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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Authors
- 1. Maximilian Berens (TU Dortmund University)
- 2. Jens Teubner (Lamarr Institute for Machine Learning and Artificial Intelligence; TU Dortmund University)
BibTeX Citation
@article{berens_vldb26,
title = {{Index Intersection for High-Dimensional Range Queries}},
author = {Berens, Maximilian and Teubner, Jens},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {4},
pages = {767--779},
doi = {10.14778/3785297.3785315},
url = {https://doi.org/10.14778/3785297.3785315},
year = {2026}
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4 | The R*-tree: An Efficient and Robust Access Method for Points and Rectangles | 1990 | SIGMOD | 0.001157935 |
| 46 | A Quantitative Analysis and Performance Study for Similarity-Search Methods in High-Dimensional Spaces | 1998 | VLDB | 0.00044853085 |
| 85 | The X-tree: An Index Structure for High-Dimensional Data | 1996 | VLDB | 0.00035405879 |
| 2,224 | An Experimental Study of Bitmap Compression vs. Inverted List Compression | 2017 | SIGMOD | 8.9183396e-05 |
| 2,455 | DDSketch: A Fast and Fully-Mergeable Quantile Sketch with Relative-Error Guarantees | 2019 | VLDB | 8.5552968e-05 |
| 2,723 | Learned Cardinality Estimation: A Design Space Exploration and A Comparative Evaluation | 2022 | VLDB | 8.2049453e-05 |
| 8,201 | List Intersection for Web Search: Algorithms, Cost Models, and Optimizations | 2019 | VLDB | 5.467444e-05 |
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