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: 6536
Venue: SIGMOD
Year: 2023
Pagerank: 4.8971368e-05
Overall Rank: 6,879 | 52.15%
DOI: 10.1145/3588713

Incoming Non-self Citations Over Time

Authors

1. Meghdad Kurmanji
2. Peter Triantafillou

Incoming Citations (Sorted by Pagerank)

Showing 9 of 9 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
5,952	Eraser: Eliminating Performance Regression on Learned Query Optimizer	2024	VLDB	5.2591691e-05
7,828	Modeling Shifting Workloads for Learned Database Systems	2024	SIGMOD	4.6407986e-05
8,186	E2ETune: End-to-End Knob Tuning via Fine-tuned Generative Language Model	2025	VLDB	4.5651684e-05
8,688	NeurDB: On the Design and Implementation of an AI-powered Autonomous Database	2025	CIDR	4.4673127e-05
9,213	PACE: Poisoning Attacks on Learned Cardinality Estimation	2024	SIGMOD	4.3721075e-05
9,852	Machine Unlearning in Learned Databases: An Experimental Analysis	2024	SIGMOD	4.2714575e-05
10,032	Rainbow: Risk-aware Index Benefit Estimation Facing Out Of Distribution Workloads	2026	SIGMOD	4.1945683e-05
10,038	Understanding Robustness Issues of Updatable Learned Indexes: [Experiments & Analysis]	2026	SIGMOD	4.1945683e-05
10,288	TATA: An Efficient Framework for Task Transfer in Query Plan Representation	2026	VLDB	4.1945683e-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
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
640	Bao: Making Learned Query Optimization Practical	2021	SIGMOD	0.00018759152
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
1,369	Random Sampling over Joins Revisited	2018	SIGMOD	0.00012339777
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
3,142	Active Learning for ML Enhanced Database Systems	2020	SIGMOD	7.4815444e-05
3,625	Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings	2020	SIGMOD	6.9055212e-05
3,685	Detecting Change in Data Streams	2004	VLDB	6.8448674e-05
4,804	Efficient Deep Learning Pipelines for Accurate Cost Estimations Over Large Scale Query Workload	2021	SIGMOD	5.910467e-05
5,258	One Model to Rule them All: Towards Zero-Shot Learning for Databases	2022	CIDR	5.5998705e-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
6,775	A Unified Transferable Model for ML-Enhanced DBMS	2022	CIDR	4.9299192e-05

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3,142	Active Learning for ML Enhanced Database Systems	2020	SIGMOD	7.4815444e-05
4,417	Robust Query Driven Cardinality Estimation under Changing Workloads	2023	VLDB	6.2037371e-05
2,364	Deep Learning Models for Selectivity Estimation of Multi-Attribute Queries	2020	SIGMOD	8.9554751e-05
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608	DeepDB: Learn from Data, not from Queries!	2020	VLDB	0.00019235898
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