ReSequel uses LLMs guided by metadata-derived templates and sampling-based verification to rewrite SQL queries, delivering up to 16x workload speedups over native DBMSs and 22x over prior LLM baselines across eight benchmarks and three systems.
Agarwal, Ashish Mittal, Saksham Chintalapani, Rekha Singhal, and Biswapesh Chatterjee
3 Pith papers cite this work. Polarity classification is still indexing.
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BACC achieves mean absolute compliance gaps of 0.44 and 0.42 percentage points on Azure Functions traces by separating prediction, ACI-based calibration, and PI-based budget-paced control for horizontal autoscaling.
Presto is extended to GPU-aware execution using cuDF experiments on TPC-H, delivering up to 6x cost/performance gains over CPU Presto via optimized data paths and inter-operator communication.
citing papers explorer
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ReSequel: Robust LLM-assisted Query Rewriting and Optimization using Templatization and Sampling
ReSequel uses LLMs guided by metadata-derived templates and sampling-based verification to rewrite SQL queries, delivering up to 16x workload speedups over native DBMSs and 22x over prior LLM baselines across eight benchmarks and three systems.
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BACC: Budget-Aware Calibration and Control for Horizontal Autoscaling
BACC achieves mean absolute compliance gaps of 0.44 and 0.42 percentage points on Azure Functions traces by separating prediction, ACI-based calibration, and PI-based budget-paced control for horizontal autoscaling.
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Accelerating Presto with GPUs
Presto is extended to GPU-aware execution using cuDF experiments on TPC-H, delivering up to 6x cost/performance gains over CPU Presto via optimized data paths and inter-operator communication.