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The timely discovery, sharing and integration of architectural knowledge (AK) have become critical aspects in enabling the software architects to make meaningful conceptual and technical design decisions and trade-offs. In large-scale organizations particular obstacles in making AK available to architects are a heterogeneous pool of internal and external knowledge sources, poor interoperability between...
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...
In production system engineering, the machine-understandable definition of relations between engineering information views is important to enable automating dependency checking between these views. Unfortunately, in automation engineering there is no standardized representation of relations and dependencies, which makes it difficult to automate consistency checking. In this paper we derive requirements...
In a multi-disciplinary engineering project, such as the parallel engineering of industrial production plants, domain experts want to efficiently monitor project-level constraints that depend on technical parameter values in local engineering models. However, the heterogeneous representations of constraint parameters in these engineering models make the automation of constraint monitoring difficult...
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...
Automation systems engineering projects depend on contributions from several engineering disciplines. These contributions consist of complex artifacts like mechanical, electrical, and software components and plans, which get updated concurrently. While there are version management features in the software tools for each individual engineering discipline, there is very little work on version management...
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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