<b>Revolutionizing Database Design through a Scalable, Formal, Learning-Powered</b><b>Systematic Tradeoff Analysis</b>

Optimizing database design is crucial for the performance of any software systems reliant on databases. However, current database design tools offer limited support for evaluating the performance implications of database designs. Instead, they often rely on single-point solutions guided by static heuristics, offering limited insights. While state-of-the-art design tradeoff analysis tools extend greater assistance, their usage becomes impractical when dealing with large-scale, real-world systems hosting thousands of potential designs. Specifically, the exorbitant cost associated with dynamically analyzing the performance of each conceivable design impedes the scalability of these cutting-edge techniques. In this paper, we introduce an innovative solution grounded in machine learning to efficiently and scalably identify and present optimal tradeoffs within the realm of database designs for object-oriented software systems. Our approach centers on training a transformer model using a dataset of formally analyzed database designs. This trained model is then employed to identify Pareto-optimal design tradeoffs, drastically reducing the number of design candidates requiring dynamic evaluation. These select designs can subsequently undergo evaluation and comparison, with the designs offering the best tradeoffs presented to the user, significantly reducing the time required compared to state-of-the-art techniques.The extensive experiments across various software database systems corroborate our approach's high effectiveness in identifying optimal design alternatives overlooked by leading analysis tools. Additionally, our results demonstrate an impressive 98.21 % improvement in analysis efficiency compared to the current state of the art, reducing tradeoff analysis time from 58 days to just 43 minutes—a remarkable advancement in efficiency.对于任何依赖数据库的软件系统而言，优化数据库设计对其性能表现至关重要。然而，当前的数据库设计工具在评估数据库设计的性能影响方面支持有限，往往仅依赖基于静态启发式规则的单点解决方案，所能提供的分析洞察极为有限。尽管当前最先进的设计权衡分析工具能提供更多帮助，但在处理包含数千种潜在设计方案的大规模真实世界系统时，这些工具的实际应用便不再可行。具体而言，动态分析每一种可设想设计方案的性能所需的高昂成本，阻碍了这些前沿技术的可扩展性。本文提出了一种基于机器学习的创新解决方案，可高效且可扩展地识别并呈现面向对象软件系统数据库设计领域内的最优权衡方案。我们的方法核心在于，使用经过形式化分析的数据库设计数据集训练一个Transformer模型。随后利用该训练完成的模型识别帕累托最优（Pareto-optimal）设计权衡方案，大幅减少了需要进行动态评估的候选设计数量。经筛选得到的候选设计可随后开展评估与对比，并将最优权衡方案呈现给用户，相较于当前最先进技术，所需时间大幅缩短。针对多种软件数据库系统开展的大量实验证实，我们的方法能有效识别出顶尖分析工具所遗漏的最优设计方案，具备优异的有效性。此外，相较于当前主流技术，我们的方法在分析效率上实现了高达98.21%的提升，将权衡分析时长从58天缩短至仅43分钟，在效率层面实现了突破性进展。