Engineering and Management of Data Science, Analytics, and AI/ML Projects

Foundations, Models, Frameworks, Architectures, Standards, Processes, Practices, Platforms and Tools for Small and Big Data
Erscheinungsjahr 2025
ISBN: 978-3-032-06888-0
Verlag: Springer

Buch, Englisch, 139 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 405 g

Reihe: Intelligent Systems Reference Library

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.

Zielgruppe

Research

Autoren/Hrsg.

Mora, Manuel

Gómez, Jorge Marx

Wang, Fen

Duran-Limon, Hector A.

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Regelungstechnik
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken

Inhaltsverzeichnis

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.