{'fraction_non_alphanumeric': 0.02313475997686524, 'fraction_numerical': 0.0, 'mean_word_length': 5.7315175097276265, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Flexible Multi-aspect Model Integration for Cyber-Physical Production Systems Engineering

. Background. Consistent cross-disciplinary engineering data models have become increasingly important for engineers and project managers to validate system designs or implement new features in existing systems. However, discipline-speciﬁc designs (mechanical, electrical, automation engineering etc.) in isolated data models and proprietary software tools often create information silos. Similar to information systems, the challenges in Cyber-Physical Production System (CPPS) are a high amount of heterogeneous data that needs to be analysed and accessible for stakeholders and systems. Aim. The goal of the Flexible Multi-aspect Model Integration project is to support the integration of local engineering views and artefacts using the deﬁnition of common concepts across diﬀerent disciplines. Therefore, the thesis project will provide capabilities to integrate and validate multi-aspect models more eﬃciently to increase the data quality. Method. The project will follow Design Science methodology to design and evaluate i) a method for collecting and deﬁning common concepts across engineering disciplines, ii) a modu-larised software system design that enables ﬂexible model integration processes in a CPPS context, and iii) an exemplary model integration process that supports data integration needs in the planning, operation, and analytics phase. The model integration processes are evaluated with real-world uses cases from industry. Conclusion. The information systems community will gain insight into the requirements in engineering and a method for agreeing on an inter-disciplinary common understanding from this research.