提出可兼容SQL的语义文档处理系统，统一查询逻辑与优化
SABER: A SQL-Compatible Semantic Document Processing System Based on Extended Relational Algebra
- 基于扩展关系代数构建语义操作的统一代数体系
- 支持结构化与非结构化数据混合查询，实现逻辑计划优化
- 提供开放接口，便于社区集成不同语义算子
大型语言模型（LLMs）催生了新一代语义数据处理系统（SDPS），支持对非结构化文档的声明式查询。然而现有SDPS缺乏统一的代数基础，导致查询难以组合、推理与优化。本文提出一种新语义代数SABER（基于扩展关系代数的语义代数），首次实现语义操作的逻辑计划构建、优化与形式化正确性保证。进一步将SABER实现为兼容SQL的语法，原生支持混合结构化/非结构化数据处理。通过SABER，我们展示了为现有SDPS提供统一接口的可行性，使其能有效混用任意语义兼容的算子实现，显著提升系统对社区贡献的适用性。
原文摘要 · Abstract (English)
The emergence of large-language models (LLMs) has enabled a new class of semantic data processing systems (SDPSs) to support declarative queries against unstructured documents. Existing SDPSs are, however, lacking a unified algebraic foundation, making their queries difficult to compose, reason, and optimize. We propose a new semantic algebra, SABER (Semantic Algebra Based on Extended Relational algebra), opening the possibility of semantic operations' logical plan construction, optimization, and formal correctness guarantees. We further propose to implement SABER in a SQL-compatible syntax so that it natively supports mixed structured/unstructured data processing. With SABER, we showcase the feasibility of providing a unified interface for existing SDPSs so that it can effectively mix and match any semantically-compatible operator implementation from any SDPS, greatly enhancing SABER's applicability for community contributions.
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