The Data Grid : A Multidisciplinary Guide to Building the Soft Infrastructure of the AI Era (Springerbriefs in Computer Science)

個数：

The Data Grid : A Multidisciplinary Guide to Building the Soft Infrastructure of the AI Era (Springerbriefs in Computer Science)

Lee, Zhongyuan Thomas

ウェブストア価格 ¥12,580（本体¥11,437）
Springer Nature Switzerland AG（2026/05発売）
外貨定価 US$ 54.99
ポイント 114pt

オンデマンド(OD/POD)版です。キャンセルは承れません。

【入荷遅延について】
世界情勢の影響により、海外からお取り寄せとなる洋書・洋古書の入荷が、表示している標準的な納期よりも遅延する場合がございます。
おそれいりますが、あらかじめご了承くださいますようお願い申し上げます。

◆画像の表紙や帯等は実物とは異なる場合があります。

◆ウェブストアでの洋書販売価格は、弊社店舗等での販売価格とは異なります。
また、洋書販売価格は、ご注文確定時点での日本円価格となります。
ご注文確定後に、同じ洋書の販売価格が変動しても、それは反映されません。

製本 Paperback:紙装版/ペーパーバック版／ページ数 126 p.
商品コード 9783032250032

Description

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.

.- Data as Soft Infrastructure in the AI Era.
.- The Foundations of Soft Data Infrastructure.
.- The Components of Soft Data Infrastructure.
.- Architecting the Soft Data Infrastructure A Cross-Disciplinary Blueprint.
.- Data Modeling.
.- Data Lineage and Documentation.
.- Data Quality and Integrity.
.- Data Governance and Security.
.- Data Orchestration.
.- Data Consumption.
.- The Data Grid A Forward-Looking Synthesis.

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.