The VLDB Journal

, Volume 25, Issue 3, pp 317–338

Exact and approximate flexible aggregate similarity search

  • Feifei Li
  • Ke Yi
  • Yufei Tao
  • Bin Yao
  • Yang Li
  • Dong Xie
  • Min Wang
Regular Paper

DOI: 10.1007/s00778-015-0418-x

Cite this article as:
Li, F., Yi, K., Tao, Y. et al. The VLDB Journal (2016) 25: 317. doi:10.1007/s00778-015-0418-x
  • 524 Downloads

Abstract

Aggregate similarity search, also known as aggregate nearest-neighbor (Ann) query, finds many useful applications in spatial and multimedia databases. Given a group Q of M query objects, it retrieves from a database the objects most similar to Q, where the similarity is an aggregation (e.g., \({{\mathrm{sum}}}\), \(\max \)) of the distances between each retrieved object p and all the objects in Q. In this paper, we propose an added flexibility to the query definition, where the similarity is an aggregation over the distances between p and any subset of \(\phi M\) objects in Q for some support\(0< \phi \le 1\). We call this new definition flexible aggregate similarity search and accordingly refer to a query as a flexible aggregate nearest-neighbor (Fann) query. We present algorithms for answering Fann queries exactly and approximately. Our approximation algorithms are especially appealing, which are simple, highly efficient, and work well in both low and high dimensions. They also return near-optimal answers with guaranteed constant-factor approximations in any dimensions. Extensive experiments on large real and synthetic datasets from 2 to 74 dimensions have demonstrated their superior efficiency and high quality.

Keywords

Aggregate nearest neighbor query Approximate similarity search Aggregate similarity search 

Funding information

Funder NameGrant NumberFunding Note
Directorate for Computer and Information Science and Engineering
  • 1251019
Directorate for Computer and Information Science and Engineering
  • 1200792
National Natural Science Foundation of China
  • 61428204
National Natural Science Foundation of China
  • 61202025
National Basic Research Program of China (973 program)
  • 2015CB352403
The Scientific Innovation Act of STCSM
  • 13511504200
  • 15JC1402400
the EU FP7 CLIMBER project
  • PIRSES-GA-2012-318939

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Feifei Li
    • 1
  • Ke Yi
    • 2
  • Yufei Tao
    • 3
  • Bin Yao
    • 4
  • Yang Li
    • 4
  • Dong Xie
    • 4
  • Min Wang
    • 5
  1. 1.University of UtahSalt Lake CityUSA
  2. 2.Hong Kong University of Science and TechnologyHong KongChina
  3. 3.Chinese University of Hong KongHong KongChina
  4. 4.Shanghai Key Laboratory of Scalable Computing and SystemsShanghai Jiao Tong UniversityShanghaiChina
  5. 5.Visa Research, Visa Inc.Foster CityUSA

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