Abstract Artificial intelligence-enabled database technology, known as AI4DB (Artificial Intelligence for Databases), is an active research area attracting significant attention and innovation. This survey first introduces the background of learning-based database techniques. It then reviews advanced query optimization methods for learning databases, focusing on four popular directions: cardinality/cost estimation, learning-based join order selection, learning-based end-to-end optimizers, and text-to-SQL models. Cardinality/cost estimation is classified into supervised and unsupervised methods based on learning models, with illustrative examples provided to explain the working mechanisms. Detailed descriptions of various query optimizers are also given to elucidate the working mechanisms of each component in learning query optimizers. Additionally, we discuss the challenges and development opportunities of learning query optimizers. The survey further explores text-to-SQL models, a new research area within AI4DB. Finally, we consider the future development prospects of learning databases.

Melanoma is a malignant tumor originating from the lesions of skin cells. Medical image segmentation tasks for skin lesion play a crucial role in quantitative analysis. Achieving precise and efficient segmentation remains a significant challenge for medical practitioners. Hence, a skin lesion segmentation model named MSDUNet, which incorporates multi-scale deformable block (MSD Block) and dual-input dynamic enhancement module(D2M), is proposed. Firstly, the model employs a hybrid architecture encoder that better integrates global and local features. Secondly, to better utilize macroscopic and microscopic multiscale information, improvements are made to skip connection and decoder block, introducing D2M and MSD Block. The D2M leverages large kernel dilated convolution to draw out attention bias matrix on the decoder features, supplementing and enhancing the semantic features of the decoder's lower layers transmitted through skip connection features, thereby compensating semantic gaps. The MSD Block uses channel-wise split and deformable convolutions with varying receptive fields to better extract and integrate multi-scale information while controlling the model's size, enabling the decoder to focus more on task-relevant regions and edge details. MSDUNet attains outstanding performance with Dice scores of 93.08% and 91.68% on the ISIC-2016 and ISIC-2018 datasets, respectively. Furthermore, experiments on the HAM10000 dataset demonstrate its superior performance with a Dice score of 95.40%. External validation experiments based on the ISIC-2016, ISIC-2018, and HAM10000 experimental weights on the PH2 dataset yield Dice scores of 92.67%, 92.31%, and 93.46%, respectively, showcasing the exceptional generalization capability of MSDUNet. Our code implementation is publicly available at the Github.