Semantic Data Integration: Tools and Architectures
This chapter is focused on the technical aspects of semantic data integration that provides solutions for bridging semantic gaps between common project-level concepts and the local tool concepts as identified in the Engineering Knowledge Base (EKB). Based
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Semantic Data Integration: Tools and Architectures Richard Mordinyi, Estefania Serral and Fajar Juang Ekaputra
Abstract This chapter is focused on the technical aspects of semantic data integration that provides solutions for bridging semantic gaps between common project-level concepts and the local tool concepts as identified in the Engineering Knowledge Base (EKB). Based on the elicitation of use case requirements from automation systems engineering, the chapter identifies required capabilities an EKB software architecture has to consider. The chapter describes four EKB software architecture variants and their components, and discusses identified drawbacks and advantages regarding the utilization of ontologies. A benchmark is defined to evaluate the efficiency of the EKB software architecture variants in the context of selected quality attributes, like performance and scalability. Main results suggest that architectures relying on a relational database still outperform traditional ontology storages while NoSQL databases outperforms for query execution.
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Keywords Ontology Semantic data integration Multidisciplinary projects
⋅ Versioning ⋅ Performance ⋅
R. Mordinyi (✉) ⋅ F.J. Ekaputra Institute of Software Technology and Interactive Systems, CDL-Flex, Vienna University of Technology, Vienna, Austria e-mail: [email protected] F.J. Ekaputra e-mail: [email protected] E. Serral Leuven Institute for Research on Information Systems (LIRIS), KU Leuven, Naamsestraat 69, 3555, 3000 Louvain, Belgium e-mail: [email protected] © Springer International Publishing Switzerland 2016 S. Biffl and M. Sabou (eds.), Semantic Web Technologies for Intelligent Engineering Applications, DOI 10.1007/978-3-319-41490-4_8
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Introduction
In large-scale systems engineering projects, like power plants, steel mills, or car manufactures, the seamless cooperation and data exchange of expert knowledge from various engineering domains and organizations is a crucial success factor (Biffl et al. 2009a). This environment consists of a wide range of engineering systems and tools that differ in the underlying technical platforms and the used data models. Each domain or organization usually prefers using their own well-known models, from now on referred as local tool models. In order to successfully develop projects, it is essential to integrate important knowledge of different domain experts. However, these experts usually prefer using their well-known local tool models. In addition, they want to access data from other tools within their local data representation approach (Moser and Biffl 2012). The standardization of data interchange is one of the most promising approaches (Wiesner et al. 2011) to enable efficient data integration that allows experts to continue using their familiar data models and formats. This approach is based on agreeing on a minimal common model for data exchange that represents the common concepts shared among different disciplines on project level. Chapter
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