All-Visible-k-Nearest-Neighbor Queries

  • Yafei Wang
  • Yunjun Gao
  • Lu Chen
  • Gang Chen
  • Qing Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7447)

Abstract

The All-k-Nearest-Neighbor (AkNN) operation is common in many applications such as GIS and data analysis/mining. In this paper, for the first time, we study a novel variant of AkNN queries, namely All-Visible-k-Nearest-Neighbor (AVkNN) query, which takes into account the impact of obstacles on the visibility of objects. Given a data set P, a query set Q, and an obstacle set O, an AVkNN query retrieves for each point/object in Q its visible k nearest neighbors in P. We formalize the AVkNN query, and then propose efficient algorithms for AVkNN retrieval, assuming that P, Q, and O are indexed by conventional data-partitioning indexes (e.g., R-trees). Our approaches employ pruning techniques and introduce a new pruning metric called VMDIST. Extensive experiments using both real and synthetic datasets demonstrate the effectiveness of our presented pruning techniques and the performance of our proposed algorithms.

Keywords

Query Processing Synthetic Dataset Query Point Query Performance Pruning Technique 
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

  • Yafei Wang
    • 1
  • Yunjun Gao
    • 1
  • Lu Chen
    • 1
  • Gang Chen
    • 1
  • Qing Li
    • 2
  1. 1.College of Computer ScienceZhejiang UniversityChina
  2. 2.Department of Computer ScienceCity University of Hong KongHong Kong

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