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Linked Data as Integrating Technology for Industrial Data
Linked Data as Integrating Technology for Industrial Data,10.1109/NBiS.2011.33,Markus Graube,Johannes Pfeffer,Jens Ziegler,Leon Urbas
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Linked Data as Integrating Technology for Industrial Data
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Markus Graube
,
Johannes Pfeffer
,
Jens Ziegler
,
Leon Urbas
In a globalised world the process industry faces great 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 so-called virtual companies to profit from each other. Today, however, industrial data struc- tures are characterised by high heterogeneity. This complicates
collaborative work
and hinders the flow of data between different stakeholders from various domains. Existing solutions are much too rigid and potentially cumbersome. There still is a broad gap between the need of virtual companies to share data from mixed sources in a controlled way and the available technologies to accomplish this. Our approach to close this gap is the usage of
semantic web
technologies for representing industrial data in a generic way. Major advantages in comparison to traditional approaches arise from the inherent merging abilities and from the extensibility of Linked Data. Distributed information spaces from different do- mains 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 paper discusses a set of proven web technologies for cloud-driven industrial
data sharing
in virtual companies and presents preliminary results. Index Terms—Semantic Web; Linked Data; Meta-Ontologies; Virtual Companies; Process Industry; Knowledge Management; Graph Theory; Data Clouds;
Conference:
Network-Based Information Systems - NBiS
, pp. 162-167, 2011
DOI:
10.1109/NBiS.2011.33
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References
(17)
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Citations: 2
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Martin Hepp
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IEEE International Conference on e-Business Engineering - ICEBE
, pp. 535-540, 2005
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DSNotify - Detecting and Fixing Broken Links in Linked Data Sets
(
Citations: 4
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Bernhard Haslhofer
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Conference:
Database and Expert Systems Applications - DEXA
, pp. 89-93, 2009