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: 6.0194657e-05
Overall Rank: 5,716 | 61.57%
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
5,456	Eraser: Eliminating Performance Regression on Learned Query Optimizer	2024	VLDB	6.1239873e-05
5,865	Modeling Shifting Workloads for Learned Database Systems	2024	SIGMOD	5.9659203e-05
7,070	E2ETune: End-to-End Knob Tuning via Fine-tuned Generative Language Model	2025	VLDB	5.6080535e-05
8,385	NeurDB: On the Design and Implementation of an AI-powered Autonomous Database	2025	CIDR	5.3420959e-05
8,800	PACE: Poisoning Attacks on Learned Cardinality Estimation	2024	SIGMOD	5.2742531e-05
8,947	Machine Unlearning in Learned Databases: An Experimental Analysis	2024	SIGMOD	5.2532248e-05
9,777	Understanding Robustness Issues of Updatable Learned Indexes: [Experiments & Analysis]	2026	SIGMOD	5.1314952e-05
10,484	NeurBench: A Benchmark Suite for Learned Database Components with Drift Modeling: [Experiments & Analysis]	2026	SIGMOD	4.9793485e-05
10,532	Rainbow: Risk-aware Index Benefit Estimation Facing Out Of Distribution Workloads	2026	SIGMOD	4.9793485e-05
10,798	NeurIDA: Dynamic Modeling for Effective In-Database Analytics	2026	VLDB	4.9793485e-05
10,802	BaCon: Efficient Batch Processing of Counting Queries	2026	VLDB	4.9793485e-05
10,924	TATA: An Efficient Framework for Task Transfer in Query Plan Representation	2026	VLDB	4.9793485e-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	0.00046284649
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
362	Bao: Making Learned Query Optimization Practical	2021	SIGMOD	0.00019989474
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
795	Random Sampling over Joins Revisited	2018	SIGMOD	0.00013938779
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
1,829	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
2,275	Active Learning for ML Enhanced Database Systems	2020	SIGMOD	8.7090584e-05
2,834	Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings	2020	SIGMOD	7.9560627e-05
3,677	Detecting Change in Data Streams	2004	VLDB	7.1049861e-05
3,978	Efficient Deep Learning Pipelines for Accurate Cost Estimations Over Large Scale Query Workload	2021	SIGMOD	6.8807882e-05
4,538	One Model to Rule them All: Towards Zero-Shot Learning for Databases	2022	CIDR	6.553705e-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
6,416	A Unified Transferable Model for ML-Enhanced DBMS	2022	CIDR	5.7920805e-05

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