The proliferation of multi-dimensional trajectory data, fueled by large-scale IoT and the emerging low-altitude economy, particularly UAV operations, drives repositories to jointly support (x,y), (x,y,t), (x,y,z), and (x,y,z,t) queries within a single storage framework. Yet existing HBase-based systems fall short in three respects: severe row-key interval fragmentation when altitude is jointly encoded with horizontal coordinates, locality-unfriendly spatial encodings with workload-blind shape-code ordering, and coarse-grained temporal indexes that leave intra-slot boundary ambiguity unresolved. We present AeroMesa, an efficient data management system for multi-dimensional spatio-temporal trajectories built on Apache HBase and Redis, that natively supports (x,y), (x,y,t), (x,y,z), and (x,y,z,t) queries within a unified storage framework. AeroMesa addresses the above limitations through three designs: a decoupled horizontal-altitude architecture with a multi-granularity Height Spatio-Temporal Index (HTSI) that eliminates joint encoding fragmentation; Hilbert-BFS with Workload-Aware Jaccard (WAJ) reordering that improves spatial locality; and TI+, a dual-offset temporal index that resolves intra-slot false positives. Evaluations on T-Drive and an 87,537-trajectory high-fidelity UAV simulation demonstrate that AeroMesa reduces 3D/4D query latency by up to 30x over XZ3/TXZ3, lowers 2D latency by up to 17.9% over TMan, and cuts temporal candidates by up to 51.3% over MCTM, with sub-linear scalability confirmed under 200x data expansion, confirming AeroMesa's efficiency for multi-dimensional spatio-temporal trajectory management.

翻译：大规模物联网和新兴的低空经济（特别是无人机作业）催生了多维轨迹数据的激增，这促使存储系统需要在一个统一的存储框架内联合支持 (x,y)、(x,y,t)、(x,y,z) 和 (x,y,z,t) 查询。然而，现有的基于 HBase 的系统存在三个不足：当高度与水平坐标联合编码时，行键区间碎片化严重；采用对工作负载无感的形状编码顺序，导致空间编码与局部性不友好；以及粗粒度的时间索引未能解决时间槽内边界模糊问题。本文提出 AeroMesa，一个基于 Apache HBase 和 Redis 构建、面向多维时空轨迹的高效数据管理系统，它在一个统一的存储框架内原生支持 (x,y)、(x,y,t)、(x,y,z) 和 (x,y,z,t) 查询。AeroMesa 通过三个设计解决了上述局限：一种解耦的水平-高度架构，配合多粒度的空间高度时空索引（HTSI），消除了联合编码碎片；一种采用工作负载感知Jaccard（WAJ）重排序的 Hilbert-BFS 方法，提升了空间局部性；以及 TI+，一种双偏移量时间索引，解决了时间槽内的误报问题。在 T-Drive 数据集和包含 87,537 条轨迹的高保真无人机模拟数据集上的评估表明：与 XZ3/TXZ3 相比，AeroMesa 将 3D/4D 查询延迟降低了最多 30 倍；与 TMan 相比，2D 查询延迟降低了最多 17.9%；与 MCTM 相比，时间候选集减少了最多 51.3%。在 200 倍数据扩展下，可扩展性呈亚线性，这证实了 AeroMesa 在多维时空轨迹管理方面的效率。