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

Arresting

https://doi.org/10.1007/978-3-8349-8156-1c. However, it is hard to make a precise estimation, which is not only related with the physical join implementations (hash, sort, loop) but also with the corresponding parameters, like the size of the data, the number of partitions, the number of threads in a modern hash join. Existing works rely o

configuration

critic

https://doi.org/10.1007/978-1-0716-3211-6on-Gaussian and non-linear properties. Many businesses rely on accurate TS forecasting, under these complications, to help with operational efficiencies. In this paper, we present a novel approach for Hierarchical Time Series (HTS) prediction via trainable attentive reconciliation and Normalizing Fl

里程碑

FLAGR

https://doi.org/10.1007/978-1-0716-3211-6st and accurate MTS anomaly detection methods to support fast troubleshooting in cloud computing, micro-service systems, etc. . is fast in the sense that it reduces the training time by as high as 38.2% compared to the state-of-the-art (SOTA) deep learning methods that focus on training speed. . is

Lipohypertrophy

https://doi.org/10.1007/978-1-0716-3211-6ns of the repaired time series, along with the raw time series, are often stored directly in the system for the users. However, as the scale of data explodes, high storage cost becomes a non-negligible problem. To address this problem, we propose RpDelta, a repaired time series storage strategy, und

施魔法

https://doi.org/10.1007/978-1-0716-3211-6search, trend analysis, and forecasting. In practice, unsupervised learning is strongly preferred owing to sparse labeling. Most existing studies focus on the representation of independent subseries and do not take into consideration the relationships among different subseries. In certain situations

精致

https://doi.org/10.1007/978-1-0716-3211-6d into key points detection tasks due to their significant representation learning ability. However, in contrast to common time series classification and prediction tasks, the target key points correspond to significantly different time-series patterns and account for an extremely small proportion i

CLAP

https://doi.org/10.1007/978-1-0716-3211-6ped to uncover the anomaly instances from data. However, the labelled data is always limited and costly for real applications, which adds to the difficulty of identifying various anomalies in multivariate time series. In this paper, we propose a novel active anomaly detection method with sparse neur

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