SIREN: A Similarity Retrieval Engine for Complex Data
Summary: Similarity queries for complex data are integrated into SQL via SIREN, an in-DB retrieval engine. The paper details SIREN's architecture, key features, and a demo showcasing SQL-level similarity search support. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Maria Camila N. Barioni (University of São Paulo)
- 2. Humberto Razente (University of São Paulo)
- 3. Agma Traina (University of São Paulo)
- 4. Caetano Traina Jr. (University of São Paulo)
BibTeX Citation
@article{barioni_vldb06,
title = {{SIREN: A Similarity Retrieval Engine for Complex Data}},
author = {Barioni, Maria Camila N. and Razente, Humberto and Traina, Agma and Traina, Jr., Caetano},
journal = {PVLDB},
series = {{VLDB} '06},
year = {2006}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,951 | Extensible and Robust Evaluation of Similarity Queries | 2025 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 0 of 0 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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