kNR-tree: a novel R-tree-based index for facilitating spatial window queries on any k relations among N spatial relations in mobile environments

  • Authors:

  • Anirban Mondal;Anthony K. H. Tung;Masaru Kitsuregawa

  • Affiliations:

  • University of Tokyo, Japan;National University of Singapore, Singapore;University of Tokyo, Japan

  • Venue:
  • Proceedings of the 6th international conference on Mobile data management
  • Year:
  • 2005

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Abstract

The ever-increasing popularity of mobile applications coupled with the prevalence of spatial data has created the need for efficient processing of spatial queries in mobile environments. While different types of spatial queries (e.g., spatial select queries, spatial join queries and nearest neighbour queries) need to be addressed in mobile environments, this work specifically addresses the processing of spatial select queries (i.e., window queries) on any k relations among N spatial relations. We designate such window queries on any k relations among N spatial relations as kNW queries. Notably, the processing of kNW queries is much more challenging in mobile environments than in traditional environments primarily due to the mobility of the clients which issue the queries to the respective base stations. The main contribution of this work is the proposal of the kNR-tree, a single integrated novel R-tree-based structure for indexing objects from N different spatial relations. Notably, the kNR-tree facilitates efficient processing of kNW queries. Our performance evaluation demonstrates that our proposed technique, which is based on the kNR-tree, is indeed effective in reducing the response times of kNW queries in mobile environments.