Detect, Distill and Update: Learned DB Systems Facing Out of Distribution Data

Summary: DDUp is an updatability framework for learned DB components facing insertion-driven shifts, pairing OOD detection with efficient updates. It uses a test to flag OOD data and a distillation-based update to keep AQP, CE, DG accurate without retraining. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: h25cfda59f2c4ca76
Venue: SIGMOD
Year: 2023
Pagerank: 6.0178585e-05
Overall Rank: 5,715 | 61.59%
DOI: 10.1145/3588713

Incoming Non-self Citations Over Time

Authors

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

BibTeX Citation

@inproceedings{kurmanji_sigmod23,
        title = {{Detect, Distill and Update: Learned DB Systems Facing Out of Distribution Data}},
        author = {Kurmanji, Meghdad and Triantafillou, Peter},
        series = {{SIGMOD} '23},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3588713},
        url = {https://dl.acm.org/doi/10.1145/3588713},
        year = {2023}
}

Incoming Citations (Sorted by Pagerank)

Showing 12 of 12 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
5,438	Eraser: Eliminating Performance Regression on Learned Query Optimizer	2024	VLDB	6.1278045e-05
5,850	Modeling Shifting Workloads for Learned Database Systems	2024	SIGMOD	5.9697921e-05
7,072	E2ETune: End-to-End Knob Tuning via Fine-tuned Generative Language Model	2025	VLDB	5.6053987e-05
8,032	NeurDB: On the Design and Implementation of an AI-powered Autonomous Database	2025	CIDR	5.4017794e-05
8,808	PACE: Poisoning Attacks on Learned Cardinality Estimation	2024	SIGMOD	5.2717563e-05
8,952	Machine Unlearning in Learned Databases: An Experimental Analysis	2024	SIGMOD	5.2517414e-05
9,782	Understanding Robustness Issues of Updatable Learned Indexes: [Experiments & Analysis]	2026	SIGMOD	5.129066e-05
10,495	NeurBench: A Benchmark Suite for Learned Database Components with Drift Modeling: [Experiments & Analysis]	2026	SIGMOD	4.9769913e-05
10,543	Rainbow: Risk-aware Index Benefit Estimation Facing Out Of Distribution Workloads	2026	SIGMOD	4.9769913e-05
10,808	NeurIDA: Dynamic Modeling for Effective In-Database Analytics	2026	VLDB	4.9769913e-05
10,812	BaCon: Efficient Batch Processing of Counting Queries	2026	VLDB	4.9769913e-05
10,933	TATA: An Efficient Framework for Task Transfer in Query Plan Representation	2026	VLDB	4.9769913e-05

Outgoing Citations (Sorted by Pagerank)

Showing 27 of 27 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	0.00046363107
78	Automatic Database Management System Tuning Through Large-scale Machine Learning	2017	SIGMOD	0.00036675568
85	Learned Cardinalities: Estimating Correlated Joins with Deep Learning	2019	CIDR	0.00035876108
144	Neo: A Learned Query Optimizer	2019	VLDB	0.00029090793
314	An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning	2019	SIGMOD	0.00021276452
318	DeepDB: Learn from Data, not from Queries!	2020	VLDB	0.00021166957
361	Bao: Making Learned Query Optimization Practical	2021	SIGMOD	0.00020000855
406	Deep Unsupervised Cardinality Estimation	2020	VLDB	0.00019050182
422	ALEX: An Updatable Adaptive Learned Index	2020	SIGMOD	0.00018488849
437	QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning	2019	VLDB	0.00018310278
510	NeuroCard: One Cardinality Estimator for All Tables	2021	VLDB	0.00017059914
795	Random Sampling over Joins Revisited	2018	SIGMOD	0.00013934719
869	Learning Multi-dimensional Indexes	2020	SIGMOD	0.00013363241
1,065	Are We Ready For Learned Cardinality Estimation?	2021	VLDB	0.00012202293
1,188	Tsunami: A Learned Multi-dimensional Index for Correlated Data and Skewed Workloads	2021	VLDB	0.00011598149
1,580	Deep Learning Models for Selectivity Estimation of Multi-Attribute Queries	2020	SIGMOD	0.00010177136
1,828	DBEst: Revisiting Approximate Query Processing Engines with Machine Learning Models	2019	SIGMOD	9.547768e-05
2,002	FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation	2021	VLDB	9.2076835e-05
2,167	Data Synthesis based on Generative Adversarial Networks	2018	VLDB	8.9299807e-05
2,278	Active Learning for ML Enhanced Database Systems	2020	SIGMOD	8.7057608e-05
2,833	Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings	2020	SIGMOD	7.9539771e-05
3,679	Detecting Change in Data Streams	2004	VLDB	7.1016619e-05
3,979	Efficient Deep Learning Pipelines for Accurate Cost Estimations Over Large Scale Query Workload	2021	SIGMOD	6.8791817e-05
4,536	One Model to Rule them All: Towards Zero-Shot Learning for Databases	2022	CIDR	6.552998e-05
4,779	Learned Approximate Query Processing: Make it Light, Accurate and Fast	2021	CIDR	6.416435e-05
5,657	PGMJoins: Random Join Sampling with Graphical Models	2021	SIGMOD	6.0461e-05
6,408	A Unified Transferable Model for ML-Enhanced DBMS	2022	CIDR	5.7921918e-05

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1	5,905	On Efficient Approximate Queries over Machine Learning Models	2023	VLDB
2	3,327	Robust Query Driven Cardinality Estimation under Changing Workloads	2023	VLDB
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