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: hec4c84a507c3ca03
Venue: SIGMOD
Year: 2024
Pagerank: 5.2532248e-05
Overall Rank: 8,947 | 39.85%
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 3 of 3 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
8,385	NeurDB: On the Design and Implementation of an AI-powered Autonomous Database	2025	CIDR	5.3420959e-05
10,484	NeurBench: A Benchmark Suite for Learned Database Components with Drift Modeling: [Experiments & Analysis]	2026	SIGMOD	4.9793485e-05
10,798	NeurIDA: Dynamic Modeling for Effective In-Database Analytics	2026	VLDB	4.9793485e-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
40	The Case for Learned Index Structures	2018	SIGMOD	0.00046284649
78	Automatic Database Management System Tuning Through Large-scale Machine Learning	2017	SIGMOD	0.00036684414
85	Learned Cardinalities: Estimating Correlated Joins with Deep Learning	2019	CIDR	0.00035864347
145	Neo: A Learned Query Optimizer	2019	VLDB	0.0002908188
314	An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning	2019	SIGMOD	0.00021282642
318	DeepDB: Learn from Data, not from Queries!	2020	VLDB	0.00021167555
406	Deep Unsupervised Cardinality Estimation	2020	VLDB	0.00019045544
430	ALEX: An Updatable Adaptive Learned Index	2020	SIGMOD	0.00018409112
437	QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning	2019	VLDB	0.00018315867
512	NeuroCard: One Cardinality Estimator for All Tables	2021	VLDB	0.00017050173
868	Learning Multi-dimensional Indexes	2020	SIGMOD	0.00013354403
1,064	Are We Ready For Learned Cardinality Estimation?	2021	VLDB	0.00012202282
1,191	Tsunami: A Learned Multi-dimensional Index for Correlated Data and Skewed Workloads	2021	VLDB	0.00011590153
1,580	Deep Learning Models for Selectivity Estimation of Multi-Attribute Queries	2020	SIGMOD	0.00010180835
1,734	Flow-Loss: Learning Cardinality Estimates That Matter	2021	VLDB	9.7545773e-05
1,829	DBEst: Revisiting Approximate Query Processing Engines with Machine Learning Models	2019	SIGMOD	9.5510333e-05
2,004	FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation	2021	VLDB	9.2065719e-05
2,165	Data Synthesis based on Generative Adversarial Networks	2018	VLDB	8.933677e-05
2,834	Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings	2020	SIGMOD	7.9560627e-05
2,846	FactorJoin: A New Cardinality Estimation Framework for Join Queries	2023	SIGMOD	7.9453616e-05
3,978	Efficient Deep Learning Pipelines for Accurate Cost Estimations Over Large Scale Query Workload	2021	SIGMOD	6.8807882e-05
4,563	Warper: Efficiently Adapting Learned Cardinality Estimators to Data and Workload Drifts	2022	SIGMOD	6.5320994e-05
4,781	Learned Approximate Query Processing: Make it Light, Accurate and Fast	2021	CIDR	6.4162085e-05
5,657	PGMJoins: Random Join Sampling with Graphical Models	2021	SIGMOD	6.0488437e-05
5,716	Detect, Distill and Update: Learned DB Systems Facing Out of Distribution Data	2023	SIGMOD	6.0194657e-05

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