Datenbank-Spektrum

, Volume 18, Issue 1, pp 27–37 | Cite as

Semantic Data Management for Experimental Manufacturing Technologies

  • Nasr Kasrin
  • Maliha Qureshi
  • Simon Steuer
  • Daniela Nicklas
Schwerpunktbeitrag
  • 80 Downloads

Abstract

Experimental manufacturing technologies play a significant role in improving production processes. For example, the microwave assisted manufacturing of composites can save energy and reduce turn-around times compared to the traditional heating in ovens. However, since this technology is not yet well-understood, it requires more research and development activities (e.g., simulation or production experiments) to enable stable and efficient production with controlled product quality. These activities span multiple divisions over (possibly) multiple organizations and require close cooperation and communication. In addition this process proceeds in an iterative manner and produces a lot of data and documents on the way. From our practical experience working in such a project, we identified knowledge gaps and communication challenges, many of which can be overcome with the support of IT Knowledge Management Systems.

The contribution of this paper is twofold: first, we propose a system design and domain modeling framework for developing semantic data management systems. Second, we apply the framework to develop a system with state-of-the-art technology practices. The system enriches data with semantic metadata, and manages heterogeneous data forms (structured and unstructured) using polyglot persistence and a data lake. Moreover, the system is modular and service-oriented; it can easily integrate with existing systems in the manufacturing environment via HTTP interfaces. The example use case for the proposed system involves simulations and experiments on the microwave assisted heating of composites as carried out in the scope of the Horizon 2020 project “SIMUTOOL” (http://www.simutool.com).

Keywords

Knowledge Management Semantic Data Management Data Lakes Digital Manufacturing Industry 4.0 Information Systems 

Notes

Acknowledgements

The authors would like to thank the two anonymous reviewers for their valuable comments, as well as Eugene Yip and Rayan Alagbari for proofreading the paper in such a short notice.

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Copyright information

© Springer-Verlag GmbH Deutschland, ein Teil von Springer Nature 2018

Authors and Affiliations

  • Nasr Kasrin
    • 1
  • Maliha Qureshi
    • 1
  • Simon Steuer
    • 1
  • Daniela Nicklas
    • 1
  1. 1.University of BambergBambergGermany

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