Index Intersection for High-Dimensional Range Queries
Summary: Team-based Indexing: build lightweight indices over medium-sized attribute groups and intersect them to produce candidate tuple IDs for high-dimensional, highly-selective range queries. 5-attribute Teams beat bitmaps (6–7× faster, 1.58–2.07× less storage for 85-D), shifting optimization from per-index accuracy to efficient intersection. (summarized by gpt-5-mini on Mar 13 2026)
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6 | The R*-tree: An Efficient and Robust Access Method for Points and Rectangles | 1990 | SIGMOD | 0.0016162015 |
| 79 | A Quantitative Analysis and Performance Study for Similarity-Search Methods in High-Dimensional Spaces | 1998 | VLDB | 0.00056242144 |
| 129 | The X-tree: An Index Structure for High-Dimensional Data | 1996 | VLDB | 0.0004429571 |
| 2,862 | An Experimental Study of Bitmap Compression vs. Inverted List Compression | 2017 | SIGMOD | 7.9898539e-05 |
| 2,914 | DDSketch: A Fast and Fully-Mergeable Quantile Sketch with Relative-Error Guarantees | 2019 | VLDB | 7.9118579e-05 |
| 3,449 | Learned Cardinality Estimation: A Design Space Exploration and A Comparative Evaluation | 2022 | VLDB | 7.0824319e-05 |
| 8,156 | List Intersection for Web Search: Algorithms, Cost Models, and Optimizations | 2019 | VLDB | 4.5741172e-05 |
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