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: 6920
Venue: SIGMOD
Year: 2024
Pagerank: 5.2525104e-05
Overall Rank: 9,587 | 34.23%
DOI: 10.1145/3639304

Incoming Non-self Citations Over Time

Authors

1. Meghdad Kurmanji (University of Warwick)
2. Eleni Triantafillou (Google)
3. Peter Triantafillou (University of Warwick)

BibTeX Citation

@inproceedings{kurmanji_sigmod24,
        title = {{Machine Unlearning in Learned Databases: An Experimental Analysis}},
        author = {Kurmanji, Meghdad and Triantafillou, Eleni and Triantafillou, Peter},
        series = {{SIGMOD} '24},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3639304},
        url = {https://dl.acm.org/doi/10.1145/3639304},
        year = {2024}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
8,221	NeurDB: On the Design and Implementation of an AI-powered Autonomous Database	2025	CIDR	5.4640314e-05
10,272	NeurBench: A Benchmark Suite for Learned Database Components with Drift Modeling: [Experiments & Analysis]	2026	SIGMOD	5.093636e-05

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
43	The Case for Learned Index Structures	2018	SIGMOD	0.00046060254
84	Learned Cardinalities: Estimating Correlated Joins with Deep Learning	2019	CIDR	0.00035838391
86	Automatic Database Management System Tuning Through Large-scale Machine Learning	2017	SIGMOD	0.00035316107
154	Neo: A Learned Query Optimizer	2019	VLDB	0.00028726181
323	DeepDB: Learn from Data, not from Queries!	2020	VLDB	0.00021264788
334	An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning	2019	SIGMOD	0.00020875082
401	Deep Unsupervised Cardinality Estimation	2020	VLDB	0.00019092557
447	ALEX: An Updatable Adaptive Learned Index	2020	SIGMOD	0.00018322593
498	QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning	2019	VLDB	0.00017440583
513	NeuroCard: One Cardinality Estimator for All Tables	2021	VLDB	0.00017190574
873	Learning Multi-dimensional Indexes	2020	SIGMOD	0.00013481915
1,061	Are We Ready For Learned Cardinality Estimation?	2021	VLDB	0.00012369764
1,174	Tsunami: A Learned Multi-dimensional Index for Correlated Data and Skewed Workloads	2021	VLDB	0.00011817414
1,573	Deep Learning Models for Selectivity Estimation of Multi-Attribute Queries	2020	SIGMOD	0.00010328171
1,799	DBEst: Revisiting Approximate Query Processing Engines with Machine Learning Models	2019	SIGMOD	9.7326398e-05
1,876	Flow-Loss: Learning Cardinality Estimates That Matter	2021	VLDB	9.5717543e-05
1,988	FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation	2021	VLDB	9.3501502e-05
2,129	Data Synthesis based on Generative Adversarial Networks	2018	VLDB	9.1266572e-05
2,822	Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings	2020	SIGMOD	8.0898536e-05
2,991	FactorJoin: A New Cardinality Estimation Framework for Join Queries	2023	SIGMOD	7.8880723e-05
3,953	Efficient Deep Learning Pipelines for Accurate Cost Estimations Over Large Scale Query Workload	2021	SIGMOD	6.996368e-05
4,612	Warper: Efficiently Adapting Learned Cardinality Estimators to Data and Workload Drifts	2022	SIGMOD	6.6072026e-05
4,789	Learned Approximate Query Processing: Make it Light, Accurate and Fast	2021	CIDR	6.5072039e-05
5,551	PGMJoins: Random Join Sampling with Graphical Models	2021	SIGMOD	6.1782856e-05
6,132	Detect, Distill and Update: Learned DB Systems Facing Out of Distribution Data	2023	SIGMOD	5.9660278e-05

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