Multistep Search Algorithm for Sum k-Nearest Neighbor Queries on Remote Spatial Databases

Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 14)

Abstract

Processing sum k-Nearest Neighbor (NN) queries on remote spatial databases suffers from a large amount of communication. In this paper, we propose RQP-M search algorithm for efficiently searching sum k-NN query results to overcome the difficulty. It refines query results originally searched by RQP-S algorithm with subsequent k-NN queries, whose query points are chosen among vertices of a regular polygon inscribed in a before-searched circle. Experimental results show that Precision is over 0.99 for uniformly distributed data, over 0.95 for skew-distributed data, and over 0.97 for real data. Also, NOR (Number of Requests) ranges between 3.2 and 4.0, between 3.1 to 3.8, and between 2.9 and 3.5, respectively. Precision of RQP-M increases by 0.04-0.20 for uniformly distributed data, in comparison with that of RQP-S.

Keywords

Minimal Point Near Neighbor Range Query Query Result Query Point 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.School of InformaticsDaido UniversityNagoyaJapan
  2. 2.Aichi Toho UniversityNagoyaJapan

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