Workload-aware and Learned Z-Indexes

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.