AeroMesa: Efficient Data Management System for Multi-Dimensional Spatio-Temporal Trajectories
2026-06-08 • Databases
Databases
AI summaryⓘ
The authors address challenges in managing large amounts of drone flight data, which include issues with how time and space information is stored and searched. They created AeroMesa, a system that improves how data is organized and queried by using finer time filtering, better spatial indexing methods, and separating altitude data from other location data. When tested, AeroMesa showed faster and more efficient searches compared to previous methods. Their work makes it easier to handle complex 3D and 4D trajectory queries in big datasets.
trajectory dataspatio-temporal indexingHBaseUAVHilbert curvetemporal pruningspatial encoding4D indexingdatabasesquery optimization
Authors
Yue Zhang, Zizhong Ding, Lin Sun, Haopeng Chen, Yan Jiao, Yongming Xu
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.