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DESIRE: An Efficient Dynamic Cluster-based Forest Indexing for Similarity Search in Multi-Metric Spaces

Summary: DESIRE: dynamic cluster-based forest index for multi-metric similarity search. Builds compact centers, indexes center–object distances with B+-trees, supports dynamic updates, and uses filtering to speed multi-metric queries, outperforming prior indexes. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
12710
Venue
VLDB
Year
2022
Pagerank
4.1905499e-05
Overall Rank
11,377 | 20.93%
DOI
10.14778/3547305.3547317

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Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
8,167 GTS: GPU-based Tree Index for Fast Similarity Search 2024 SIGMOD 4.567569e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 6 of 6 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
91 M-tree: An Efficient Access Method for Similarity Search in Metric Spaces 1997 VLDB 0.00051785122
574 Distance-Based Indexing For High-Dimensional Metric Spaces 1997 SIGMOD 0.0001987713
707 Near Neighbor Search in Large Metric Spaces 1995 VLDB 0.00017770532
2,967 Processing a Large Number of Continuous Preference Top-k Queries 2012 SIGMOD 7.7975455e-05
5,086 Pivot-based Metric Indexing 2017 VLDB 5.7036022e-05
6,111 Continuously Adaptive Similarity Search 2020 SIGMOD 5.2016636e-05
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