Towards Agentic Schema Refinement
Fuente:
arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866912151706271744 |
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| author | Rissaki, Agapi Fountalis, Ilias Vasiloglou, Nikolaos Gatterbauer, Wolfgang |
| author_facet | Rissaki, Agapi Fountalis, Ilias Vasiloglou, Nikolaos Gatterbauer, Wolfgang |
| contents | Large enterprise databases can be complex and messy, obscuring the data semantics needed for analytical tasks. We propose a semantic layer in-between the database and the user as a set of small and easy-to-interpret database views, effectively acting as a refined version of the schema. To discover these views, we introduce a multi-agent Large Language Model (LLM) simulation where LLM agents collaborate to iteratively define and refine views with minimal input. Our approach paves the way for LLM-powered exploration of unwieldy databases. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_07786 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards Agentic Schema Refinement Rissaki, Agapi Fountalis, Ilias Vasiloglou, Nikolaos Gatterbauer, Wolfgang Databases Artificial Intelligence Large enterprise databases can be complex and messy, obscuring the data semantics needed for analytical tasks. We propose a semantic layer in-between the database and the user as a set of small and easy-to-interpret database views, effectively acting as a refined version of the schema. To discover these views, we introduce a multi-agent Large Language Model (LLM) simulation where LLM agents collaborate to iteratively define and refine views with minimal input. Our approach paves the way for LLM-powered exploration of unwieldy databases. |
| title | Towards Agentic Schema Refinement |
| topic | Databases Artificial Intelligence |
| url | https://arxiv.org/abs/2412.07786 |