AI RESEARCH
Agentic Cost-Aware Query Planning with Knowledge Distillation for Big Data Analytics
arXiv CS.LG
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ArXi:2605.17831v1 Announce Type: new Query optimization in big data analytics remains computationally expensive, particularly for resource-constrained environments where traditional optimizers fail to satisfy memory and latency constraints. We present an agentic query planning system that combines a rule-based teacher planner, UCB1 bandit exploration, cost-aware prediction, and knowledge distillation to a lightweight student planner.