G-tree: an efficient index for KNN search on road networks

  • Authors:

  • Ruicheng Zhong;Guoliang Li;Kian-Lee Tan;Lizhu Zhou

  • Affiliations:

  • Tsinghua University, Beijing, China;Tsinghua University, Beijing, China;National University of Singapore, Singapore, Singapore;Tsinghua University, Beijing, China

  • Venue:
  • Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
  • Year:
  • 2013

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Abstract

In this paper we study the problem of kNN search on road networks. Given a query location and a set of candidate objects in a road network, the kNN search finds the k nearest objects to the query location. To address this problem, we propose a balanced search tree index, called G-tree. The G-tree of a road network is constructed by recursively partitioning the road network into sub-networks and each G-tree node corresponds to a sub-network. Inspired by classical kNN search on metric space, we introduce a best-first search algorithm on road networks, and propose an elaborately-designed assembly-based method to efficiently compute the minimum distance from a G-tree node to the query location. G-tree only takes O(|V|log|V|) space, where |V| is the number of vertices in a network, and thus can easily scale up to large road networks with more than 20 millions vertices. Experimental results on eight real-world datasets show that our method significantly outperforms state-of-the-art methods, even by 2-3 orders of magnitude.