HAP: An Efficient Hamming Space Index Based on Augmented Pigeonhole Principle
Summary: Relax disjoint partitioning of binary vectors by allowing dimension redundancy to form Augmented Pigeonhole Principle (APP) for tighter Hamming pruning. HAP combines APP, SimCardNet, PLA-Elias-Fano compression, and batch optimization to support Hamming range and k-NN with improved space/time efficiency on large binary DBs. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Qiyu Liu
- 2. Yanyan Shen
- 3. Lei Chen
Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9,746 | Why Are Learned Indexes So Effective but Sometimes Ineffective? | 2025 | VLDB | 4.2897489e-05 |
| 10,698 | Not Small Enough? SegPQ: A Learned Approach to Compress Product Quantization Codebooks | 2025 | VLDB | 4.1945683e-05 |
| 10,833 | Cardinality Estimation for Similarity Search on High-Dimensional Data Objects: The Impact of Reference Objects | 2025 | VLDB | 4.1945683e-05 |
| 11,247 | A Two-Level Signature Scheme for Stable Set Similarity Joins | 2023 | VLDB | 4.1945683e-05 |
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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