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Nearest base-neighbor search on spatial datasets

  • Hong-Jun Jang
  • Kyeong-Seok Hyun
  • Jaehwa Chung
  • Soon-Young JungEmail author
Regular Paper
  • 26 Downloads

Abstract

This paper presents a nearest base-neighbor (NBN) search that can be applied to a clustered nearest neighbor problem on spatial datasets with static properties. Given two sets of data points R and S, a query point q, distance threshold δ and cardinality threshold k, the NBN query retrieves a nearest point r (called the base-point) in R where more than k points in S are located within the distance δ. In this paper, we formally define a base-point and NBN problem. As the brute-force approach to this problem in massive datasets has large computational and I/O costs, we propose in-memory and external memory processing techniques for NBN queries. In particular, our proposed in-memory algorithms are used to minimize I/Os in the external memory algorithms. Furthermore, we devise a solution-based index, which we call the neighborhood-augmented grid, to dramatically reduce the search space. A performance study is conducted both on synthetic and real datasets. Our experimental results show the efficiency of our proposed approach.

Keywords

Information technology k-nearest neighbor query Group version of nearest neighbor query Nearest base-neighbor query Spatial databases 

Notes

Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) Grant Funded by the Korean Government (MSIP) (NRF-2016R1A2B1014013) and by Basic Science Research Program through the National Research Foundation of Korea (NRF) Funded by the Ministry of Education (2016R1D1A1B03930907)

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringKorea UniversitySeoulKorea
  2. 2.Department of Computer ScienceKorea National Open UniversitySeoulKorea

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