When Plans Change Answers: Formalizing Cost-Accuracy Optimization for Semantic Queries
In semantic query engines, predicates are evaluated by machine-learned models, and the choice of a query plan affects not only the cost of a query but also its result.
ProofPaper ↗
Key points
- We give a formal problem definition for cost-accuracy optimization of such queries.
- Building on this, we define an oracle semantics for relational algebra with semantic operators, physical plans as pairs of a logical plan and a decision policy, declarative output-level targets, and a hierarchy of plan equivalence.
- We show that accuracy is plan-invariant under pointwise-deterministic policies, and that selection pushdown is not quality-sound when escalation bands are calibrated on the plan's own candidates.
- Expected quality can be computed in polynomial time under bag semantics; under set semantics it follows the dichotomy of tuple-independent probabilistic databases when every relation carries a semantic predicate.
Sources (1)
- [1]When Plans Change Answers: Formalizing Cost-Accuracy Optimization for Semantic QueriesarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 6, 10:19 AM
In semantic query engines, predicates are evaluated by machine-learned models, and the choice of a query plan affects not only the cost of a query but also its result.
We give a formal problem definition for cost-accuracy optimization of such queries.
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