DESIRE: An efficient dynamic cluster-based forest indexing for similarity search in multi-metric spaces

Similarity search fnds similar objects for a given query object based on a certain similarity metric. Similarity search in metric spaces has attracted increasing attention, as the metric space can accommodate any type of data and support fexible distance metrics. However, a metric space only models...

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Main Authors: ZHU, Yifan, CHEN, Lu, GAO, Yunjun, ZHENG, Baihua, WANG, Pengfei
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Language:English
Published: Institutional Knowledge at Singapore Management University 2022
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Online Access:https://ink.library.smu.edu.sg/sis_research/7254
https://ink.library.smu.edu.sg/context/sis_research/article/8257/viewcontent/p2121_gao.pdf
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spelling sg-smu-ink.sis_research-82572022-09-12T10:14:58Z DESIRE: An efficient dynamic cluster-based forest indexing for similarity search in multi-metric spaces ZHU, Yifan CHEN, Lu GAO, Yunjun ZHENG, Baihua WANG, Pengfei Similarity search fnds similar objects for a given query object based on a certain similarity metric. Similarity search in metric spaces has attracted increasing attention, as the metric space can accommodate any type of data and support fexible distance metrics. However, a metric space only models a single data type with a specifc similarity metric. In contrast, a multi-metric space combines multiple metric spaces to simultaneously model a variety of data types and a collection of associated similarity metrics. Thus, a multi-metric space is capable of performing similarity search over any combination of metric spaces. Many studies focus on indexing a single metric space, while only a few aims at indexing multi-metric space to accelerate similarity search. In this paper, we propose DESIRE, an efcient dynamic cluster-based forest index for similarity search in multi-metric spaces. DESIRE frst selects high-quality centers to cluster objects into compact regions, and then employs B+ -trees to efectively index distances between centers and corresponding objects. To support dynamic scenarios, efcient update strategies are developed. Further, we provide fltering techniques to accelerate similarity queries in multi-metric spaces. Extensive experiments on four real datasets demonstrate the superior efciency and scalability of our proposed DESIRE compared with the state-of-the-art multi-metric space indexes. 2022-06-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/7254 info:doi/10.14778/3547305.3547317 https://ink.library.smu.edu.sg/context/sis_research/article/8257/viewcontent/p2121_gao.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Databases and Information Systems
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Databases and Information Systems
spellingShingle Databases and Information Systems
ZHU, Yifan
CHEN, Lu
GAO, Yunjun
ZHENG, Baihua
WANG, Pengfei
DESIRE: An efficient dynamic cluster-based forest indexing for similarity search in multi-metric spaces
description Similarity search fnds similar objects for a given query object based on a certain similarity metric. Similarity search in metric spaces has attracted increasing attention, as the metric space can accommodate any type of data and support fexible distance metrics. However, a metric space only models a single data type with a specifc similarity metric. In contrast, a multi-metric space combines multiple metric spaces to simultaneously model a variety of data types and a collection of associated similarity metrics. Thus, a multi-metric space is capable of performing similarity search over any combination of metric spaces. Many studies focus on indexing a single metric space, while only a few aims at indexing multi-metric space to accelerate similarity search. In this paper, we propose DESIRE, an efcient dynamic cluster-based forest index for similarity search in multi-metric spaces. DESIRE frst selects high-quality centers to cluster objects into compact regions, and then employs B+ -trees to efectively index distances between centers and corresponding objects. To support dynamic scenarios, efcient update strategies are developed. Further, we provide fltering techniques to accelerate similarity queries in multi-metric spaces. Extensive experiments on four real datasets demonstrate the superior efciency and scalability of our proposed DESIRE compared with the state-of-the-art multi-metric space indexes.
format text
author ZHU, Yifan
CHEN, Lu
GAO, Yunjun
ZHENG, Baihua
WANG, Pengfei
author_facet ZHU, Yifan
CHEN, Lu
GAO, Yunjun
ZHENG, Baihua
WANG, Pengfei
author_sort ZHU, Yifan
title DESIRE: An efficient dynamic cluster-based forest indexing for similarity search in multi-metric spaces
title_short DESIRE: An efficient dynamic cluster-based forest indexing for similarity search in multi-metric spaces
title_full DESIRE: An efficient dynamic cluster-based forest indexing for similarity search in multi-metric spaces
title_fullStr DESIRE: An efficient dynamic cluster-based forest indexing for similarity search in multi-metric spaces
title_full_unstemmed DESIRE: An efficient dynamic cluster-based forest indexing for similarity search in multi-metric spaces
title_sort desire: an efficient dynamic cluster-based forest indexing for similarity search in multi-metric spaces
publisher Institutional Knowledge at Singapore Management University
publishDate 2022
url https://ink.library.smu.edu.sg/sis_research/7254
https://ink.library.smu.edu.sg/context/sis_research/article/8257/viewcontent/p2121_gao.pdf
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