KathDB让数据库支持多模态查询，还能解释结果并人机协作。
KathDB: Explainable Multimodal Database Management System with Human-AI Collaboration
- 融合关系型语义与大模型推理，处理文本图像等多模态数据。
- 支持用户在解析、执行、解释阶段与AI交互，获得可解释答案。
- 适合需要透明性与协作的多模态数据分析场景。
传统数据库管理系统（DBMS）在关系型数据上执行用户提供的SQL查询，具备强语义保证和高级查询优化能力，但编写复杂SQL困难，且仅限结构化表格。现代多模态系统（操作关系数据的同时也包含文本、图像甚至视频）要么暴露底层控制，迫使用户手动编写或创建机器学习用户自定义函数（UDF），要么将执行完全交由黑箱大语言模型（LLM），牺牲可用性或可解释性。我们提出KathDB，一种结合关系型语义与基础模型对多模态数据的推理能力的新系统。此外，KathDB在查询解析、执行和结果解释阶段引入人机交互通道，使用户能跨数据模态迭代获取可解释的答案。
原文摘要 · Abstract (English)
Traditional DBMSs execute user- or application-provided SQL queries over relational data with strong semantic guarantees and advanced query optimization, but writing complex SQL is hard and focuses only on structured tables. Contemporary multimodal systems (which operate over relations but also text, images, and even videos) either expose low-level controls that force users to use (and possibly create) machine learning UDFs manually within SQL or offload execution entirely to black-box LLMs, sacrificing usability or explainability. We propose KathDB, a new system that combines relational semantics with the reasoning power of foundation models over multimodal data. Furthermore, KathDB includes human-AI interaction channels during query parsing, execution, and result explanation, such that users can iteratively obtain explainable answers across data modalities.
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