A Cost Model for Similarity Queries in Metric Spaces
Summary: Cost model for range and k-NN in metric spaces estimating CPU (distance computations) and I/O for M-tree by using global distance distributions under a probabilistic homogeneity assumption. Validated empirically; enables M-tree tuning and sketched extension to vp-tree. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Paolo Ciaccia
- 2. Marco Patella
- 3. Pavel Zezula
Incoming Citations (Sorted by Pagerank)
Showing 6 of 6 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 34 | Similarity Search in High Dimensions via Hashing | 1999 | VLDB | 0.00076824554 |
| 2,160 | PM-LSH: A Fast and Accurate LSH Framework for High-Dimensional Approximate NN Search | 2020 | VLDB | 9.4037759e-05 |
| 3,199 | Return of the Lernaean Hydra: Experimental Evaluation of Data Series Approximate Similarity Search | 2020 | VLDB | 7.3999833e-05 |
| 3,802 | Time-Parameterized Queries in Spatio-Temporal Databases | 2002 | SIGMOD | 6.7524078e-05 |
| 4,524 | Data Series Progressive Similarity Search with Probabilistic Quality Guarantees | 2020 | SIGMOD | 6.1091797e-05 |
| 12,493 | Peer-to-Peer Similarity Search in Metric Spaces | 2007 | VLDB | 4.1905499e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 11 of 11 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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