DIDS: Double Indices and Double Summarizations for Fast Similarity Search
Summary: DIDS introduces Double Indices and Double Summarizations: combines traditional segment-based and novel reference-point-based summarizations with a sorted representation to tighten lower bounds and reduce invalid accesses. Uses reference-point clustering with a cost model and a graph-based inter-region index to improve approximate candidate quality and enable scalable, high-precision disk-based exact similarity search on large data-series collections. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Han Hu (Harbin Engineering University)
- 2. Jiye Qiu (Harbin Engineering University)
- 3. Hongzhi Wang (Harbin Engineering University)
- 4. Bin Liang (Harbin Engineering University)
- 5. Songling Zou (Harbin Engineering University)
BibTeX Citation
@article{hu_vldb24,
title = {{DIDS: Double Indices and Double Summarizations for Fast Similarity Search}},
author = {Hu, Han and Qiu, Jiye and Wang, Hongzhi and Liang, Bin and Zou, Songling},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {9},
pages = {2198--2211},
doi = {10.14778/3665844.3665851},
url = {https://doi.org/10.14778/3665844.3665851},
year = {2024}
}
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,615 | A Topology-Aware Localized Update Strategy for Graph-Based ANN Index | 2026 | VLDB | 5.8214312e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 20 of 20 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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