A Structured Approach for Building Descriptive Models from Data

Vortragende(r)	Philipp Meyer
Vortragstyp	Bachelorarbeit
Betreuer(in)	Raziyeh Dehghani
Termin	Fr 13. März 2026, 11:30 (Raum 010 (Gebäude 50.34))
Vortragssprache	Englisch
Vortragsmodus	in Präsenz
Kurzfassung	The development of Cyber-Physical Systems (CPS) is characterized by a high degree of complexity and requires continuous optimization throughout the entire development process. The feedback cycles of the MODA framework are ideal for systematically controlling these adjustments. However, their effective use requires that descriptive models can be derived from runtime data. Established approaches to model derivation, however, were primarily designed for other domains and applications. Against this background, this work develops an automated pipeline to extract descriptive models from raw data and systematically evaluates the suitability of various modeling approaches for the domain of cyber-physical systems. A central element of the solution approach is the integration of the analysis results into a standardized metamodel based on the Structured Metrics Metamodel in order to give the raw data a semantic structure and ensure interoperability for downstream MDD tools. To objectively evaluate the results, a dedicated evaluation framework was developed that compares the various approaches using quantitative metrics and qualitative expert feedback. The evaluation confirms that the automated derivation of statistical parameters, segmentations, and discrete system states delivers robust results. In contrast, limitations were identified in the generation of complex process models using process mining, as the conversion of continuous physical signals into discrete logic remains a challenge. Overall, the work demonstrates as a proof of concept how the gap between collected runtime data and formal models can be closed, thus providing a technological basis for MODA feedback cycles in CPS development.