• English
  • Deutsch
  • Log In
    Password Login
    Research Outputs
    Fundings & Projects
    Researchers
    Institutes
    Statistics
Repository logo
Fraunhofer-Gesellschaft
  1. Home
  2. Fraunhofer-Gesellschaft
  3. Konferenzschrift
  4. Towards a Reference Model for Knowledge Driven Data Provision Processes
 
  • Details
  • Full
Options
2020
Conference Paper
Title

Towards a Reference Model for Knowledge Driven Data Provision Processes

Abstract
Value creation in most business areas takes place in networks that involve a wide range of stakeholders from various disciplines within and beyond company borders. Collaboration in such networks require the exchange of knowledge that is manifested in digital artefacts and consequently in data. As the utilization of that ""hidden"" knowledge has become increasingly important, the provision of relevant data in sufficient quality has also become crucial. This article proposes a reference model for knowledge driven data provision processes that is developed within a research project at the Virtual Vehicle Research GmbH for a future networked engineering environment. It describes a systematic process to drive operationalization of data provision from knowledge requirements to identify, extract and provide raw data until the application of such data sets. Still, the model in its current state is only applicable by descriptive means and needs further development and validation in practical use cases.
Author(s)
Wang, Wei Min  
Fraunhofer-Institut für Produktionsanlagen und Konstruktionstechnik IPK  
Preidel, Maurice
Fachbach, Bernd
Stark, Rainer
Mainwork
Boosting collaborative networks 4.0: 21st IFIP WG 5.5 Working Conference on Virtual Enterprises, PRO-VE 2020. Proceedings  
Conference
Working Conference on Virtual Enterprises (PRO-VE) 2020  
Open Access
DOI
10.1007/978-3-030-62412-5_10
Additional full text version
Landing Page
Language
English
Fraunhofer-Institut für Produktionsanlagen und Konstruktionstechnik IPK  
Keyword(s)
  • Data provision

  • Reference model

  • Knowledge discovery

  • Networked engineering

  • Cookie settings
  • Imprint
  • Privacy policy
  • Api
  • Contact
© 2024