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SR-KNN: An Real-time Approach of Processing k-NN Queries over Moving Objects

  • Ziqiang Yu
  • Yuehui Chen
  • Kun Ma
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 1)

Abstract

Central to many location-based service applications is the task of processing k-nearest neighbor (k-NN) queries over moving objects. Many existing approaches adapt different index structures and design various search algorithms to deal with this problem. In these works, tree-based indexes and grid index are mainly utilized to maintain a large volume of moving objects and improve the performance of search algorithms. In fact, tree-based indexes and grid index have their own flaws for supporting processing k-NN queries over an ocean of moving objects. A treebased index (such as R-tree) needs to constantly maintain the relationship between nodes with objects continuously moving, which usually causes a high maintenance cost. Grid index is although widely used to support k-NN queries over moving objects, but the approaches based on grid index almost require an uncertain number of iterative calculations, which makes the performance of these approaches be not predictable. To address this problem, we present a dynamic Strip-Rectangle Index (SRI), which can reach a good balance of the maintenance cost and the performance of supporting k-NN queries over moving objects. SRI supplies two different index granularities that makes it better adapt to handle different data distributions than existing index structures. Based on SRI, we propose a search algorithm called SR-KNN that can rapidly calculate a final region space with a filter-and-refine strategy to enhance the efficiency of process k-NN queries, rather than iteratively enlarging the search space like the approaches based on grid index. Finally, we conduct experiments to fully evaluate the performance of our proposal.

Keywords

Index Structure Search Region Spatial Query Neighbor Query Grid Index 
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 International Publishing AG 2017

Authors and Affiliations

  1. 1.The university of Ji’nanJinanChina

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