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Challenges for capturing data within data-driven design processes

Published online by Cambridge University Press:  16 May 2024

Christopher Langner*
Affiliation:
University of Stuttgart, Germany
Yevgeni Paliyenko
Affiliation:
University of Stuttgart, Germany
Benedikt Müller
Affiliation:
University of Stuttgart, Germany
Daniel Roth
Affiliation:
University of Stuttgart, Germany
Matthias R. Guertler
Affiliation:
University of Technology Sydney, Australia
Matthias Kreimeyer
Affiliation:
University of Stuttgart, Germany

Abstract

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Cyber-Physical-Systems provide extensive data gathering opportunities along the lifecycle, enabling data-driven design to improve the design process. However, its implementation faces challenges, particularly in the initial data capturing stage. To identify those, a comprehensive approach combining a systematic literature review and an industry survey was applied. Four groups of interrelated challenges were identified as most relevant to practitioners: data selection, data availability in systems, knowledge about data science processes and tools, and guiding users in targeted data capturing.

Type
Artificial Intelligence and Data-Driven Design
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2024.

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