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Industrial Dataspace for smart manufacturing: connotation, key technologies, and framework.

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Title: Industrial Dataspace for smart manufacturing: connotation, key technologies, and framework.
Authors: Guo, Jingwei1 (AUTHOR), Cheng, Ying1 (AUTHOR) ycheng@buaa.edu.cn, Wang, Dongxu1 (AUTHOR), Tao, Fei1 (AUTHOR), Pickl, Stefan2 (AUTHOR)
Source: International Journal of Production Research. Jun2023, Vol. 61 Issue 12, p3868-3883. 16p. 5 Diagrams, 1 Chart.
Subjects: Manufacturing process management, Workflow, Databases, Big data, Data management
Abstract: Smart manufacturing is a popular concept for smarter decision-making and more efficient production. Although distributed methods for data management and processing in smart manufacturing have many advantages such as low cost of adaptation and convenience for local database, some methods are hard to manage variable data sources and discover proper range of data for smart decision-making. Therefore, Dataspace is considered in this article to be a feasible and effective method. From the relation-defined perspective of utilisation of industrial Big Data, the contribution is a novel industrial Dataspace design with static structure and working flow paths for smart manufacturing. In design, the industrial Dataspace platform has been proposed to accommodate smart manufacturing characteristics with the intelligence of pay-as-you-go, like harnessing distributed heterogenous data from industrial enterprises, understanding industrial data by ontology or knowledge, corelating the data with smart applications, and enabling related decisions. A further analytical case in Surface Mounting Technology manufacturing of welding procedure is provided to illustrate the execution of customisation, focused and related decision support, and system evolution within industrial Dataspace. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Production+Research%22">International Journal of Production Research</searchLink>. Jun2023, Vol. 61 Issue 12, p3868-3883. 16p. 5 Diagrams, 1 Chart.
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  Data: Smart manufacturing is a popular concept for smarter decision-making and more efficient production. Although distributed methods for data management and processing in smart manufacturing have many advantages such as low cost of adaptation and convenience for local database, some methods are hard to manage variable data sources and discover proper range of data for smart decision-making. Therefore, Dataspace is considered in this article to be a feasible and effective method. From the relation-defined perspective of utilisation of industrial Big Data, the contribution is a novel industrial Dataspace design with static structure and working flow paths for smart manufacturing. In design, the industrial Dataspace platform has been proposed to accommodate smart manufacturing characteristics with the intelligence of pay-as-you-go, like harnessing distributed heterogenous data from industrial enterprises, understanding industrial data by ontology or knowledge, corelating the data with smart applications, and enabling related decisions. A further analytical case in Surface Mounting Technology manufacturing of welding procedure is provided to illustrate the execution of customisation, focused and related decision support, and system evolution within industrial Dataspace. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/00207543.2021.1955996
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        Text: English
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      – SubjectFull: Databases
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              Text: Jun2023
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              Y: 2023
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