COL-Trees: Efficient Hierarchical Object Search in Road Networks

TL;DR

This paper introduces COL-Trees, a new hierarchical data structure that significantly improves the efficiency of complex POI search queries in road networks, outperforming existing methods by up to 10,000 times.

Contribution

The paper presents COL-Trees, a novel landmark-based hierarchical data structure for efficient graph traversal in POI search queries, addressing limitations of Euclidean heuristics.

Findings

Achieves up to 4 orders of magnitude speedup over existing methods.

Provides effective solutions for AkNN and kFN queries in road networks.

Requires minimal pre-processing overhead.

Abstract

Location-based services rely heavily on efficient methods that search for relevant points-of-interest (POIs) near a given location. A k Nearest Neighbor (kNN) query is one such example that finds the k closest POIs from an agent's location. While most existing techniques focus on retrieving nearby POIs for a single agent, these search heuristics do not translate to many other applications. For example, Aggregate k Nearest Neighbor (AkNN) queries require POIs that are close to multiple agents. k Farthest Neighbor (kFN) queries require POIs that are the antithesis of nearest. Such problems naturally benefit from a hierarchical approach, but existing methods rely on Euclidean-based heuristics, which have diminished effectiveness in graphs such as road networks. We propose a novel data structure, COL-Tree (Compacted Object-Landmark Tree), to address this gap by enabling efficient hierarchical graph traversal using a more accurate landmark-based heuristic. We then present query algorithms that utilize COL-Trees to efficiently answer AkNN, kFN, and other queries. In our experiments on real-world and synthetic datasets, we demonstrate that our techniques significantly outperform existing approaches, achieving up to 4 orders of magnitude improvement. Moreover, this comes at a small pre-processing overhead in both theory and practice.