Efficiency-Quality Tradeoffs for Vector Score Aggregation
Summary: Multi-space vector NN with weighted aggregation for XML/image retrieval. Proposes two schemes: resource-allocation inspired and a geometry-based unified-space approximation—achieving near-exhaustive quality at reduced compute; experiments confirm. (summarized by gpt-5-nano on Feb 09 2026)
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2 | R-Trees: A Dynamic Index Structure For Spatial Searching | 1984 | SIGMOD | 0.0032118946 |
| 6 | The R*-tree: An Efficient and Robust Access Method for Points and Rectangles | 1990 | SIGMOD | 0.0016113151 |
| 8 | Optimal Aggregation Algorithms for Middleware [Extended Abstract] | 2001 | PODS | 0.0015436578 |
| 24 | The R+-Tree: A Dynamic Index For Multi-Dimensional Objects | 1987 | VLDB | 0.00083300888 |
| 129 | The X-tree: An Index Structure for High-Dimensional Data | 1996 | VLDB | 0.00044255276 |
| 301 | The SR-tree: An Index Structure for High-Dimensional Nearest Neighbor Queries | 1997 | SIGMOD | 0.00028396714 |
| 710 | Efficient Similarity Search and Classification via Rank Aggregation | 2003 | SIGMOD | 0.00017727016 |
| 3,097 | Optimized Query Execution in Large Search Engines with Global Page Ordering | 2003 | VLDB | 7.5745942e-05 |
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