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  • 标题:Domain Models and Data Modeling as Drivers for Data Management: The ASSISTANT Data Fabric Approach
  • 本地全文:下载
  • 作者:Per-Olov Östberg ; Eduardo Vyhmeister ; Gabriel G. Castañé
  • 期刊名称:IFAC PapersOnLine
  • 印刷版ISSN:2405-8963
  • 出版年度:2022
  • 卷号:55
  • 期号:10
  • 页码:19-24
  • DOI:10.1016/j.ifacol.2022.09.362
  • 语种:English
  • 出版社:Elsevier
  • 摘要:To develop AI-based models capable of governing or providing decision support to complex manufacturing environments, abstractions and mechanisms for unified management of data storage and processing capabilities are needed. Specifically, as such models tend to include and rely on detailed representations of systems, components, and tools with complex interactions, mechanisms for simplifying, integrating, and scaling management capabilities in the presence of complex data requirements (e.g., high volume, velocity, and diversity of data) are of particular interest. A data fabric is a system that provides a unified architecture for management and provisioning of data. In this work we present the background, design requirements, and high-level outline of the ASSISTANT data fabric - a flexible data management tool designed for use in adaptive manufacturing contexts. The paper outlines the implementation of the system with specific focus on the use of domain models and the data modeling approach used, as well as provides a generic use case structure reusable in many industrial contexts.
  • 关键词:Domain Models;Knowledge Graph;Data Modeling;Data Fabric;Data Base;Data Lake;AI;adaptive manufacturing
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