KathDB-FAO: Synthesized Query Plans in a Multimodal DBMS

2026-09-23

AI Summary

This study addresses the inherent challenge of balancing execution efficiency and accuracy in natural language database querying. To this end, it proposes a multimodal database query subsystem that translates natural language into execution plans composed of dynamically synthesized functions, thereby achieving query-level optimization. The core innovation lies in extracting atomic actions, establishing input-output contracts, and synthesizing functions on the fly, effectively integrating techniques from natural language processing, program synthesis, and database query optimization. Evaluated on the SemBench benchmark, the proposed approach reduces execution costs by an average of 58.8% while maintaining comparable or superior query quality. These results demonstrate that the method effectively enables both efficient and accurate natural language interfaces to databases.

Abstract

We design, implement, and evaluate KathDB-FAO, a new query evaluation subsystem for our KathDB multimodal DBMS. KathDB-FAO takes as input a query in natural language (NL) and converts it into a query execution plan where each operator is a function whose body is synthesized during query evaluation, which allows powerful query-specific optimizations. To generate accurate and efficient plans from NL, KathDB-FAO first extracts fine-grained atomic actions for correctness, then establishes contracts on the inputs and outputs of those actions and groups them for efficiency, and finally synthesizes the function for each group on the fly. On SemBench, KathDB-FAO cuts execution cost by 58.8% on average across scenarios compared with the next best system, at comparable or better quality.

Problem

• multimodal DBMS
• natural language query
• query execution plan
• function synthesis
• query optimization

Innovation

• Multimodal DBMS
• Natural Language Query
• Query Plan Synthesis
• Dynamic Function Synthesis
• Fine-grained Atomic Actions