Exact and approximate flexible aggregate similarity search

Feifei Li, Ke Yi, Yufei Tao, Bin Yao, Yang Li, Dong Xie, Min Wang

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

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., 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 ϕM objects in Q for some support0 < ϕ≤ 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.

Original languageEnglish (US)
Pages (from-to)317-338
Number of pages22
JournalVLDB Journal
Volume25
Issue number3
DOIs
StatePublished - Jun 1 2016

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Hardware and Architecture

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