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Titlebook: Database Systems for Advanced Applications; 28th International C Xin Wang,Maria Luisa Sapino,Hongzhi Yin Conference proceedings 2023 The Ed

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21#
發(fā)表于 2025-3-25 05:58:33 | 只看該作者
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/d/image/263403.jpg
22#
發(fā)表于 2025-3-25 11:06:07 | 只看該作者
Selling in International MarketsG-TQA is making a different bottom-up paradigm. Extensive experiments on a widely-used benchmark and online experiments on a practical industry system demonstrate the superiority of SIG-TQA. Currently, our SIG-TQA has been applied to a real-world Table QA system, and its code is available on ..
23#
發(fā)表于 2025-3-25 12:37:00 | 只看該作者
24#
發(fā)表于 2025-3-25 17:02:41 | 只看該作者
25#
發(fā)表于 2025-3-25 21:50:48 | 只看該作者
https://doi.org/10.1007/978-3-658-05653-7M. PFtree reduces memory allocations in critical paths by allocating bulk memory when creating a leaf array. Then, we design an adaptive persistence way based on data block size for PFtree to fully use PM bandwidth. Experimental results show that our proposed PFtree outperforms the radix tree by up
26#
發(fā)表于 2025-3-26 00:47:23 | 只看該作者
https://doi.org/10.1007/978-3-658-23067-8-key scenarios. Because of this, we propose a Multi-key LBF (MLBF) data structure, which contains a value-interaction-based multi-key classifier and a multi-key Bloom filter. To reduce FPR, we further propose an Interval-based MLBF, which divides keys into specific intervals according to the data di
27#
發(fā)表于 2025-3-26 07:25:22 | 只看該作者
https://doi.org/10.1007/978-3-8349-8156-1eGAT, a heterogonous graph neural network, to fully capture the edge weights (the number of function calls) in the join-graph. The embeddings learned from ReGAT can be used to predict the running time. In addition, we optimize JG2Time with a multi-task model that also predicts the times of function
28#
發(fā)表于 2025-3-26 08:34:47 | 只看該作者
https://doi.org/10.1007/978-1-0716-3211-6ced Contrastive Learning (BCL), which avoids excessive intra-class compaction of tail classes by introducing a balanced supervised contrastive loss with hierarchical prototypes, resulting in a balanced feature space and better generalization. From the data perspective, we explore the effectiveness o
29#
發(fā)表于 2025-3-26 13:43:44 | 只看該作者
30#
發(fā)表于 2025-3-26 16:53:01 | 只看該作者
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