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About this paper
Tianqing Wang, Xun Xue, Guoliang Li, Yong Wang
AnDB is introduced as an AI-native database designed for unified semantic analysis across structured and unstructured data, enabling users to perform complex AI-driven queries using extended SQL-like statements. The system demonstrates capabilities in classification, aggregation, and cross-source trend analysis of text documents through a cost-accuracy optimized query engine.
Key insights
- Extending SQL with semantic-specific tokens and corresponding logical/physical operators allows users to express complex AI-driven analysis directly, bridging the semantic gap for unstructured data within a familiar database interface.
- A comprehensive query optimization framework that explicitly models the trade-off between financial cost, accuracy, and runtime is essential for practical and efficient deployment of AI-native database systems.
- Incorporating a calibration component, inspired by agentic AI frameworks, enables continuous refinement of system performance through both user feedback and automated model-based cross-validation.
Summary by alphaXiv.
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