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Popularity-Based Ranking for Fast Approximate kNN Search
Volume 28, Issue 1 (2017), pp. 1–21
Pub. online: 1 January 2017      Type: Research Article      Open accessOpen Access
Received
1 October 2016
Accepted
1 February 2017
Published
1 January 2017

Abstract

Similarity searching has become widely available in many on-line archives of multimedia data. Users accessing such systems look for data items similar to their specific query object and typically refine results by re-running the search with a query from the results. We study this issue and propose a mechanism of approximate kNN query evaluation that incorporates statistics of accessing index data partitions. Apart from the distance between database objects, it also considers the prior query answers to prioritize index partitions containing frequently retrieved data, so evaluating repetitive similar queries more efficiently. We verify this concept in a number of experiments.

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Biographies

Antol Matej
xantol@fi.muni.cz

M. Antol is a PhD student at the Faculty of Informatics, Masaryk University in Brno, Czech Republic. His area of interest is data analytics, namely optimization of hierarchical structures and their performance in metric spaces.

Dohnal Vlastislav
dohnal@fi.muni.cz

V. Dohnal is an associate professor at the Faculty of Informatics, Masaryk University. He has been interested in database systems and information retrieval. In particular, indexing principles and mechanisms for unstructured data modelled as a metric space.


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Open access article under the CC BY license.

Keywords
kNN query approximate search query popularity index structure metric space

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