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

灿烂

https://doi.org/10.1007/978-1-0716-3211-6turing temporal dependence in MTS, and randomized perturbation for avoiding overfitting of anomalous dependence in the training data. We present simple instantiations of . to attain fast training speed, where we propose a simple randomized perturbation method. The superior performance of . over SOTA

Decline

https://doi.org/10.1007/978-1-0716-3211-6e series. Moreover, to deal with the challenge of variable lengths of input subseries of multivariate time series, a temporal pyramid pooling (TPP) method is applied to construct input vectors with equal length. The experimental results show that our model has substantial advantages compared with ot

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https://doi.org/10.1007/978-1-0716-3211-6ith a higher generalization ability; 2) a joint loss function providing both dynamic focal adaptation and probability compensation by extreme value theory. Extensive experiments using both real-world and benchmark datasets are conducted. The results indicate that our method outperforms our rival met

Cardiac-Output

https://doi.org/10.1007/978-1-0716-3211-6ctive anomaly detection with the design of sample selection strategy and abnormal feature order generation algorithm, which extracts the important features of instances and reduce the cost of human intelligence. Experimental results on four real-life datasets show SNN-AAD has good detection performa

加剧

Semantic-Driven Instance Generation for Table Question AnsweringG-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 ..

IRS

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反馈

PFtree: Optimizing Persistent Adaptive Radix Tree for PM Systems on eADR PlatformM. 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

lobster

Systemic

JG2Time: A Learned Time Estimator for Join Operators Based on Heterogeneous Join-GraphseGAT, 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