The availability of water-related data from various sources presents a valuable opportunity to enhance data-driven decision-making and research in water management. However, these datasets often exist in diverse formats, such as CSV, JSON, and XML, making integration and interoperability a challenge. To address this, our approach focuses on transforming heterogeneous water data into RDF-based linked data, enabling seamless connectivity and interoperability across different datasets.
A crucial aspect of this transformation is the handling of time series data, which is essential for tracking changes in water resources over time. By developing an efficient mechanism to represent and process temporal data in RDF, we ensure accurate and meaningful integration of time-dependent information.
Finally, to enhance accessibility and real-time data exchange, we aim to publish the transformed data as a linked data event stream, allowing stakeholders to consume and utilize up-to-date water-related information dynamically. This approach aligns with FAIR data principles, promoting findability, accessibility, interoperability, and reusability of water data.