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This book presents a dual perspective on modern research and praxis on Data Science, Analytics, and AI/Machine Learning (DSA-AI/ML) system with small or big data. Consequently, potential readers academics, researchers and practitioners interested in the systematic development and implementation of DSA-AI/ML systems can be benefited with the high-quality conceptual and empirical research chapters focused on:
- Foundations, Development Platforms, and Tools on Engineering and Management of DSA-AI/ML Projects:
- DSA-AI/ML reference architectures.
- Data visualization principles for DSA-AI/ML.
- Federated Learning in large-scale DSA-AI/ML systems.
- Achievements, Challenges, Trends, and Future Research Directions on DSA-AI/ML Projects:
- Large multimodal model-based simulation game for DSA-AI/ML systems.
- Value stream analysis and design applied to DSA-AI/ML systems.
- Quality management 4.0 and AI for DSA-AI/ML systems.
Hence, this research-oriented co-edited book contributes to achieve the systematic development and implementation of Data Science, Analytics, and AI/ML systems.
List of contents
1.A Review of Main Non-Proprietary Domain- Independent Data Science Analytics AI/ML Reference Architectures a dual ISO/IEC/IEEE 42010 and IT Service Design Approach.- 2.Data Visualization in the Era of Data Science: a review.- 3.Requirements for using Federated Learning in Manufacturing Supply Chains.- 4.Large Multimodal Model-Based Simulation Game as a Socio-Technical System for Value Stream Analysis and Design.- 5.A Data-driven Clustering Approach for Assessing Service Performance of Brand Chains' Branches in the Food Service Industry Data Analytics Systems.- 6.Integrating Quality Management 4.0 with AI and Machine Learning.
Summary
This book presents a dual perspective on modern research and praxis on Data Science, Analytics, and AI/Machine Learning (DSA-AI/ML) system with small or big data. Consequently, potential readers—academics, researchers and practitioners interested in the systematic development and implementation of DSA-AI/ML systems—can be benefited with the high-quality conceptual and empirical research chapters focused on:
Foundations, Development Platforms, and Tools on Engineering and Management of DSA-AI/ML Projects:
- DSA-AI/ML reference architectures.
- Data visualization principles for DSA-AI/ML.
- Federated Learning in large-scale DSA-AI/ML systems.
Achievements, Challenges, Trends, and Future Research Directions on DSA-AI/ML Projects:
- Large multimodal model-based simulation game for DSA-AI/ML systems.
- Value stream analysis and design applied to DSA-AI/ML systems.
- Quality management 4.0 and AI for DSA-AI/ML systems.
Hence, this research-oriented co-edited book contributes to achieve the systematic development and implementation of Data Science, Analytics, and AI/ML systems.
