EnumGRPO is a self-improving optimizer for agentic query execution that reduces LLM-operator costs by ~317x while improving accuracy by 18% over a hybrid baseline across four databases.
VLDB Endow.14, 1 (2020), 50–60
5 Pith papers cite this work, alongside 294 external citations. Polarity classification is still indexing.
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representative citing papers
ASMR extracts concepts with an LLM, clusters them into candidate fields, then uses RL to select compact non-redundant schemas for each ship-report form type.
BaCon combines factorized join computation with workload-aware domain quantization to evaluate batches of counting queries 2×–178× faster than independent or post-filtering baselines, without modifying DBMS internals.
Query-driven table integration that uses Steiner-tree search to choose which joins LLMs must verify, reporting 30%+ accuracy gains at 5x lower LLM cost.
MoRER builds an ER model repository via feature distribution clustering of tasks, achieving competitive results with limited labels versus active learning, transfer learning, and self-supervised methods on three multi-source datasets.
citing papers explorer
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Cost-Aware Optimization for Agentic Query Execution
EnumGRPO is a self-improving optimizer for agentic query execution that reduces LLM-operator costs by ~317x while improving accuracy by 18% over a hybrid baseline across four databases.
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ASMR: Agentic Schema Generation for Ship Maintenance Report Writing
ASMR extracts concepts with an LLM, clusters them into candidate fields, then uses RL to select compact non-redundant schemas for each ship-report form type.
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BaCon: Efficient Batch Processing of Counting Queries [Full Version]
BaCon combines factorized join computation with workload-aware domain quantization to evaluate batches of counting queries 2×–178× faster than independent or post-filtering baselines, without modifying DBMS internals.
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EcoTable: Cost-effective Table Integration in Data Lakes for Natural Language Queries
Query-driven table integration that uses Steiner-tree search to choose which joins LLMs must verify, reporting 30%+ accuracy gains at 5x lower LLM cost.
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Efficient Model Repository for Entity Resolution: Construction, Search, and Integration
MoRER builds an ER model repository via feature distribution clustering of tasks, achieving competitive results with limited labels versus active learning, transfer learning, and self-supervised methods on three multi-source datasets.