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
Granular-Ball Computing (GBC) is an efficient, robust, and highly interpretable multi-granularity representation and computation method. Nonetheless, most feature selection methods based on GBC require considerable time to calculate the significance measures of features or repeatedly generate granular balls, which limi...
Ye Li, Lei Yang, Bin-Bin Sang et al.· IEEE Transactions on Knowled...· 2 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 proposes SeeExplainer, a parameter-free explainer to interpret graph neural networks, and introduces a granular-ball graph refinement mechanism that decomposes a graph into several disjoint granular-balls with no fixed size, and utilizes them as nodes to construct a structural graph.
Jian-Cu Chen, Shuyin Xia, Guan Wang et al.· arXiv.org· 0 citations
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