Digital Transformation. Core Technologies and Emerging Topics from a Computer Science Perspective

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
		2.2 Resource Optimization
		2.3 Quality Assurance of Products
		2.4 Diagnosis
	3 Machine Learning
	4 Challenges to ML for Cyber-Physical Systems
	5 Challenge 1: Time and State
		5.1 Approaches
		5.2 State of the Art
		5.3 Conclusion
	6 Challenge 2: Uncertainty and Noise
		6.1 Approaches
		6.2 State of the Art
		6.3 Conclusion
	7 Challenge 3: Usage of A-Priori Knowledge
		7.1 Approaches
		7.2 State of the Art
		7.3 Conclusion
	8 Challenge 4: Representations and Concepts
		8.1 Approaches
		8.2 State of the Art
		8.3 Conclusion
	9 Conclusion
Visual Data Science for Industrial Applications
	1 Introduction
	2 Foundations of Visual Data Science and Challenges
		2.1 Interactive Data Visualization
		2.2 Integrating Data Visualization with Data Science
		2.3 Data Types and Characteristics
		2.4 Challenges in Creating Visual Data Analysis Applications
		2.5 Under the Hood: Visual Data Science Infrastructure
	3 Selected Visual Data Science Approaches for Industrial Data Analysis
		3.1 Production Planning
		3.2 Quality Control
		3.3 Equipment Condition Monitoring
		3.4 Predictive Maintenance
		3.5 Causality Analysis
	4 Conclusions and Future Directions
Digital Transformation towards Industry 5.0
Self-Adaptive Digital Assistance Systems for Work 4.0
	1 Introduction
	2 Background
		2.1 Industry and Work 4.0
		2.2 Digital Assistance Systems
	3 Challenges
	4 Related Work
	5 Monitoring and Adaptation Framework
	6 Case Studies
		6.1 Example 1: AR-Based Context-Aware Assistance for Maintenance Tasks
		6.2 Example 2: VR-Based Context-Aware Assistance for Warehouse Management Training
		6.3 Discussion
	7 Conclusion and Outlook
Digital Transformation—Towards Flexible Human-Centric Enterprises
	Abstract
	1	Introduction
	2	Foundations and Related Work
		2.1	Notions
		2.2	Related Work
	3	Reference Model-Based Evolution of OSP
		3.1	OSP Evolution Taxonomy
		3.2	Conceptual Enterprise Model
		3.3	Reference Enterprise Model
		3.4	The Concrete Enterprise Model
	4	Digital Transformation of Enterprises
	5	Conclusions and Future Perspectives
	References