STRATOS: Bridging the Symbolic-to-Numeric Gap in Spatio-Temporal Text-to-SQL for Meteorological Data
arXiv:2607.03501v1 Announce Type: cross Abstract: Copernicus, the European Union's Earth observation program, produces petabytes of Earth observation and climate data, offering immense potential for research, policy, and applications. However, access to these datasets requires advanced programming skills and familiarity with domain-specific formats such as NetCDF or GRIB. Moreover, general-purpose Text-to-SQL systems fail when applied naively to the meteorological domain due to a profound ``Symbolic-to-Numeric'' gap. To overcome these limitations, we present an end-to-end Text-to-SQL framework
The proliferation of Earth observation data combined with advancements in Text-to-SQL and domain-specific AI models is creating solutions for complex data access challenges.
This development allows a broader range of users, including policymakers and researchers without advanced programming skills, to access and leverage vast meteorological datasets, potentially accelerating climate and environmental insights.
Access to complex meteorological data moves from requiring highly specialized programming to more intuitive natural language interfaces, bridging a critical 'Symbolic-to-Numeric' gap.
- · Climate researchers
- · Environmental policymakers
- · Data scientists
- · European Union (Copernicus program)
- · Exclusive reliance on specialized programmers for data access
- · Current general-purpose Text-to-SQL systems
Non-expert users gain direct access to sophisticated meteorological datasets for analysis.
Faster and more widespread utilization of Earth observation data could lead to accelerated discovery and more informed policy decisions regarding climate change.
The success in meteorological data could spur similar 'Symbolic-to-Numeric' bridging solutions across other scientific and industrial domains with complex datasets, boosting broader AI agentic capabilities.
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Read at arXiv cs.AI