A Framework for Transforming Data Tables into Descriptive Models
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Typ | Bachelorarbeit oder Masterarbeit |
|---|---|---|
| Aushang | DatatoModelThesis.pdf | |
| Betreuer | Wenden Sie sich bei Interesse oder Fragen bitte an: Raziyeh Dehghani (E-Mail: raziyeh.dehghani@kit.edu) |
Motivation
Cyber-physical systems (CPS) produce vast amounts of observational data from interconnected software and physical components. This data must be aligned to ensure system consistency. This thesis proposes a mechanism to transform raw data tables into descriptive models. These models will help maintain the consistency between observation metamodels and design metamodels. The research builds on an existing EMF-based project for preserving metamodel consistency. It aims to extend the project’s capabilities by enabling the extraction of models from raw data. These models will then be transformed into corresponding metamodels, facilitating the effective application of consistency preservation rules.
Tasks
- Develop methods to preprocess raw CPS observational data and extract meaningful models.
- Design mechanisms to convert extracted models into their corresponding metamodels, ensuring alignment with the intended structure and semantics.
- Integrate the model extraction and transformation functionalities into the existing EMF-based consistency preservation framework.
- Provide practical examples to demonstrate the applicability of the proposed mechanisms in CPSs.
Benefits
- Working with cutting-edge and innovative technologies
- Close connection to ongoing/current research project
- Excellent working environment and intensive support