Publication at Charles University, Faculty of Mathematics and Physics |
2024
Abstract
Correctly and completely describing the structure and semantics of data is laborious but necessary for complex domains, critical systems, or interoperability at the national and international levels where potential errors from misinterpretation of data could be costly. However, traditional data modeling processes often do not consider more complex approaches where structure and semantics may be designed separately, derived from already existing models, or composed from multiple models from different teams.
We propose a robust framework inspired by a model-driven approach to support and ease the development and management of data specifications mapped to semantically or structurally richer models and keep them consistent through evolution.