
The Data Grid by Zhongyuan Thomas Lee
As industries transition from the automation focus of Industry 4.0 to the human–AI collaboration of Industry 5.0, artificial intelligence stands at the forefront. Yet the lasting capability of intelligent systems is rooted in a deeper layer: robust data infrastructures. The Data Grid argues that AI’s true scalability and reliability hinge not just on algorithms, but on stable, governed, and semantically structured data systems. Across industries, fragmented and inconsistent data foundations constrain AI’s potential. By redefining data as infrastructure' imbued with stability, scalability, and lifecycle continuity, this volume establishes the structural foundation for sustainable intelligence.
Drawing from systems engineering, industrial engineering, reliability theory, and risk management, this book offers a cross-disciplinary framework for building AI-native data infrastructures. While data engineering originates from computer and software engineering, in the infrastructure context, it is not and should not be confined to these disciplines. It shows how principles such as determinism, fault isolation, boundary control, and semantic layering can be adapted for enterprise-level data environments. Supported by engineering analysis and practical case studies, the book redefines data not as a static resource but as a continuously flowing soft infrastructure: an engineered backbone for resilient, long-term intelligent systems.
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Concise Computer Mathematics
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Resource Management for Satellite-Terrestrial Vehicular Networks
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Computational Intelligence for Remote Sensing Image Change Detection
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Algorithmic Fairness in AI-Mediated Institutional Communication
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AbstractSwarm Multi-Agent Modeling and Simulation
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Federated Learning for Smart Mobility
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Introduction to Ethical Software Development
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Digital Image Forgery Detection
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Blockchain Without Barriers
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Human Reconstruction Using mmWave Technology
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Universal Time-Series Forecasting with Mixture Predictors
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Turbo Message Passing Algorithms for Structured Signal Recovery
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Developing Sustainable and Energy-Efficient Software Systems
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Latent Factor Analysis for High-dimensional and Sparse Matrices
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Cognitive Security
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Dynamic Network Representation Based on Latent Factorization of Tensors
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Security-aware Cooperation in Cognitive Radio Networks
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Robust Latent Feature Learning for Incomplete Big Data
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Seamless and Secure Communications over Heterogeneous Wireless Networks
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The AI Act and The Agile Safety Plan
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Representation in Machine Learning
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Enabling Secure and Privacy Preserving Communications in Smart Grids
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Energy Detection for Spectrum Sensing in Cognitive Radio
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Enabling Content Distribution in Vehicular Ad Hoc Networks
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Machine Learning Empowered Intelligent Data Center Networking
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Energy-Efficient Area Coverage for Intruder Detection in Sensor Networks
Zhongyuan Thomas Lee (formerly Zhongyuan Li) is a doctoral researcher in Multidisciplinary Engineering at Texas A&M University. He also serves as a Staff Data Engineer at Compass, where he works on enterprise-scale data infrastructure. His research focuses on Industry 4.0/5.0 systems, digital twins, and AI-ready data infrastructures. He has published over twenty-five peer-reviewed papers in journals and conferences. With more than fifteen years of professional experience as a Data Engineer, he has worked across multiple industries including power grids, telecommunications, finance, and healthcare.
| SKU | Unavailable |
| ISBN 13 | 9783032250032 |
| ISBN 10 | 303225003X |
| Title | The Data Grid |
| Author | Zhongyuan Thomas Lee |
| Series | Springerbriefs In Computer Science |
| Condition | Unavailable |
| Binding Type | Paperback |
| Publisher | Springer Nature Switzerland AG |
| Year published | 2026-05-23 |
| Number of pages | 126 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |

























