arXiv:2508.17693v2 Announce Type: replace-cross Abstract: Database normalization is crucial to preserving data integrity. However, it is time-consuming and error-prone, as it is typically performed manually by data engineers. To this end, we present Miffie, a database normalization framework that leverages the capability of large language models. Miffie enables automated data normalization without human effort while preserving high accuracy. The core of Miffie is a dual-model self-refinement architecture that combines the best-performing models for normalized schema generation and verification
The proliferation of complex data environments and the advancement of large language models are converging, making automated solutions for data management increasingly viable and necessary.
Automating the labor-intensive and error-prone process of database normalization with AI can significantly improve data integrity and development efficiency for organizations.
The reliance on manual data engineers for database normalization may decrease as AI-driven frameworks like Miffie demonstrate high accuracy and efficiency.
- · AI software developers
- · Data-intensive businesses
- · Cloud service providers
- · Software engineers
- · Entry-level data engineers
- · Consulting firms specializing in manual data normalization
Companies will experience faster time-to-market for applications requiring robust data models due to accelerated normalization processes.
A shift in demand for data professionals towards roles focused on AI model oversight, data governance, and complex schema design rather than manual normalization.
Increased adoption of AI tools could lead to a broader rethink of data management best practices, potentially enabling new, more flexible data architectures.
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Read at arXiv cs.AI