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Semantic and syntactic interoperability in online processing of big Earth observation data.
Martin Sudmanns1, Dirk Tiede1, Stefan Lang1
1Department of Geoinformatics - Z_GIS, University of Salzburg, Salzburg, Austria.
This study introduces a system for big Earth observation (EO) data processing, enabling both semantic and syntactic interoperability. It allows experts and non-experts to collaboratively analyze complex spatio-temporal data online.
Area of Science:
- Geospatial Science
- Data Science
- Computer Science
Background:
- Big Earth observation (EO) data requires robust interoperability for comprehensive and reproducible online processing.
- Extracting valuable information from multi-temporal gridded datasets necessitates both syntactic and semantic interoperability.
Purpose of the Study:
- To propose a system that integrates world models (semantic interoperability) with OGC Web Processing Services (syntactic interoperability) for online semantic analyses.
- To enable technical interoperability via a standardized interface for diverse clients and foster collaborative analysis among domain experts.
Main Methods:
- Wrapping world models into OGC Web Processing Services to achieve semantic and syntactic interoperability.
- Utilizing a centralized storage for 3D spatio-temporal data cubes, connected to world models online.
- Demonstrating usability with Earth observation data and non-EO gridded datasets like CORINE land cover.
Main Results:
- The system facilitates online semantic analyses by formally describing spatio-temporal entities and their relationships.
- It enables collaborative development of complex analyses by experts from different domains.
- Non-experts can extract valuable information from EO data by ignoring data management and software complexities.
Conclusions:
- The proposed system effectively addresses the challenge of interoperability in big Earth observation data processing.
- It enhances collaboration and accessibility for analyzing complex spatio-temporal datasets.
- The system proves versatile for various gridded, multi-temporal datasets beyond traditional EO applications.
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