Compute Express Link (CXL) is an open industry-standard interconnect protocol designed for communication between processors and devices, e.g., memory expansions. It brings new opportunities for enhancing system performance, particularly for workloads constrained by memory capacity. Consequently, CXL opens new avenues for the design of database management systems (DBMS), which is highly beneficial for managing complex data tasks. However, traditional techniques focused on addressing I/O bottlenecks are insufficient to meet the new challenges brought by CXL memory management. In this tutorial, we introduce the advantages and application scenarios of tiered CXL memory, pooled CXL memory, and shared CXL memory, as well as the new challenges that arise, including: (1) reducing data access, data exchange, and data transfer cost; (2) optimizing memory allocation and competition to improve memory utilization; (3) managing shared data for failure, series operators, and distributed transactions. We also review emerging techniques aimed at addressing these issues. Finally, we summarize new opportunities and highlight open research problems in this evolving area.

Compute Express Link (CXL) is emerging as a significant player in the landscape of modern database man-agement systems (DBMS). CXL is an open industry-standard interconnect protocol between processors and devices such as memory buffers. Boasting high bandwidth, low latency, and support for coherency and memory semantics, CXL opens a new direction for addressing the limitations and bottlenecks faced by traditional distributed DBMS, particularly in large-scale data management, efficient query processing, and improving system availability. This paper explores the significant potential of employing CXL in constructing next-generation DBMS. Through a thorough analysis of CXL's key characteristics, this paper identifies emerging opportunities, particularly in buffer pool expansion, memory elasticity, swift data recovery, and index optimization. More importantly, this paper outlines a series of new challenges accompanying these opportunities, with the objective of inspiring cutting-edge approaches in future DBMS design that emphasize efficiency, reliability, and reduced total cost of ownership.

In this paper, we introduce CTFL, a fair, robust, and interpretable framework designed to estimate clients' contributions to federated learning, aiming to incentivize high-quality data providers to participate in the federation. Firstly, CTFL can precisely allocate contribution credits in a single pass of model training and inference, ensuring computational efficiency. This is accomplished by tracking the test performance gain brought by each participant through exploiting classification rules. Secondly, CTFL adheres to essential theoretical properties of an ideal contribution estimation algorithm, including symmetry, zero-element, and additivity, ensuring fair and rational estimations. Thirdly, CTFL demonstrates resilience against strategic and malicious behaviors due to carefully crafted micro and macro contribution estimation schemes. Fourthly, CTFL offers insights into participants' roles within the federation by interpreting their contribution scores through respective high-frequently activated rules. Finally, CTFL integrates logical neural networks and model binarization techniques to ensure effectiveness and efficiency while preserving data privacy. Extensive experiments validate that CTFL accurately estimates contributions, significantly reducing computation time by 2–3 orders of magnitude compared to state-of-the-art methods while maintaining robustness.

Latent Dirichlet Allocation (LDA) has been widely used for topic modeling, with applications spanning various areas such as natural language processing and information retrieval. While LDA on small and static datasets has been extensively studied, several real-world challenges are posed in practical scenarios where datasets are often huge and are gathered in a streaming fashion. As the state-of-the-art LDA algorithm on streams, Streaming Variational Bayes (SVB) introduced Bayesian updating to provide a streaming procedure. However, the utility of SVB is limited in applications since it ignored three challenges of processing real-world streams: topic evolution , data turbulence , and real-time inference . In this article, we propose a novel distributed LDA algorithm—referred to as StreamFed-LDA— to deal with challenges on streams. For topic modeling of streaming data, the ability to capture evolving topics is essential for practical online inference. To achieve this goal, StreamFed-LDA is based on a specialized framework that supports lifelong (continual) learning of evolving topics. On the other hand, data turbulence is commonly present in streams due to real-life events. In that case, the design of StreamFed-LDA allows the model to learn new characteristics from the most recent data while maintaining the historical information. On massive streaming data, it is difficult and crucial to provide real-time inference results. To increase the throughput and reduce the latency, StreamFed-LDA introduces additional techniques that substantially reduce both computation and communication costs in distributed systems. Experiments on four real-world datasets show that the proposed framework achieves significantly better performance of online inference compared with the baselines. At the same time, StreamFed-LDA also reduces the latency by orders of magnitudes in real-world datasets.