KathDB-FAO: Synthesized Query Plans in a Multimodal DBMS

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.

Databases (cs.DB); Artificial Intelligence (cs.AI)

arXiv:2609.28761 [cs.DB]

(or arXiv:2609.28761v1 [cs.DB] for this version)

https://doi.org/10.48550/arXiv.2609.28761 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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