Multiple Object Types KNN Search Using Network Voronoi Diagram

  • Authors:

  • Geng Zhao;Kefeng Xuan;David Taniar;Maytham Safar;Marina Gavrilova;Bala Srinivasan

  • Affiliations:

  • Clayton School of Information Technology, Monash University, Australia;Clayton School of Information Technology, Monash University, Australia;Clayton School of Information Technology, Monash University, Australia;Department of Computer Engineering, Kuwait University, Kuwait;Department of Computer Science, the University of Calgary, Canada;Clayton School of Information Technology, Monash University, Australia

  • Venue:
  • ICCSA '09 Proceedings of the International Conference on Computational Science and Its Applications: Part II
  • Year:
  • 2009

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Abstract

Existing work on k nearest neighbor (k NN) in spatial/mobile query processing focuses on single object types. Furthermore, they do not consider optimum path in KNN. In this paper, we focus on multiple type k NN whereby the interest points are of multiple types. Additionally, we also consider an optimum path to reach the interest points. We propose three different query types involving multiple object types. Our algorithms adopt the network Voronoi Diagram (NVD). We describe two ways to solve multiple types of KNN queries: one is to create NVD for each object type, and two is to create one NVD for all objects. The comparison between these two approaches is presented in performance evaluation section.