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

Part I - Digital Representation: Engineering Digital Twins and Digital Shadows as Key Enablers for Industry 4.0.- Designing Strongly-decoupled Industry 4.0 applications across the stack: a use case.- Variability in Products and Production.- Part II - Digital Infrastructures: Reference Architectures for closing the IT/OT gap.- Edge Computing: Use Cases and Research Challenges.- Dynamic Access Control in Industry 4.0 Systems.- Challenges in OT-Security and their Impacts on Safety-related Cyber-Physical Production Systems.- Runtime Monitoring for Systems of System.- Blockchain technologies in the design and operation of cyber-physical systems.- Part III - Data Management: Big Data Integration for Industry 4.0.- Tons of data - is data quality still an issue?.- Coupling of Top Floor Internal and External Data Exchange Matters.- Part IV - Data Analytics: Conceptualizing Analytics: An Overview of Business Intelligence and Analyticsfrom a Conceptual Modeling Perspective.- Discovering Actionable Knowledge for Industry 4.0: From Data Mining to Predictive and Prescriptive Analytics.- Process Mining - Discovery, Conformance, and Enhancement of Manufacturing Processes.- Symbolic artificial intelligence methods for prescriptive analytics.- Machine Learning for Cyber-Physical Systems.- Visual Data Science for Industrial Applications.- Part V - Digital Transformation towards Industry 5.0: Self-Adaptive Digital Assistance Systems for Work 4.0.- Digital Transformation - Towards flexible human-centric enterprises.