Query performance prediction is a challenging task in AI-enabled database (DB) research because prediction capability is crucial to optimizing DB operations (e.g., parameter optimization) and resource allocation in modern DB systems. However, existing methods often struggle to meet the flexible requirements for both pr...
Shao-Jie Qiao, Zi-Yu Song, Dai-Yuan Zhang et al.· IEEE Transactions on Knowled...· 0 citations
Relational Database Management Systems (DBMSs) serve as foundational systems for data storage and management, supporting a rich variety of data types to specify storage formats and value ranges. These data types play a critical role in both data storage and computation. However, complex data computation operations (e.g...
Jiansen Song, Wensheng Dou, Ying-Ying Zheng et al.· Proceedings of the VLDB Endo...· 0 citations
Learned Query Optimization (LQO) has emerged as a promising paradigm for improving database performance, with reported speedups of 2× to over 10× on standard benchmarks. Existing evaluations, however, remain largely latency-centric and leave the energy debt from data collection, model training, and online inference l...
Zi-Bo Liang, Quan-Qing Xu, Xu Chen et al.· Proceedings of the ACM on Ma...· 0 citations
A KL-divergence-driven model fine-tuning and plan updating strategy that dynamically adapts to workload changes is introduced, demonstrating improved scalability and robustness for industrial-scale PQO.
Song-Song Mo, Quan-Qing Xu, Xu-Chen Ding et al.· Proceedings of the VLDB Endo...· 0 citations
A network-optimized Monte Carlo tree search (NMCTS)-based index selection model, called tree-based index selection (Tree-IS), is proposed by using the sampling-based reinforcement learning algorithm Monte Carlo tree search (MCTS), achieving the rapid identification of optimal index sets and a significant improvement in...
Shaojie Qiao, Lei Yang, Rongmin Tang et al.· IEEE Transactions on Neural...· 0 citations
This work presents DocMaster, a hierarchical structure-aware document analysis system that parses documents into hierarchical document trees preserving original layouts and constructs a structure-aware semantic index that enables accurate document filtering and in-depth analysis.
Ziqi Chen, Yingli Zhou, Fangyuan Zhang et al.· 0 citations
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