 5.3 Artificial Intelligence in Digital Shadows -- 6 The Future of Digital Twins and Digital Shadows -- 2 Designing Strongly-decoupled Industry 4.0 Applications Across the Stack: A Use Case -- 1 Introduction -- 2 Running Example: Factory in a Box -- 3 Architecture-centric Design -- 3.1 Architecture Modeling Elements -- 3.2 The C2myx Architectural Style for Strong Decoupling -- 3.3 Benefits of Using C2myx -- 3.4 Architecture Style Vs Modeling Language -- 3.5 Brief Related Work on Architectures in CPPS -- 4 Building Blocks -- 4.1 Capabilities -- 4.2 Grounding Capabilities in OPC UA -- 4.3 Production Process Modeling -- 5 Designing Behavior with Capabilities -- 5.1 Soft Real-Time Execution within Control Devices -- 5.2 Actor-based Non-time Critical Execution-PLC Level -- 5.3 Process-Based Execution -- 5.4 Scheduler Based Execution and Transport -- 5.5 Interoperability and Composition of Systems-of-Systems -- 6 Discussion and Conclusion -- 3 Variability in Products and Production -- 1 Introduction 2 Variability Challenges in Automation -- 2.1 Variability of Different Production Levels -- 2.2 Variability Binding Times -- 2.3 Re-Configurable Production -- 2.4 Verifying and Validating Variable Products and Production Systems -- 3 Injection Molding Machine Example -- 4 Variability Engineering -- 4.1 Variability Modeling -- 4.2 Variability Realization -- 4.3 Variability-Aware V& -- V -- 4.4 Variability Evolution -- 5 Product Line Adoption and Evolution -- 5.1 Extractive Adoption -- 5.2 Reactive Adoption -- 6 Research Challenges -- 7 Conclusions -- Digital Infrastructures -- 4 Reference Architectures for Closing the IT/OT Gap -- 1 Introduction -- 2 Architectural Reference Models -- 2.1 Reference Architecture, Views and Perspectives -- 2.2 Reference Models -- 3 Architectures Before IoT -- 3.1 Internet Protocol Suite and Open Systems Interconnection (OSI) Model -- 3.2 The Service-Oriented Architecture (SOA) -- 3.3 ISA-95: An Early Reference Architecture for Industry -- 4 Architectural Reference Models for IoT and IIoT -- 4.1 Requirements for an IIoT Architecture -- 4.2 Three ARMs for IIoT -- 4.3 Differences between the Three Architectural Reference Models -- 4.4 Connectivity: A Crosscutting Function in IIoT -- 5 Combining Information Technology with Operational Technology -- 5.1 Legacy Systems and Industrial Communication Technologies -- 5.2 Fog Computing -- 6 Applying ARM: An Industrial use Case -- 6.1 Legacy System -- 6.2 Objectives and Suitable Reference Architecture -- 6.3 Technical Implementation -- 6.4 Summary -- 7 Conclusion -- 5 Edge Computing: Use Cases and Research Challenges -- 1 Introduction -- 2 Edge Computing -- 3 Use Cases -- 3.1 Smart Manufacturing Scenario -- 4 Research Challenges -- 4.1 Resource Management -- 4.2 Network Management -- 4.3 Security and Privacy -- 5 Conclusion -- 6 Dynamic Access Control in Industry 4.0 Systems 1 Introduction -- 2 Running Example -- 3 Static Data Flow Analysis -- 4 Dynamic Access Control -- 4.1 Specification of the Running Example -- 4.2 Semantics -- 5 Application Scenarios -- 5.1 Overview of the Combined Approach -- 5.2 Palladio Design-Time Tooling -- 5.3 Runtime Decision Making -- 6 Related Work -- 7 Conclusion and Outlook -- 7 Challenges in OT Security and Their Impacts on Safety-Related Cyber-Physical Production Systems -- 1 Introduction -- 1.1 Production Network: The Automation Pyramid -- 1.2 Cyber-Physical Systems -- 1.3 Cyber-Physical Production System (CPPS) -- 1.4 Motivation -- 2 Vulnerable Assets of a CPPS -- 2.1 Tangible Assets -- 2.2 Intangible Assets -- 3 Threat Modeling and Attack Vectors -- 3.1 Complexity of CPPS Attacker Modeling -- 3.2 CPS-Specific Threat Modeling -- 3.3 Threats Against CPPS Assets -- 3.4 Attack Vectors -- 4 Measures Against Threats -- 4.1 Security Relevant Differences Between IT and OT Systems -- 4.2 IEC 62443 -- 4.3 NIST Special Publication 800-82 -- 4.4 IEC 61784 -- 5 Risk Management -- 6 Challenges of Integrating Safety and Security -- 6.1 Current Status and Objectives -- 6.2 Challenges Relevant for the Physical Layer -- 6.3 Software-Related Challenges -- 7 Conclusion -- 8 Runtime Monitoring for Systems of System -- 1 Introduction -- 2 Systems of Systems and Cyber-Physical Production Systems -- 3 Runtime Monitoring of Industry 4.0 Applications-The Two Perspectives -- 3.1 The Machine Vendor View -- 3.2 The Shop Floor Owner View -- 4 Potential Applications of Monitoring -- 4.1 Monitoring Safety Properties -- 4.2 Condition Monitoring -- 5 Requirements-Based Monitoring for Systems of Systems -- 5.1 Challenges for Monitoring Systems of Systems -- 5.2 A Requirements Monitoring Model -- 5.3 A Domain-Specific Language for SoS Constraint Checking -- 6 Conclusion 9 Blockchain Technologies in the Design and Operation of Cyber-Physical Systems -- 1 Introduction -- 2 Starting from the Beginning: What is a Blockchain? -- 2.1 Blockchain Basic Concepts -- 2.2 A Blockchain