Trusted execution environment (TEE) offers a promising approach to building encrypted databases, which keep data confidential for users. However, designing an efficient index for TEE databases remains a significant challenge. Due to the limited enclave memory and system call support in enclaves, traditional indexes incur massive context switches (including enclave entry and exiting), which cause performance regression. Existing approaches, such as introducing rich execution environment (REE) buffer pools or index parameter optimization, may not alleviate these problems effectively. To address these limitations, we propose LETIndex, an efficient learned dynamic index designed for TEE databases. LETIndex adopts LSM-structured Piecewise Geometric Model (PGM) indexes and an adaptive prefetch mechanism to support lookup, range queries, and updates with significantly reduced context switches and disk I/O overhead. Experimental results show that LETIndex achieves superior performance compared to existing approaches on the SOSD benchmark. We demonstrate LETIndex with two real scenarios, binary join and multi-tale join.

Machine learning(ML) has widespread applications and has revolutionized many industries, but suffers from several challenges. First, sufficient high-quality training data is inevitable for producing a well-performed model, but the data is always human expensive to acquire.Second, a large amount of training data and complicated model structures lead to the inefficiency of training and inference. Third, given an ML task, one always needs to train lots of models, which are hard to manage in real applications. Fortunately, database techniques can benefit ML by addressing the above three challenges. In this paper, we review existing studies from the following three aspects along with the pipeline highly related to ML. (1) Data preparation(Pre-ML): it focuses on preparing high-quality training data that can improve the performance of the ML model, where we review data discovery, data cleaning and data labeling. (2) Model training \& inference(In-ML): researchers in ML community focus on improving the model performance during training, while in this survey we mainly study how to accelerate the entire training process, also including feature selection and model selection. (3) Model management(Post-ML): in this part, we survey how to store, query, deploy and debug the models after training. Finally, we provide research challenges and future directions.

This paper presents a lightweight tele-immersive video chat system named CuteChat. Based on our recently developed video object cutout technology, the CuteChat system is designed and optimized to provide a radically new video chat experience by merging each participant in the same shared space, allowing them to interact more naturally in an integrated manner. With the goal to make the system easily accessible by massive consumers, we address the challenges in the whole pipeline of video processing, coding, communication, composition, and playback. Extensive experiments have shown that the proposed CuteChat system runs reliably and comfortably in real time on one's laptop or desktop PC, and it needs only a commodity webcam for video acquisition and just public Internet for tele-immersive video conferencing. With such a really minimal deployment requirement, we present a variety of interesting applications and user experiences created by the CuteChat system.