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One Seed, Two Birds: A Unified Learned Structure for Exact and Approximate Counting
Summary: CardIndex: unified learned structure for exact and approximate counting, a multidimensional index and adaptive estimator. Training/updating two orders faster; low-selectivity accuracy up to 314×; range queries 20% faster; indexing faster than RSMI/R-tree.
(summarized by gpt-5-nano on Feb 09 2026)
- Paper ID
- 6824
- Venue
- SIGMOD
- Year
- 2024
- Pagerank
- 4.423786e-05
- Overall Rank
- 8,948 | 37.76%
- DOI
-
10.1145/3639270
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 22 of 22 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
| 5 |
The K-D-B-Tree: A Search Structure for Large Multidimensional Dynamic Indexes |
1981 |
SIGMOD |
0.0018397217 |
| 6 |
The R*-tree: An Efficient and Robust Access Method for Points and Rectangles |
1990 |
SIGMOD |
0.0016162015 |
| 32 |
Differential Files: Their Application To The Maintenance Of Large Data Bases |
1976 |
SIGMOD |
0.00077486306 |
| 64 |
Improved Histograms for Selectivity Estimation of Range Predicates |
1996 |
SIGMOD |
0.00063612837 |
| 71 |
How Good Are Query Optimizers, Really? |
2016 |
VLDB |
0.00059038975 |
| 102 |
The Case for Learned Index Structures |
2018 |
SIGMOD |
0.00049545203 |
| 608 |
DeepDB: Learn from Data, not from Queries! |
2020 |
VLDB |
0.00019235898 |
| 758 |
Deep Unsupervised Cardinality Estimation |
2020 |
VLDB |
0.0001706608 |
| 910 |
NeuroCard: One Cardinality Estimator for All Tables |
2021 |
VLDB |
0.00015423056 |
| 1,369 |
Random Sampling over Joins Revisited |
2018 |
SIGMOD |
0.00012339777 |
| 1,375 |
FITing-Tree: A Data-aware Index Structure |
2019 |
SIGMOD |
0.00012303141 |
| 1,703 |
Are We Ready For Learned Cardinality Estimation? |
2021 |
VLDB |
0.00010836769 |
| 1,889 |
Tsunami: A Learned Multi-dimensional Index for Correlated Data and Skewed Workloads |
2021 |
VLDB |
0.00010200865 |
| 2,502 |
Integrating the UB-Tree into a Database System Kernel |
2000 |
VLDB |
8.6438339e-05 |
| 2,552 |
Updatable Learned Index with Precise Positions |
2021 |
VLDB |
8.5530411e-05 |
| 2,678 |
Effectively Learning Spatial Indices |
2020 |
VLDB |
8.3252088e-05 |
| 2,953 |
Moment-Based Quantile Sketches for Efficient High Cardinality Aggregation Queries |
2018 |
VLDB |
7.8267643e-05 |
| 3,449 |
Learned Cardinality Estimation: A Design Space Exploration and A Comparative Evaluation |
2022 |
VLDB |
7.0824319e-05 |
| 4,284 |
HTAP Databases: What is New and What is Next |
2022 |
SIGMOD |
6.2914924e-05 |
| 5,903 |
Building Wavelet Histograms on Large Data in MapReduce |
2012 |
VLDB |
5.2791351e-05 |
| 7,042 |
LMSFC: A Novel Multidimensional Index based on Learned Monotonic Space Filling Curves |
2023 |
VLDB |
4.8541986e-05 |
| 8,359 |
Waffle: A Workload-Aware and Query-Sensitive Framework for Disk-Based Spatial Indexing |
2023 |
VLDB |
4.5372925e-05 |
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| 5,469 |
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SIGMOD |
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| 10,590 |
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VLDB |
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| 6,368 |
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5.0937722e-05 |
| 1,703 |
Are We Ready For Learned Cardinality Estimation? |
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VLDB |
0.00010836769 |
| 9,945 |
SSCard: Substring Cardinality Estimation using Suffix Tree-Guided Learned FM-Index |
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SIGMOD |
4.2432653e-05 |