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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
- 6825
- Venue
- SIGMOD
- Year
- 2024
- Pagerank
- 4.4195459e-05
- Overall Rank
- 8,952 | 37.79%
- 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 |
0.0018380955 |
| 6 |
The R*-tree: An Efficient and Robust Access Method for Points and Rectangles |
1990 |
SIGMOD |
0.0016113151 |
| 32 |
Differential Files: Their Application To The Maintenance Of Large Data Bases |
1976 |
SIGMOD |
0.00077553033 |
| 63 |
Improved Histograms for Selectivity Estimation of Range Predicates |
1996 |
SIGMOD |
0.00063595699 |
| 71 |
How Good Are Query Optimizers, Really? |
2016 |
VLDB |
0.00059446482 |
| 101 |
The Case for Learned Index Structures |
2018 |
SIGMOD |
0.00049778866 |
| 606 |
DeepDB: Learn from Data, not from Queries! |
2020 |
VLDB |
0.00019251186 |
| 752 |
Deep Unsupervised Cardinality Estimation |
2020 |
VLDB |
0.00017138049 |
| 905 |
NeuroCard: One Cardinality Estimator for All Tables |
2021 |
VLDB |
0.00015423174 |
| 1,365 |
FITing-Tree: A Data-aware Index Structure |
2019 |
SIGMOD |
0.00012379754 |
| 1,372 |
Random Sampling over Joins Revisited |
2018 |
SIGMOD |
0.0001233325 |
| 1,699 |
Are We Ready For Learned Cardinality Estimation? |
2021 |
VLDB |
0.00010848882 |
| 1,887 |
Tsunami: A Learned Multi-dimensional Index for Correlated Data and Skewed Workloads |
2021 |
VLDB |
0.00010201938 |
| 2,492 |
Integrating the UB-Tree into a Database System Kernel |
2000 |
VLDB |
8.649434e-05 |
| 2,550 |
Updatable Learned Index with Precise Positions |
2021 |
VLDB |
8.5569576e-05 |
| 2,676 |
Effectively Learning Spatial Indices |
2020 |
VLDB |
8.326321e-05 |
| 2,954 |
Moment-Based Quantile Sketches for Efficient High Cardinality Aggregation Queries |
2018 |
VLDB |
7.8218804e-05 |
| 3,455 |
Learned Cardinality Estimation: A Design Space Exploration and A Comparative Evaluation |
2022 |
VLDB |
7.0760196e-05 |
| 4,382 |
HTAP Databases: What is New and What is Next |
2022 |
SIGMOD |
6.2318984e-05 |
| 5,908 |
Building Wavelet Histograms on Large Data in MapReduce |
2012 |
VLDB |
5.2731311e-05 |
| 6,806 |
LMSFC: A Novel Multidimensional Index based on Learned Monotonic Space Filling Curves |
2023 |
VLDB |
4.917024e-05 |
| 8,354 |
Waffle: A Workload-Aware and Query-Sensitive Framework for Disk-Based Spatial Indexing |
2023 |
VLDB |
4.5329424e-05 |
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| 5,477 |
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SIGMOD |
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| 10,598 |
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VLDB |
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| 6,365 |
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5.0892829e-05 |
| 1,699 |
Are We Ready For Learned Cardinality Estimation? |
2021 |
VLDB |
0.00010848882 |
| 9,947 |
SSCard: Substring Cardinality Estimation using Suffix Tree-Guided Learned FM-Index |
2026 |
SIGMOD |
4.2391999e-05 |