【English | 中文】
根据我们过去在三大数据库顶会的tutorial，持续更新自治数据库工作。
如果我们漏掉了任何重要的论文，请告诉我们。谢谢！
会议截止日期：https://ccfddl.github.io/
（请注意 会议延期 可能不会及时同步，所以请只将其视为一个参考 :）
- 0. 综述和教程 (16)
- 1. 数据库自配置
- 2. 查询优化
- 3. 负载调度 (2)
- 4. 数据库自设计
- 5. 数据库自监控 (9)
- 6. 数据库自诊断
- 7. 一般技术
- 8. 自治数据库框架 (16)
- 9. 演示系统 (11)
- S1. 大模型x数据库 (10)
- S2. AI相关资料 (4)
- S3. 开源数据集和查询语句 (3)
不容错过的精彩演讲 >>
Make Your Database System Dream of Electric Sheep : Towards Self-Driving Operation. Andy Pavlo, Matthew Butrovich, Lin Ma, et al. [link]
Towards instance-optimized data systems. Tim Kraska. [link]
AI-Native Database. Guoliang Li. [link]
From BERT to GPT-3 Codex: Harnessing the Potential of Very Large Language Models for Data Management. Immanuel Trummer. [link]
Retrieval-based Language Models and Applications. Akari Asai, Sewon Min, Zexuan Zhong, Danqi Chen. [link]
Database meets deep learning: Challenges and opportunities.
Wei Wang, Meihui Zhang, Gang Chen, et al. SIGMOD Record, 2016. [paper]
Database Meets Artificial Intelligence: A Survey. 
Xuanhe Zhou, Chengliang Chai, Guoliang Li, et al. TKDE, 2020. [paper]
A Survey on Advancing the DBMS Query Optimizer: Cardinality Estimation, Cost Model, and Plan Enumeration.
Hai Lan, Zhifeng Bao, Yuwei Peng. Data Science and Engineering, 2021. [paper]
A Survey on Deep Reinforcement Learning for Data Processing and Analytics.
Qingpeng Cai, Can Cui, Yiyuan Xiong, et al. TKDE, 2022. [paper]
Self-Driving Database Papers (CMU Spring Course). 2022.
https://15799.courses.cs.cmu.edu/spring2022/schedule.html
Automatic Database Knob Tuning: A Survey.
Xinyang Zhao, Xuanhe Zhou, Guoliang Li. TKDE, 2023. [paper] [code]
From auto-tuning one size fits all to self-designed and learned data-intensive systems.
Stratos Idreos, Tim Kraska. SIGMOD, 2019. [paper]
Speedup Your Analytics: Automatic Parameter Tuning for Databases and Big Data Systems.
Jiaheng Lu, Yuxing Chen, Herodotos Herodotou, Shivnath Babu. VLDB, 2019. [paper] [slides]
Tutorial: Adaptive Replication and Partitioning in Data Systems.
Brad Glasbergen, Michael Abebe, Khuzaima Daudjee. Middleware, 2018. [paper]
A Tutorial on Learned Multi-dimensional Indexes.
Abdullah Al-Mamun, Hao Wu, Walid G. Aref. SIGSPATIAL, 2020. [paper]
AI Meets Database: AI4DB and DB4AI.
Guoliang Li, Xuanhe Zhou, Lei Cao. SIGMOD, 2021. [paper] [slides]
Machine Learning for Databases.
Guoliang Li, Xuanhe Zhou, Lei Cao. VLDB, 2021. [paper][slides]
Machine Learning for Cloud Data Systems: the Promise, the Progress, and the Path Forward.
Alekh Jindal, Matteo Interlandi. VLDB, 2021. [paper]
Workload-Aware Performance Tuning for Autonomous DBMSs.
Zhengtong Yan, Jiaheng Lu, Naresh Chainani, et al. ICDE, 2021. [paper]
Learned Query Optimizer: At the Forefront of AI-Driven Databases.
Zhu, Rong, Ziniu Wu, Chengliang Chai, et al. EDBT, 2022. [paper]
From BERT to GPT-3 Codex: Harnessing the Potential of Very Large Language Models for Data Management.
Immanuel Trummer. VLDB, 2022. [paper]
PGTune: https://pgtune.leopard.in.ua.
