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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
12709
Venue
VLDB
Year
2022
Pagerank
4.1945683e-05
Overall Rank
11,375 | 20.87%
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
8,171 GTS: GPU-based Tree Index for Fast Similarity Search 2024 SIGMOD 4.5688498e-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 0.0005181666
575 Distance-Based Indexing For High-Dimensional Metric Spaces 1997 SIGMOD 0.00019882723
708 Near Neighbor Search in Large Metric Spaces 1995 VLDB 0.00017772684
2,976 Processing a Large Number of Continuous Preference Top-k Queries 2012 SIGMOD 7.789303e-05
4,985 Pivot-based Metric Indexing 2017 VLDB 5.7856648e-05
6,107 Continuously Adaptive Similarity Search 2020 SIGMOD 5.2066612e-05
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