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PilotScope: Steering Databases with Machine Learning Drivers

Summary: PilotScope is middleware for deploying AI4DB algorithms, separating ML-centric AI4DB drivers from engine-specific database interactors. This abstraction lowers integration cost and enables portable, cross-database benchmarking of tuning and optimization techniques. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
h55157da4d1966a28
Venue
VLDB
Year
2024
Pagerank
6.028998e-05
Overall Rank
5,700 | 61.70%
DOI
10.14778/3641204.3641209
PDF
Download (CC BY-NC-ND 4.0)

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{zhu_vldb24,
        title = {{PilotScope: Steering Databases with Machine Learning Drivers}},
        author = {Zhu, Rong and Weng, Lianggui and Wei, Wenqing and Wu, Di and Peng, Jiazhen and Wang, Yifan and Ding, Bolin and Lian, Defu and Zheng, Bolong and Zhou, Jingren},
        journal = {PVLDB},
        series = {{VLDB} '24},
        volume = {17},
        number = {5},
        pages = {980--993},
        doi = {10.14778/3641204.3641209},
        url = {https://doi.org/10.14778/3641204.3641209},
        year = {2024}
}

Incoming Citations (Sorted by Pagerank)

Showing 12 of 12 citing papers.

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Outgoing Citations (Sorted by Pagerank)

Showing 50 of 51 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
15 How Good Are Query Optimizers, Really? 2016 VLDB 0.00061067652
30 SCOPE: Easy and Efficient Parallel Processing of Massive Data Sets 2008 VLDB 0.00050475202
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
98 LEO - DB2's LEarning Optimizer 2001 VLDB 0.00034099838
144 Neo: A Learned Query Optimizer 2019 VLDB 0.00029090793
149 Efficient Mid-Query Re-Optimization of Sub-Optimal Query Execution Plans 1998 SIGMOD 0.00028977821
151 An Efficient, Cost-Driven Index Selection Tool for Microsoft SQL Server 1997 VLDB 0.00028664776
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
422 ALEX: An Updatable Adaptive Learned Index 2020 SIGMOD 0.00018488849
471 Robust Query Processing through Progressive Optimization 2004 SIGMOD 0.00017744392
510 NeuroCard: One Cardinality Estimator for All Tables 2021 VLDB 0.00017059914
751 Automatic Physical Database Tuning: A Relaxation-based Approach 2005 SIGMOD 0.00014251362
768 FITing-Tree: A Data-aware Index Structure 2019 SIGMOD 0.00014107655
883 Dynamic Programming Strikes Back 2008 SIGMOD 0.00013263866
981 Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation 2022 VLDB 0.00012713454
1,065 Are We Ready For Learned Cardinality Estimation? 2021 VLDB 0.00012202293
1,195 Balsa: Learning a Query Optimizer Without Expert Demonstrations 2022 SIGMOD 0.00011574218
1,258 Sampling-Based Query Re-Optimization 2016 SIGMOD 0.00011308863
1,280 AI Meets AI: Leveraging Query Executions to Improve Index Recommendations 2019 SIGMOD 0.00011224914
1,396 Magic mirror in my hand, which is the best in the land? An Experimental Evaluation of Index Selection Algorithms 2020 VLDB 0.00010788714
1,605 SkinnerDB: Regret-Bounded Query Evaluation via Reinforcement Learning 2019 SIGMOD 0.00010095581
1,815 CoPhy: A Scalable, Portable, and Interactive Index Advisor for Large Workloads 2011 VLDB 9.5756946e-05
1,849 Diagnosing Root Causes of Intermittent Slow Queries in Cloud Databases 2020 VLDB 9.5032324e-05
2,002 FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation 2021 VLDB 9.2076835e-05
2,209 Lero: A Learning-to-Rank Query Optimizer 2023 VLDB 8.8360101e-05
2,518 Learned Cardinality Estimation: A Design Space Exploration and A Comparative Evaluation 2022 VLDB 8.3532841e-05
2,686 Cost-based or Learning-based? A Hybrid Query Optimizer for Query Plan Selection 2022 VLDB 8.1300913e-05
2,770 Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation 2022 VLDB 8.0343719e-05
2,833 Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings 2020 SIGMOD 7.9539771e-05
2,837 Query Performance Prediction for Concurrent Queries using Graph Embedding 2020 VLDB 7.9495917e-05
2,908 AI Meets Database: AI4DB and DB4AI 2021 SIGMOD 7.8716173e-05
3,052 LlamaTune: Sample-Efficient DBMS Configuration Tuning 2022 VLDB 7.704739e-05
3,337 The Composable Data Management System Manifesto 2023 VLDB 7.4077425e-05
3,544 Computation Reuse in Analytics Job Service at Microsoft 2018 SIGMOD 7.2108612e-05
3,588 Make Your Database System Dream of Electric Sheep: Towards Self-Driving Operation 2021 VLDB 7.1841858e-05
3,680 openGauss: An Autonomous Database System 2021 VLDB 7.1016555e-05
3,948 Deploying a Steered Query Optimizer in Production at Microsoft 2022 SIGMOD 6.9051584e-05
3,964 MB2: Decomposed Behavior Modeling for Self-Driving Database Management Systems 2021 SIGMOD 6.889374e-05
3,979 Efficient Deep Learning Pipelines for Accurate Cost Estimations Over Large Scale Query Workload 2021 SIGMOD 6.8791817e-05
4,240 LEON: A New Framework for ML-Aided Query Optimization 2023 VLDB 6.7064546e-05
4,677 AutoSteer: Learned Query Optimization for Any SQL Database 2023 VLDB 6.4721041e-05
5,110 Steering Query Optimizers: A Practical Take on Big Data Workloads 2021 SIGMOD 6.2678118e-05
5,438 Eraser: Eliminating Performance Regression on Learned Query Optimizer 2024 VLDB 6.1278045e-05
6,802 ROX: Run-time Optimization of XQueries 2009 SIGMOD 5.6773357e-05
7,426 Learning to be a Statistician: Learned Estimator for Number of Distinct Values 2022 VLDB 5.5295692e-05
7,915 Tastes Great! Less Filling! High Performance and Accurate Training Data Collection for Self-Driving Database Management Systems 2022 SIGMOD 5.4258674e-05
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