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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
6598
Venue
SIGMOD
Year
2023
Pagerank
5.9660278e-05
Overall Rank
6,132 | 57.93%
DOI
10.1145/3588713

Incoming Non-self Citations Over Time

Authors

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 10 of 10 citing papers.

Rank Citing Paper Year Venue Pagerank
5,573 Eraser: Eliminating Performance Regression on Learned Query Optimizer 2024 VLDB 6.1682747e-05
6,323 Modeling Shifting Workloads for Learned Database Systems 2024 SIGMOD 5.9141228e-05
6,997 E2ETune: End-to-End Knob Tuning via Fine-tuned Generative Language Model 2025 VLDB 5.7300324e-05
8,221 NeurDB: On the Design and Implementation of an AI-powered Autonomous Database 2025 CIDR 5.4640314e-05
8,643 PACE: Poisoning Attacks on Learned Cardinality Estimation 2024 SIGMOD 5.3940849e-05
9,587 Machine Unlearning in Learned Databases: An Experimental Analysis 2024 SIGMOD 5.2525104e-05
9,599 Understanding Robustness Issues of Updatable Learned Indexes: [Experiments & Analysis] 2026 SIGMOD 5.2492748e-05
10,272 NeurBench: A Benchmark Suite for Learned Database Components with Drift Modeling: [Experiments & Analysis] 2026 SIGMOD 5.093636e-05
10,327 Rainbow: Risk-aware Index Benefit Estimation Facing Out Of Distribution Workloads 2026 SIGMOD 5.093636e-05
10,586 TATA: An Efficient Framework for Task Transfer in Query Plan Representation 2026 VLDB 5.093636e-05
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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
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
378 Bao: Making Learned Query Optimization Practical 2021 SIGMOD 0.00019638121
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
802 Random Sampling over Joins Revisited 2018 SIGMOD 0.00013907725
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
1,988 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
2,313 Active Learning for ML Enhanced Database Systems 2020 SIGMOD 8.762627e-05
2,822 Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings 2020 SIGMOD 8.0898536e-05
3,785 Detecting Change in Data Streams 2004 VLDB 7.1265312e-05
3,953 Efficient Deep Learning Pipelines for Accurate Cost Estimations Over Large Scale Query Workload 2021 SIGMOD 6.996368e-05
4,468 One Model to Rule them All: Towards Zero-Shot Learning for Databases 2022 CIDR 6.6819041e-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
6,357 A Unified Transferable Model for ML-Enhanced DBMS 2022 CIDR 5.9020843e-05
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