Geodabs: Trajectory Indexing Meets Fingerprinting at Scale

TL;DR

This paper introduces geodabs, a trajectory fingerprinting method based on geohash, enabling scalable, efficient indexing and retrieval in large, dense trajectory datasets without performance degradation.

Contribution

It presents a novel fingerprinting approach for trajectories using geohash-based geodabs, facilitating scalable, distributed indexing unaffected by dataset density.

Findings

Geodabs enable efficient trajectory indexing at scale.

Normalization impacts precision and recall.

Method maintains performance regardless of dataset density.

Abstract

Finding trajectories and discovering motifs that are similar in large datasets is a central problem for a wide range of applications. Solutions addressing this problem usually rely on spatial indexing and on the computation of a similarity measure in polynomial time. Although effective in the context of sparse trajectory datasets, this approach is too expensive in the context of dense datasets, where many trajectories potentially match with a given query. In this paper, we apply fingerprinting, a copy-detection mechanism used in the context of textual data, to trajectories. To this end, we fingerprint trajectories with geodabs, a construction based on geohash aimed at trajectory fingerprinting. We demonstrate that by relying on the properties of a space filling curve geodabs can be used to build sharded inverted indexes. We show how normalization affects precision and recall, two key measures in information retrieval. We then demonstrate that the probabilistic nature of fingerprinting has a marginal effect on the quality of the results. Finally, we evaluate our method in terms of performances and show that, in contrast with existing methods, it is not affected by the density of the trajectory dataset and that it can be efficiently distributed.