Smart Compaction: Predicting Compaction Utility from Lakehouse Table Metadata

arXiv:2608.08639 · cs.AI · Submitted 2026-08-09 · Read on arXiv

Jannic Cutura, Subash Prakash

cs.AI

Submitted: 2026-08-09

Updated: 2026-08-11

Code: https://github.com/JannicCutura/smart-compaction

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

XGBoost — 22× in this paper · explained in 3 episodes
Benchmark — 7× in this paper · explained in 2 episodes
Ablation — 5× in this paper · explained in 1 episode
Accuracy — 4× in this paper · explained in 1 episode
Benchmarking — 3× in this paper · explained in 4 episodes
Robustness — 3× in this paper · explained in 6 episodes
AUC — 2× in this paper · explained in 2 episodes
Clustering — 2× in this paper · explained in 2 episodes
Feature Importance — 2× in this paper · explained in 2 episodes
Papers — 2× in this paper · explained in 3 episodes
Reinforcement Learning — 2× in this paper · explained in 4 episodes
Ablation Studies — 1× in this paper · explained in 1 episode
Class Imbalance — 1× in this paper · explained in 3 episodes
Conference on Innovative Data Systems Research — 1× in this paper
Contextual Bandit — 1× in this paper · explained in 2 episodes
Contextual Bandits — 1× in this paper · explained in 3 episodes
Core — 1× in this paper · explained in 2 episodes
Deep Reinforcement Learning — 1× in this paper · explained in 3 episodes
False Negatives — 1× in this paper · explained in 2 episodes
Feature Engineering — 1× in this paper · explained in 3 episodes
Feature Extraction — 1× in this paper · explained in 3 episodes
Hyperparameters — 1× in this paper · explained in 2 episodes
Overfitting — 1× in this paper · explained in 2 episodes
Very Large Data Bases — 1× in this paper

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