In this paper, we present a learned and workload-aware variant of a Z-index, which jointly optimizes storage layout and search structures. Specifically, we first formulate a cost function to measure the performance of a Z-index on a dataset for a range-query workload. Then, we optimize the Z-index structure by minimizing the cost function through adaptive partitioning and ordering for index construction. Moreover, we design a novel page-skipping mechanism to improve its query performance by reducing access to irrelevant data pages. Our extensive experiments show that our index improves range query time by 40% on average over the baselines, while always performing better or comparably to state-of-the-art spatial indexes. Additionally, our index maintains good point query performance while providing favourable construction time and index size tradeoffs.

翻译：本文提出了一种学习型且具有工作量感知能力的Z-index变体，该变体联合优化了存储布局与搜索结构。具体而言，我们首先构建一个代价函数来衡量Z-index在特定数据集上处理范围查询工作负载的性能。随后，通过自适应分区与排序来最小化代价函数，从而优化Z-index结构以实现索引构建。此外，我们设计了一种新颖的页面跳过机制，通过减少对无关数据页面的访问来提升查询性能。大量实验表明，相较基线方法，我们的索引平均将范围查询时间提升40%，同时始终优于或持平于最先进的空间索引。同时，该索引在维持良好点查询性能的前提下，在索引构建时间与存储开销之间取得了有利的权衡。