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Modeling engineering knowledge explicitly and representing it by means of standardized modeling languages and in machine-understandable form enables advanced engineering processes in industrial and factory automation. This affects positively both process and product quality. In this paper we explore how the AutomationML format, an emerging data exchange standard, that supports the Industry 4.0 vision,...
In the context of large engineering projects the effective and efficient exchange and versioning of information from different engineering disciplines is essential. Semantic data integration approaches provide the necessary means to overcome the gap between heterogeneous local engineering tool concepts and common project-level concepts which enable the mapping of engineering data coming from different...
Large systems engineering projects involve the cooperation of various stakeholders from different engineering disciplines. Individual stakeholders apply various tools and related data storage approaches that (a) might hinder seamless interoperability and (b) include limited capability to support data versioning. Project-level concepts enable the mapping of engineering data coming from different disciplines...
Software-intensive systems in business information technology (IT) and industrial automation have become increasingly complex due to the need for more flexible system reconfiguration and business and engineering processes. Systems and software-engineering projects depend on the cooperation of experts from heterogeneous engineering domains using tools that were not designed to cooperate seamlessly...
Manufacturing systems engineering projects depend on contributions from several engineering disciplines. These contributions consist of complex artifacts like mechanical, electrical, and software components and plans. While the software tools are strong in supporting each individual engineering discipline, there is very little work on engineering processes automation across semantically heterogeneous...
The engineering of complex production automation systems involves experts from several backgrounds, such as mechanical, electrical, and software engineering. The production automation expert knowledge is embedded in their tools and data models, which are, unfortunately, insufficiently integrated across the expert disciplines, due to semantically heterogeneous data structures and terminologies. Traditional...
Open source software teams routinely develop complex software products in frequent-release settings with rather lightweight processes and project documentation. In this con-text project a major challenge for data collection is how to extract the relevant project management knowledge effectively and efficiently from a wide range of software project data sources, such as artifact versions, bug reports,...
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