An efficient and scalable approach to CNN queries in a road network

  • Authors:

  • Hyung-Ju Cho;Chin-Wan Chung

  • Affiliations:

  • Korea Advanced Institute of Science and Technology, Yusong-gu, Taejon, Korea;Korea Advanced Institute of Science and Technology, Yusong-gu, Taejon, Korea

  • Venue:
  • VLDB '05 Proceedings of the 31st international conference on Very large data bases
  • Year:
  • 2005

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Abstract

A continuous search in a road network retrieves the objects which satisfy a query condition at any point on a path. For example, return the three nearest restaurants from all locations on my route from point s to point e. In this paper, we deal with NN queries as well as continuous NN queries in the context of moving objects databases. The performance of existing approaches based on the network distance such as the shortest path length depends largely on the density of objects of interest. To overcome this problem, we propose UNICONS (a unique continuous search algorithm) for NN queries and CNN queries performed on a network. We incorporate the use of precomputed NN lists into Dijkstra's algorithm for NN queries. A mathematical rationale is employed to produce the final results of CNN queries. Experimental results for real-life datasets of various sizes show that UNICONS outperforms its competitors by up to 3.5 times for NN queries and 5 times for CNN queries depending on the density of objects and the number of NNs required.