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Machine Unlearning in Learned Databases: An Experimental Analysis
Summary: Machine unlearning for learned databases; handles deletes and updates. Experiments compare unlearning methods across SE, AQP, DG, DC; evaluate overhead, batching deletes, and interplay with inserts, proposing benchmark for learned-DB unlearning.
(summarized by gpt-5-nano on Feb 09 2026)
- Paper ID
- 6859
- Venue
- SIGMOD
- Year
- 2024
- Pagerank
- 4.2685233e-05
- Overall Rank
- 9,841 | 31.61%
- DOI
-
10.1145/3639304
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 25 of 25 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
| 101 |
The Case for Learned Index Structures |
2018 |
SIGMOD |
0.00049778866 |
| 183 |
Automatic Database Management System Tuning Through Large-scale Machine Learning |
2017 |
SIGMOD |
0.00036859633 |
| 203 |
Learned Cardinalities: Estimating Correlated Joins with Deep Learning |
2019 |
CIDR |
0.00034868567 |
| 329 |
Neo: A Learned Query Optimizer |
2019 |
VLDB |
0.00027301488 |
| 510 |
An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning |
2019 |
SIGMOD |
0.00021420477 |
| 606 |
DeepDB: Learn from Data, not from Queries! |
2020 |
VLDB |
0.00019251186 |
| 752 |
Deep Unsupervised Cardinality Estimation |
2020 |
VLDB |
0.00017138049 |
| 779 |
QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning |
2019 |
VLDB |
0.00016719473 |
| 819 |
ALEX: An Updatable Adaptive Learned Index |
2020 |
SIGMOD |
0.00016237497 |
| 905 |
NeuroCard: One Cardinality Estimator for All Tables |
2021 |
VLDB |
0.00015423174 |
| 1,464 |
Learning Multi-dimensional Indexes |
2020 |
SIGMOD |
0.0001184772 |
| 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 |
| 2,364 |
Deep Learning Models for Selectivity Estimation of Multi-Attribute Queries |
2020 |
SIGMOD |
8.955077e-05 |
| 2,423 |
Data Synthesis based on Generative Adversarial Networks |
2018 |
VLDB |
8.8447357e-05 |
| 2,494 |
DBEst: Revisiting Approximate Query Processing Engines with Machine Learning Models |
2019 |
SIGMOD |
8.6457436e-05 |
| 2,769 |
FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation |
2021 |
VLDB |
8.1512848e-05 |
| 2,781 |
Flow-Loss: Learning Cardinality Estimates That Matter |
2021 |
VLDB |
8.1282042e-05 |
| 3,623 |
Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings |
2020 |
SIGMOD |
6.9017341e-05 |
| 3,992 |
FactorJoin: A New Cardinality Estimation Framework for Join Queries |
2023 |
SIGMOD |
6.5519369e-05 |
| 4,800 |
Efficient Deep Learning Pipelines for Accurate Cost Estimations Over Large Scale Query Workload |
2021 |
SIGMOD |
5.9077188e-05 |
| 5,528 |
Warper: Efficiently Adapting Learned Cardinality Estimators to Data and Workload Drifts |
2022 |
SIGMOD |
5.4571136e-05 |
| 5,799 |
Learned Approximate Query Processing: Make it Light, Accurate and Fast |
2021 |
CIDR |
5.3219666e-05 |
| 5,952 |
PGMJoins: Random Join Sampling with Graphical Models |
2021 |
SIGMOD |
5.2547498e-05 |
| 6,860 |
Detect, Distill and Update: Learned DB Systems Facing Out of Distribution Data |
2023 |
SIGMOD |
4.9008421e-05 |
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6.8700949e-05 |
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4.9008421e-05 |