Aggregate k Nearest Neighbor Queries in Metric Spaces

  • Xin Ding
  • Yuanliang Zhang
  • Lu Chen
  • Keyu Yang
  • Yunjun GaoEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10988)


Aggregate k nearest neighbor (AkNN) queries are useful in many areas, such as multimedia retrieval and resource allocation, to name but a few. Most of existing works on AkNN query only focus on Euclidean space or specific metric space, which employ properties of particular data to accelerate the query. However, due to the complex data types involved and the needs for flexible similarity criteria seen in real applications, properties of particular data cannot be used for general case. Hence, in this paper, we investigate AkNN search in metric spaces, termed as metric AkNN (MAkNN) search, as metric spaces can support any type of data and flexible similarity criteria as long as satisfying triangle inequality. To efficiently answer MAkNN queries, we develop several pruning techniques and corresponding algorithms based on SPB-tree. Extensive experiments using three real data sets verify the efficiency of our MAkNN algorithms.


Metric space Aggregate k nearest neighbor query Algorithm 



This work was supported in part by the 973 Program of China under Grant No. 2015CB352502, the NSFC under Grant No. 61522208, the NSFC-Zhejiang Joint Fund under Grant No. U1609217, and the ZJU-Hikvision Joint Project.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Xin Ding
    • 1
  • Yuanliang Zhang
    • 1
  • Lu Chen
    • 2
  • Keyu Yang
    • 1
  • Yunjun Gao
    • 1
    Email author
  1. 1.College of Computer ScienceZhejiang UniversityHangzhouChina
  2. 2.Department of Computer ScienceAalborg UniversityAalborgDenmark

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