One of the main challenges within the growing research area of learned indexing is the lack of adaptability to dynamically expanding datasets. This paper explores the dynamization of a static learned index for complex data through operations such as node splitting and broadening, enabling efficient adaptation to new data. Furthermore, we evaluate the trade-offs between static and dynamic approaches by introducing an amortized cost model to assess query performance in tandem with the build costs of the index structure, enabling experimental determination of when a dynamic learned index outperforms its static counterpart. We apply the dynamization method to a static learned index and demonstrate that its superior scaling quickly surpasses the static implementation in terms of overall costs as the database grows. This is an extended version of the paper presented at DAWAK 2025.

翻译：学习索引这一新兴研究领域面临的主要挑战之一，是缺乏对动态扩展数据集的适应能力。本文通过节点分裂与扩展等操作，探索了复杂数据静态学习索引的动态化方法，使其能够高效适应新增数据。此外，我们通过引入摊销成本模型来评估查询性能与索引结构构建成本之间的权衡，从而通过实验确定动态学习索引在何种条件下优于静态索引。我们将动态化方法应用于静态学习索引，并证明随着数据库规模增长，其优越的扩展性在总体成本上迅速超越静态实现。本文为DAWAK 2025会议论文的扩展版本。