The rapid growth of trajectory data -- especially the dense 4D traces generated by unmanned aerial vehicles (UAVs) -- is placing mounting pressure on spatio-temporal data management systems. Existing HBase-based trajectory indexes suffer from three limitations: coarse-grained temporal pruning, locality-unfriendly XZ2 spatial encodings with workload-blind ordering, and severe row-key interval fragmentation when altitude is jointly encoded with the horizontal dimensions. We present AeroMesa, a unified system that natively supports $(x,y)$, $(x,y,t)$, $(x,y,z)$, and $(x,y,z,t)$ queries within a single storage framework. AeroMesa integrates three complementary designs: a temporal index (TI$^{+}$) that refines pruning to second-level granularity, a Hilbert-BFS spatial index with a Workload-Aware Jaccard ordering, and a decoupled 4D architecture that separates horizontal indexing from altitude-aware secondary indexing to eliminate isotropic-encoding fragmentation. We implement AeroMesa on Apache HBase and Redis and evaluate it on a real-world dataset (T-Drive) and a high-fidelity 90,000-trajectory UAV simulation dataset. AeroMesa consistently outperforms all baselines: TI$^{+}$ cuts temporal-query candidates by up to 51% over MCTM, the Hilbert-BFS/WAJ index lowers 2D latency by up to 17.9% over the state-of-the-art TMan, and the decoupled 4D design reduces latency by up to 30$\times$ while cutting merged scan ranges by up to three orders of magnitude over XZ3/TXZ3 joint-encoding approaches.

翻译：轨迹数据的快速增长——尤其是无人机生成的密集四维轨迹——正给时空数据管理系统带来日益沉重的压力。现有基于HBase的轨迹索引存在三个局限性：粗粒度的时间剪枝、局部性不佳且无视负载的XZ2空间编码排序，以及当高度与水平维度联合编码时严重的行键区间碎片化问题。我们提出AeroMesa，一个在单一存储框架内原生支持$(x,y)$、$(x,y,t)$、$(x,y,z)$及$(x,y,z,t)$查询的统一系统。AeroMesa集成了三种互补设计：一个将剪枝粒度细化至秒级的时间索引TI$^{+}$，一个采用负载感知Jaccard排序的Hilbert-BFS空间索引，以及一个将水平索引与高度感知二级索引分离的解耦四维架构，以消除各向同性编码碎片化。我们在Apache HBase和Redis上实现了AeroMesa，并在真实数据集T-Drive和高保真度的90,000条轨迹无人机仿真数据集上进行了评估。AeroMesa持续优于所有基线：TI$^{+}$相较于MCTM将时间查询候选集削减高达51%，Hilbert-BFS/WAJ索引相较于当前最先进的TMan将二维查询延迟降低高达17.9%，而解耦四维设计相较于XZ3/TXZ3联合编码方法将延迟减少高达30倍，同时将合并扫描范围缩小多达三个数量级。