Simpler is More: Efficient Top-K Nearest Neighbors Search on Large Road Networks

TL;DR

This paper introduces KNN-Index, a simple and efficient index for top-k nearest neighbors search on large road networks, outperforming complex existing methods in speed, size, and construction time.

Contribution

It proposes a novel simple index and a bidirectional construction algorithm that improve query efficiency and reduce index size and construction time.

Findings

KNN-Index answers queries optimally and progressively.

KNN-Index has smaller size and faster construction.

Experimental results show superior performance over state-of-the-art methods.

Abstract

Top-k Nearest Neighbors (kNN) problem on road network has numerous applications on location-based services. As direct search using the Dijkstra's algorithm results in a large search space, a plethora of complex-index-based approaches have been proposed to speedup the query processing. However, even with the current state-of-the-art approach, long query processing delays persist, along with significant space overhead and prohibitively long indexing time. In this paper, we depart from the complex index designs prevalent in existing literature and propose a simple index named KNN-Index. With KNN-Index, we can answer a kNN query optimally and progressively with small and size-bounded index. To improve the index construction performance, we propose a bidirectional construction algorithm which can effectively share the common computation during the construction. Theoretical analysis and experimental results on real road networks demonstrate the superiority of KNN-Index over the state-of-the-art approach in query processing performance, index size, and index construction efficiency.