Efficient k-NN Trajectory Queries on Mobility Databases

The rapid adoption of GPS-enabled mobile devices has produced massive trajectory datasets that drive modern applications in traffic prediction, logistics, and spatio-temporal analytics. Yet traditional database management systems (DBMSs) still lack native operators to process such data efficiently. To overcome this limitation, we introduce a set of k-nearest neighbor (k-NN) user-defined aggregates (UDAs) that embed k-NN processing directly within the PostgreSQL engine. By integrating computation into the database core, our approach minimizes data transfer and latency while maintaining low storage overhead. Experiments on benchmarked BerlinMOD-derived datasets demonstrate that the proposed UDAs reduce query execution time by 6–23%, depending on dataset size and query complexity.