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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
- 6858
- Venue
- SIGMOD
- Year
- 2024
- Pagerank
- 4.2714575e-05
- Overall Rank
- 9,852 | 31.47%
- 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 |
| 102 |
The Case for Learned Index Structures |
2018 |
SIGMOD |
0.00049545203 |
| 183 |
Automatic Database Management System Tuning Through Large-scale Machine Learning |
2017 |
SIGMOD |
0.00036721403 |
| 204 |
Learned Cardinalities: Estimating Correlated Joins with Deep Learning |
2019 |
CIDR |
0.00034784455 |
| 333 |
Neo: A Learned Query Optimizer |
2019 |
VLDB |
0.00027206884 |
| 514 |
An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning |
2019 |
SIGMOD |
0.0002124895 |
| 608 |
DeepDB: Learn from Data, not from Queries! |
2020 |
VLDB |
0.00019235898 |
| 758 |
Deep Unsupervised Cardinality Estimation |
2020 |
VLDB |
0.0001706608 |
| 782 |
QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning |
2019 |
VLDB |
0.00016729063 |
| 826 |
ALEX: An Updatable Adaptive Learned Index |
2020 |
SIGMOD |
0.00016224841 |
| 910 |
NeuroCard: One Cardinality Estimator for All Tables |
2021 |
VLDB |
0.00015423056 |
| 1,478 |
Learning Multi-dimensional Indexes |
2020 |
SIGMOD |
0.00011762542 |
| 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 |
| 2,364 |
Deep Learning Models for Selectivity Estimation of Multi-Attribute Queries |
2020 |
SIGMOD |
8.9554751e-05 |
| 2,421 |
Data Synthesis based on Generative Adversarial Networks |
2018 |
VLDB |
8.8514021e-05 |
| 2,501 |
DBEst: Revisiting Approximate Query Processing Engines with Machine Learning Models |
2019 |
SIGMOD |
8.6453446e-05 |
| 2,762 |
FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation |
2021 |
VLDB |
8.1585394e-05 |
| 2,783 |
Flow-Loss: Learning Cardinality Estimates That Matter |
2021 |
VLDB |
8.1293383e-05 |
| 3,625 |
Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings |
2020 |
SIGMOD |
6.9055212e-05 |
| 3,990 |
FactorJoin: A New Cardinality Estimation Framework for Join Queries |
2023 |
SIGMOD |
6.5581983e-05 |
| 4,804 |
Efficient Deep Learning Pipelines for Accurate Cost Estimations Over Large Scale Query Workload |
2021 |
SIGMOD |
5.910467e-05 |
| 5,645 |
Warper: Efficiently Adapting Learned Cardinality Estimators to Data and Workload Drifts |
2022 |
SIGMOD |
5.3923454e-05 |
| 5,951 |
PGMJoins: Random Join Sampling with Graphical Models |
2021 |
SIGMOD |
5.2592385e-05 |
| 6,230 |
Learned Approximate Query Processing: Make it Light, Accurate and Fast |
2021 |
CIDR |
5.145989e-05 |
| 6,879 |
Detect, Distill and Update: Learned DB Systems Facing Out of Distribution Data |
2023 |
SIGMOD |
4.8971368e-05 |
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