Quotation Sabou, Reka Marta, Ekaputra, Fajar J., Biffl, Stefan. 2017. Semantic Web Technologies for Data Integration in Multi-Disciplinary Engineering. In: Multi-Disciplinary Engineering for Cyber-Physical Production Systems, Hrsg. Biffl, S., Lüder, A., Gerhard, D. 301-329. Cham, Switzerland: Springer.




A key requirement in supporting the work of engineers involved in the design of Cyber-Physical Production Systems (CPPS) is offering tools that can deal with engineering data produced across the various involved engineering disciplines. Such data is created by different discipline-specific tools and is represented in tool-specific data models. Therefore, due to this data heterogeneity, it is challenging to coordinate activities that require project-level data access. Semantic Web technologies (SWTs) provide solutions for integrating and making sense of heterogeneous data sets and as such are a good solution candidate for solving data integration challenges in multi-disciplinary engineering (MDE) processes specific for the engineering of cyber-physical as well as traditional production systems. In this chapter, we investigate how SWTs can support multi-disciplinary engineering processes in CPPS. Based on CPPS engineering use cases, we discuss typical needs for intelligent data integration and access, and show how these needs can be addressed by SWTs and tools. For this, we draw on our own experiences in building Semantic Web solutions in engineering environments.


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Publication's profile

Status of publication Published
Affiliation External
Type of publication Chapter in edited volume
Language English
Title Semantic Web Technologies for Data Integration in Multi-Disciplinary Engineering
Title of whole publication Multi-Disciplinary Engineering for Cyber-Physical Production Systems
Editor Biffl, S., Lüder, A., Gerhard, D.
Page from 301
Page to 329
Location Cham, Switzerland
Publisher Springer
Year 2017
URL https://doi.org/10.1007/978-3-319-56345-9_12
Open Access N


Sabou, Reka Marta (Details)
Biffl, Stefan (TU Wien, Austria)
Ekaputra, Fajar J. (TU Wien, Austria)
Institute for Data, Process and Knowledge Management (AE Sabou) (Details)
Research areas (ÖSTAT Classification 'Statistik Austria')
1109 Information and data processing (Details)
1122 Artificial intelligence (Details)
1127 Information science (Details)
1138 Information systems (Details)
1140 Software engineering (Details)
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