Novel environmental big data grid integration and interoperability model

Currently, effectively managing, retrieving, and applying environmental big data (EBD) presents a considerable challenge owing to the abundant influx of heterogeneous, fragmented, and real-time information. The existing network domain name system lacks the spatial attribute mining necessary for handling EBD, while the geographic region name system proves inadequate in achieving EBD interoperability. EBD integration faces challenges arising from diverse sources and formats. Interoperability gaps hinder seamless collaboration among systems, impacting the efficiency of data analysis.

To address the need for unified organization of EBD, precise man-machine collaborative spatial cognition, and EBD interoperability, this paper introduces the EBD grid region name model based on the GeoSOT global subdivision grid framework (EGRN-GeoSOT). EGRN-GeoSOT effectively manages location identification codes from various sources, ensuring the independence of location identification while facilitating correlation, seamless integration, and spatial interoperability of EBD. The model comprises the grid integration method of EBD (GIGE) and the grid interoperability method of EBD (GIOE), providing an approach to enhance the organization and interoperability of diverse environmental datasets. By discretizing the Earth's surface into a uniform grid, GIGE enables standardized geospatial referencing, simplifying data integration from various sources. The integration process involves the aggregation of disparate environmental data types, including satellite imagery, sensor readings, and climate model outputs. GIGE creates a unified representation of the environment, allowing for a comprehensive understanding of complex interactions and patterns. GIOE ensures interoperability by providing a common spatial language, facilitating the fusion of heterogeneous environmental datasets. The multi-scale characteristic of GeoSOT allows for scalable adaptability to emerging environmental monitoring needs.

EGRN-GeoSOT establishes a standardized framework that enhances integration, promotes interoperability, and empowers collaborative environmental analysis. To verify the feasibility and retrieval efficiency of EGRN-GeoSOT, Oracle and PostgreSQL databases were combined and the retrieval efficiency and database capacity were compared with the corresponding spatial databases, Oracle Spatial and PostgreSQL + PostGIS, respectively. The experimental results showed that EGRN-GeoSOT not only ensures a reasonable capacity consumption of the database but also has higher retrieval efficiency for EBD.