The Engineering Knowledge Base Approach
Systems and software engineering projects depend on the cooperation of experts from heterogeneous engineering domains using tools that were not designed to cooperate seamlessly. Current semantic engineering tool and data integration is often ad hoc and fr
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The Engineering Knowledge Base Approach Thomas Moser
Abstract Systems and software engineering projects depend on the cooperation of experts from heterogeneous engineering domains using tools that were not designed to cooperate seamlessly. Current semantic engineering tool and data integration is often ad hoc and fragile, thereby making the evolution of tools and the reuse of integration solutions across projects unnecessarily inefficient and risky. This chapter describes the engineering knowledge base (EKB) framework for engineering environment integration in multidisciplinary engineering projects. The EKB stores explicit engineering knowledge to support access to and management of engineering models across tools and disciplines. The following Chaps. 5–7 discuss individual aspects of the EKB framework, which provides (1) data integration based on mappings between local and domain-level engineering concepts; (2) transformations between local engineering concepts; and (3) advanced applications built on these foundations, e.g., end-to-end analyses. As a result, experts from different organizations may use their well-known tools and data models and can access data from other tools in their syntax. Typical applications enabled by implementations of this framework are discussed in Chaps. 9 and 10. Keywords Engineering knowledge base ⋅ Explicit knowledge ⋅ Data integration ⋅ Transformation ⋅ End-to-End analysis
4.1 Introduction Software-intensive systems in industrial automation become increasingly complex due to the need for flexibility of business and engineering processes (Schäfer and Wehrheim 2007). Such systems and software engineering projects bring together experts from several engineering domains and organizations, who work in a heterogeneous engineering environment with a wide range of models, processes, and tools T. Moser (✉) St. Pölten University of Applied Sciences, Matthias Corvinus-Straße 15, 3100 St. Pölten, Austria 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_4
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that were originally not designed to cooperate seamlessly, but specifically designed for a task or a single engineering discipline. A core question is how to integrate data across engineering tools and domain boundaries. Current semantic engineering environment integration is often ad hoc and fragile, making the evolution of tools and reuse of integration solutions across projects risky (Halevy 2005; Noy et al. 2005). In order to reach the common goal of developing automation and control software in the engineering team, it is important to share the necessary knowledge between engineering domain experts (Schäfer and Wehrheim 2007). However, this knowledge is often only implicitly available and therefore inefficient to share, resulting in timeconsuming repetitive tasks; often it is hard or even impossible to create and maintain common shared knowledge repositories. A met
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