Database vendors have made significant redesigns and developments to the relational database for providing cloud-hosted and cloud-native database services. Thus, the original knob-tuning experiences of DBAs are no longer applicable to the cloud database era. An interpretable estimation service is urgently needed to provide explicit guidance for database knob tuning. Unfortunately, less attention has been paid to estimating the performance of the knob configuration. To fill this gap, we propose EAST, a knob estimation system to provide interpretable & transferable knob estimation services for cloud databases. Firstly, we design an interpretable knob-embedding-based estimator to achieve the trusted and white-box knob estimation for researchers, practitioners, and even artificial intelligence knob tuners. Secondly, we design a two-stage transfer estimation approach by stacking ensemble learning to utilize historical experiences, improving time efficiency. Thirdly, EAST provides a user-friendly interface to support direct knob estimation and transfer knob estimation services. We have deployed our EAST11https://gitee.com/opengauss/openGauss-DBMind/tree/incubator/dbmind/components/knob_estimator to the DBMind component of OpenGauss and demonstrated the effectiveness of our system under the open-source benchmark TPCC.

Sea ice extent plays a crucial role in the Arctic system, and thus the study on causal relationships between sea ice extent and other climate variables comes to our interest to better understand the system. To find the causal relationship we applied various state-of-the-art causal discovery techniques from the time-independent and time-dependent domains. Then we employed several causal inference models to quantify the causal effects of different causal relationships in the Arctic system. The NSIDC Sea Ice Concentration observation data and the ERA-5 global reanalysis data were used in our study. Our analysis shows that the GES and VarLiNGAM from causal discovery methods and the conditional instrumental variable (CIV) causal inference model perform better on the Arctic Sea Ice time series dataset.

Spatial resolution is critical for observing and monitoring environmental phenomena. Acquiring high-resolution bathymetry data directly from satellites is not always feasible due to limitations on equipment, so spatial data scientists and researchers turn to single image super-resolution (SISR) methods that utilize deep learning techniques as an alternative method to increase pixel density. While super resolution residual networks (e.g., SR-ResNet) are promising for this purpose, several challenges still need to be addressed: (1) Earth data such as bathymetry is expensive to obtain and relatively limited in its data record amount; (2) certain domain knowledge needs to be complied with during model training; (3) certain areas of interest require more accurate measurements than other areas. To address these challenges, following the transfer learning principle, we study how to leverage an existing pre-trained super-resolution deep learning model, namely SR-ResNet, for high-resolution bathymetry data generation. We further enhance the SR-ResNet model to add corresponding loss functions based on domain knowledge. To let the model perform better for certain spatial areas, we add additional loss functions to increase the penalty of the areas of interest. Our experiments show our approaches achieve higher accuracy than most baseline models when evaluating using metrics including MSE, PSNR, and SSIM.