Under the Microscope -- 3 Manipulating Data in a Blockchain -- 3.1 Smart Contracts, Languages, and Turing Completeness -- 3.2 Smart Contracts in Turing-complete Languages: The Case of Ethereum -- 3.3 Example of a Smart Contract -- 3.4 Challenges in Contracts Lifecycle -- 4 Use Cases -- 4.1 Supply Chain in Maritime Trade -- 4.2 Collaborative Design of CPSs -- 5 Designing My Blockchain -- 5.1 Socio-Technical Challenges -- 5.2 Enterprise Blockchain Platforms -- 6 Conclusions -- Data Management -- Big Data Integration for Industry 4.0 -- 1 Introduction and Related Work -- 2 Data Integration Use Cases -- 3 Knowledge Graphs -- 3.1 Knowledge Graph Foundations -- 3.2 Knowledge Graph Construction -- 4 Entity Resolution -- 4.1 Blocking -- 4.2 Pair-wise Matching -- 4.3 Clustering -- 4.4 Incremental ER -- 4.5 ER Prototypes -- 5 Conclusion & -- Open Problems -- 11 Massive Data Sets - Is Data Quality Still an Issue? -- 1 Introduction -- 2 Outlier Identification -- 3 Robust Modeling -- 4 Variable Selection -- 5 Discussion and Summary -- Modelling the Top Floor: Internal and External Data Integration and Exchange -- 1 Introduction -- 2 Enterprise Resource Planning -- 3 Manufacturing Operations Management -- 4 Vertical Integration -- 4.1 Alignment of Complementary Conceptual Models -- 4.2 Application in the MyYoghurt Use Case -- 5 Horizontal Integration -- 5.1 BPMN+I to Address Multi Team Cooperation -- 5.2 Modelling Supply Chains/Networks by BPMN and UMM -- 6 Conclusion -- Data Analytics -- Conceptualizing Analytics: An Overview of Business Intelligence and Analytics from a Conceptual-Modeling Perspective -- 1 Introduction -- 1.1 What Is Analytics? 1.2 The Bigger Picture: Business Intelligence and Analytics -- 1.3 The (Big) Data Analysis Pipeline -- 2 Acquisition and Recording -- 3 Extraction, Cleaning, Integration, and Aggregation -- 3.1 Data Models -- 3.2 Data Preparation -- 4 Analysis and Modeling -- 4.1 Data Analytics -- 4.2 Pattern-Based Approach to Analytics -- 5 Interpretation and Action -- 6 Conclusion -- Discovering Actionable Knowledge for Industry 4.0: From Data Mining to Predictive and Prescriptive Analytics -- 1 Introduction -- 2 Data Collection and Preparation -- 2.1 Data Cleaning, Integration, and Transformation -- 2.2 Data Analytics Infrastructure -- 3 Data Analysis -- 3.1 Association and Correlation -- 3.2 Classification -- 3.3 Clustering and Outlier Detection -- 4 Use Case: Condition-Based Predictive Maintenance -- 5 Use Case: Predictive Quality Control -- 6 Further Reading -- 7 Conclusions and Recommendations for Practice -- Process Mining-Discovery, Conformance, and Enhancement of Manufacturing Processes -- 1 Introduction -- 2 Data Preparation -- 2.1 Data Quality in Manufacturing -- 2.2 Data Sources and Process Mining -- 3 Analysis Model Building -- 4 Analysis Methods -- 5 Visual Analytics and Interpretation -- 6 Conclusion and Open Research Questions -- Symbolic Artificial Intelligence Methods for Prescriptive Analytics -- 1 Introduction -- 2 Running Example: Flexible Job-Shop Scheduling -- 3 Constraint Programming -- 3.1 Flexible Job-Shop Scheduling: CP Formulation -- 3.2 Tools and Application Fields -- 4 Answer Set Programming -- 4.1 Flexible Job-Shop Scheduling: ASP Formulation -- 4.2 Tools and Applications Fields -- 5 Local Search -- 6 Summary -- 6.1 Industrie 4.0, AI, and Analytics -- 6.2 AI-based Problem-Solving in Industrie 4.0 -- Machine Learning for Cyber-Physical Systems -- 1 Introduction -- 2 Application Scenarios 2.1 Condition Monitoring and Predictive Maintenance Production engineering-Data processing Cloud computing |
| title | Digital Transformation Core Technologies and Emerging Topics from a Computer Science Perspective |
| title_auth | Digital Transformation Core Technologies and Emerging Topics from a Computer Science Perspective |
| title_exact_search | Digital Transformation Core Technologies and Emerging Topics from a Computer Science Perspective |
| title_full | Digital Transformation Core Technologies and Emerging Topics from a Computer Science Perspective |
| title_fullStr | Digital Transformation Core Technologies and Emerging Topics from a Computer Science Perspective |
| title_full_unstemmed | Digital Transformation Core Technologies and Emerging Topics from a Computer Science Perspective |
| title_short | Digital Transformation |
| title_sort | digital transformation core technologies and emerging topics from a computer science perspective |
| title_sub | Core Technologies and Emerging Topics from a Computer Science Perspective |
| topic | Production engineering-Data processing Cloud computing |
| topic_facet | Production engineering-Data processing Cloud computing |
| work_keys_str_mv | AT vogelheuserbirgit digitaltransformationcoretechnologiesandemergingtopicsfromacomputerscienceperspective AT wimmermanuel digitaltransformationcoretechnologiesandemergingtopicsfromacomputerscienceperspective |