Database Federation for Spatial Digital Twins with Semantically Enhanced Data Processing: A use case of Mission Mjøsa

Abstract

Developing spatial digital twins (SDTs) for inland water bodies requires addressing challenges such as temporal variability, dynamic water surface conditions, and heterogeneous water datasets. This study presents a semantic integration framework combining Ontology-Based Data Access (OBDA) with PostgreSQL database federation to support real-time decision-making in SDT applications. The framework integrates hydrological data, sensor networks, bathymetric data, and other static datasets under a unified ontology, enabling federated SPARQL queries without centralizing data. A 3D visualization interface allows interactive analysis of results from the SPARQL queries. Demonstrated through the Mission Mjøsa use case, this approach supports inland water mapping and decision support by aligning spatial data across domains. The framework offers a scalable solution for water data processing, semantic fusion, and scenario-based simulation, contributing to the development of intelligent, semantically enriched SDTs for sustainable environment management.