面向企业ERP系统的RAG驱动数据质量治理
RAG-Driven Data Quality Governance for Enterprise ERP Systems
AI总结：
本文提出一种基于RAG的数据质量治理方法，通过自动化清洗与LLM生成SQL查询，提升企业ERP系统中多语言数据处理效率与准确性。
摘要：
企业ERP系统管理着数以万计的员工记录，当人力资源部门在多种语言中进行分散的手动录入时，面临严峻的数据质量挑战。我们提出一个端到端的流程，结合自动数据清洗与LLM驱动的SQL查询生成，该流程在管理240,000名员工记录的生产系统上部署了六个月。该系统分为两个集成阶段：一个多阶段清洗流程，在定期从Microsoft SQL Server到PostgreSQL的同步过程中执行翻译规范化、拼写纠正和实体去重；以及一个由GPT-4o驱动的检索增强生成框架，该框架能够将土耳其语、俄语和英语中的自然语言问题转换为经过验证的SQL查询。查询引擎采用LangChain编排、FAISS向量相似性搜索以及500多个经过验证的示例的少样本学习。我们的评估显示，在2,847个生产查询中，查询有效性为92.5%，模式兼容性为95.1%，语义准确性为90.7%。系统将查询处理时间从2.3天减少到不到5秒，同时保持99.2%的可用性，GPT-4o在延迟和成本方面分别比GPT-3.5低46%和68%。这种模块化架构提供了一个可重复使用的框架，用于AI原生的企业数据治理，展示了在企业规模上的实际可行性，用户满意度为4.3/5.0。
英文摘要：
Enterprise ERP systems managing hundreds of thousands of employee records face critical data quality challenges when human resources departments perform decentralized manual entry across multiple languages. We present an end-to-end pipeline combining automated data cleaning with LLM-driven SQL query generation, deployed on a production system managing 240,000 employee records over six months. The system operates in two integrated stages: a multi-stage cleaning pipeline that performs translation normalization, spelling correction, and entity deduplication during periodic synchronization from Microsoft SQL Server to PostgreSQL; and a retrieval-augmented generation framework powered by GPT-4o that translates natural-language questions in Turkish, Russian, and English into validated SQL queries. The query engine employs LangChain orchestration, FAISS vector similarity search, and few-shot learning with 500+ validated examples. Our evaluation demonstrates 92.5% query validity, 95.1% schema compliance, and 90.7\% semantic accuracy on 2,847 production queries. The system reduces query turnaround time from 2.3 days to under 5 seconds while maintaining 99.2% uptime, with GPT-4o achieving 46% lower latency and 68% cost reduction versus GPT-3.5. This modular architecture provides a reproducible framework for AI-native enterprise data governance, demonstrating real-world viability at enterprise scale with 4.3/5.0 user satisfaction.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
