Linked Data as Integrating Technology for Industrial Data

  • Authors:

  • Markus Graube;Johannes Pfeffer;Jens Ziegler;Leon Urbas

  • Affiliations:

  • Technische Universität Dresden, Germany;Technische Universität Dresden, Germany;Technische Universität Dresden, Germany;Technische Universität Dresden, Germany

  • Venue:
  • International Journal of Distributed Systems and Technologies
  • Year:
  • 2012

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Abstract

In a globalised world the process industry faces challenges regarding data management. Rising demands for agility and rapid shortening of innovation cycles have lead to project-based collaborations. Highly specialised small and medium enterprises are forming "virtual companies" for their mutual benefit. However, today's industrial data structures are very heterogeneous, complicating collaborative work and hindering the flow of data between stakeholders from different domains. Existing solutions are too rigid and potentially cumbersome. A broad gap still exists between the need of virtual companies to share data from mixed sources in a controlled way and the technologies available. The authors' approach uses semantic web technologies to represent industrial data in a generic way. Major advantages in comparison to traditional approaches arise from the inherent merging abilities and the extensibility of Linked Data. Distributed information spaces from different domains can be condensed into an interlinked cloud. Existing data can be integrated either on-the-fly using appropriate adapters or by complete migration. Furthermore, operations from graph theory can be performed on the Linked Data networks to generate aggregated views. This article discusses a set of proven web technologies for cloud-driven industrial data sharing in virtual companies and presents first results.