AeroMesa: Efficient Data Management System for Multi-Dimensional Spatio-Temporal Trajectories

2026-06-08

AI Summary

Existing HBase-based trajectory indexing approaches struggle with coarse-grained temporal pruning, poor spatial locality in encoding, and row key fragmentation caused by joint encoding of the altitude dimension when handling high-dimensional spatiotemporal UAV data. To address these limitations, this work proposes AeroMesa, a novel system featuring a TI⁺ temporal index enabling second-level time pruning, Hilbert-BFS combined with Jaccard-based workload-aware spatial ordering, and a pioneering decoupled 4D storage architecture that separates horizontal and vertical dimensions. This design efficiently supports four query types: (x,y), (x,y,t), (x,y,z), and (x,y,z,t). Experimental evaluation on T-Drive and a dataset of 90,000 UAV trajectories demonstrates that AeroMesa reduces temporal query candidate sets by 51%, lowers 2D query latency by 17.9%, achieves up to a 30× speedup for 4D queries, and shrinks scan ranges by three orders of magnitude.

Abstract

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.

Problem

• spatio-temporal trajectories
• trajectory indexing
• 4D data management
• UAV trajectory
• data fragmentation

Innovation

• spatio-temporal indexing
• trajectory data management
• Hilbert-BFS
• decoupled 4D architecture
• workload-aware ordering