TL;DR
It is argued that the next generation of systems for semantic query optimization must move from optimized execution of fixed semantic plans toward cost-aware execution of agentic analytics workflows.
Carnot is an interactive execution engine for AI-driven analytics that compiles natural language requests into physical execution graphs and surfaces them through an interactive notebook interface, and Carnot's query optimizer will optimize the query with respect to cost or latency constraints provided by the user.
CADAQUES is an open-source Python framework built on one architectural principle: cost is a first-class primitive of the discovery loop, which separates the loop into two structural protocols, an Oracle that answers queries and a Driver that proposes them, and charges both evaluations and decisions against a common vec...
It is found that although the queries triggering slow planning are largely DBMS-specific, recurring pathologies involving correlated subqueries, CTE expansion, repeated subquery expressions, disjunctive joins, and constant folding affect multiple systems.
This work proposes ProRetrieval, which recasts the language model as a retrieval orchestrator: given a natural-language query, it synthesizes an executable program in a hybrid DSL interleaving SQL operators over structured fields with vector-retrieval primitives over text and images, with SQL itself providing the logic...
LLM-QOpt++ is presented, a novel hybrid, confidence-aware query optimization framework that unifies traditional CBO estimation, machine learning–based cost prediction, and large language model (LLM) reasoning within a single adaptive pipeline.
DeQL (Decision Query Language) is demonstrated, a declarative SQL extension where users state, over relational data, what to decide, what constraints to respect, and what to optimize; the DeQL Planner chooses the formulation.
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