The recent developments in the research area indicate a significant shift towards more efficient and innovative approaches in data management and optimization. A notable trend is the focus on eliminating interlocking issues in cooperative systems, leading to the introduction of novel architectures that enhance the disjoint training of modules. This approach not only improves performance but also reduces learning overhead, marking a substantial advancement in selective rationalization. Additionally, there is a comprehensive exploration of multi-query optimization techniques, with a unified view proposed to address the complexities of selection problems across various optimization scenarios. This includes leveraging machine learning for more effective selection algorithms. Another key area of progress is the optimization of select-project-join query plans, where new measures and algorithms are introduced to achieve tighter characterizations of intermediate relation sizes, providing a more complete understanding of plan efficiency. Furthermore, the field is witnessing a push towards optimizing active data management systems, particularly in the context of big data, with strategies aimed at enhancing scalability, performance, and efficiency for proactive data updates and analytics. These developments collectively highlight a move towards more intelligent, efficient, and proactive data management solutions.