PLOP is a cost-based optimizer that finds optimal placements for semantic LLM operators in hybrid query plans via dynamic programming, delivering up to 1.5x speedup and 4.29x cost reduction on 44 benchmark queries while preserving accuracy.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
fields
cs.DB 3representative citing papers
Snowflake's Cortex AISQL adds native semantic operations to SQL via AI-aware optimization, adaptive model cascades, and semantic join rewriting, delivering 2-70x speedups in production workloads.
ScaleDoc achieves over 2x end-to-end speedup and up to 85% fewer LLM invocations for semantic predicates on large document collections via offline LLM representations, contrastive-trained proxy filtering, and adaptive cascades.
citing papers explorer
-
PLOP: Cost-Based Placement of Semantic Operators in Hybrid Query Plans
PLOP is a cost-based optimizer that finds optimal placements for semantic LLM operators in hybrid query plans via dynamic programming, delivering up to 1.5x speedup and 4.29x cost reduction on 44 benchmark queries while preserving accuracy.
-
Cortex AISQL: A Production SQL Engine for Unstructured Data
Snowflake's Cortex AISQL adds native semantic operations to SQL via AI-aware optimization, adaptive model cascades, and semantic join rewriting, delivering 2-70x speedups in production workloads.
-
ScaleDoc: Scaling LLM-based Predicates over Large Document Collections
ScaleDoc achieves over 2x end-to-end speedup and up to 85% fewer LLM invocations for semantic predicates on large document collections via offline LLM representations, contrastive-trained proxy filtering, and adaptive cascades.