OpenTuner: An Extensible Framework for Program Autotuning
Ansel J, Kamil S, Veeramachaneni K, et al. PACT, 2014. [paper]
BestConfig: Tapping the Performance Potential of Systems via Automatic Configuration Tuning
Zhu Y, Liu J, Guo M, et al. SoCC, 2017. [paper]
Tuning Database Configuration Parameters with iTuned
Duan, S., Thummala, V., & Babu, S. VLDB, 2009. [paper]
Automatic database management system tuning through large-scale machine learning
Van Aken D, Pavlo A, Gordon G J, et al. SIGMOD, 2017. [paper]
Black or White? How to Develop an AutoTuner for Memory-based Analytics 
Kunjir M, Babu S. SIGMOD, 2020. [paper]
ResTune: Resource Oriented Tuning Boosted by Meta-Learning for Cloud Databases
Zhang X, Wu H, Chang Z, et al. SIGMOD, 2021. [paper]
CGPTuner: a Contextual Gaussian Process Bandit Approach for the Automatic Tuning of IT Configurations Under Varying Workload Conditions
Cereda S, Valladares S, Cremonesi P, et al. VLDB, 2021. [paper]
Towards Dynamic and Safe Configuration Tuning for Cloud Databases
Zhang X, Wu H, Li Y, et al. SIGMOD, 2022. [paper]
LlamaTune: Sample-Efficient DBMS Configuration Tuning
Kanellis K, Ding C, Kroth B, et al. VLDB, 2022. [paper]
iBTune: Individualized Buffer Tuning for Large-scale Cloud Databases
Jian Tan, Tieying Zhang, Feifei Li, et al. VLDB, 2019. [paper]
An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning
Ji Zhang, Yu Liu, Ke Zhou, Guoliang Li, et al. SIGMOD, 2019. [paper]
QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning
Li G, Zhou X, Li S, et al. VLDB, 2019. [paper]
Watuning: A workload-aware tuning system with attention-based deep reinforcement learning
Ge J K, Chai Y F, Chai Y P. JCST, 2021. [paper]
The Case for NLP-Enhanced Database Tuning: Towards Tuning Tools that "Read the Manual"
Trummer I. VLDB, 2021. [paper]
DB-BERT: a Database Tuning Tool that “Reads the Manual”
Trummer I. SIGMOD, 2022. [paper]
HUNTER- An Online Cloud Database Hybrid Tuning System for Personalized Requirements
Cai B, Liu Y, Zhang C, et al. SIGMOD, 2022. [paper]
SARD: A statistical approach for ranking database tuning parameters
Debnath B K, Lilja D J, Mokbel M F. ICDE Workshops 2008. [paper]
Too Many Knobs to Tune? Towards Faster Database Tuning by Pre-selecting Important Knobs
Kanellis K, Alagappan R, Venkataraman S. HotStorage 2020. [paper]
IWEK: An Interpretable What-If Estimator for Database Knobs 
Yu Yan, Hongzhi Wang, Jian Geng, et al. arXiv 2023. [paper]
An inquiry into machine learning-based automatic configuration tuning services on real-world database management systems
Van Aken D, Yang D, Brillard S, et al. VLDB, 2021. [paper]
Facilitating Database Tuning with Hyper-Parameter Optimization- A Comprehensive Experimental Evaluation
Zhang X, Chang Z, Li Y, et al. VLDB, 2022. [paper]
Selecting subexpressions to materialize at datacenter scale
A. Jindal, K. Karanasos, S. Rao, and H. Patel. PVLDB, 11(7):800–812, 2018. [paper]
Automated generation of materialized views in Oracle
Ahmed, R., Bello, R., Witkowski, A., & Kumar, P. (2020). VLDB, 2020. [paper]
Computation reuse in analytics job service at microsoft
Jindal, A., Qiao, S., Patel, H., Yin, Z., Di, J., Bag, M., Friedman, M., Lin, Y., Karanasos, K. and Rao, S., SIGMOD, 2018 (pp. 191-203). [paper]
Automatic View Generation for Equivalent Subqueries with Deep Learning and Reinforcement Learning
Yuan, H., Sun, J., & Li, G. (2020). ICDE, 2020. [paper]
An Autonomous Materialized View Management System with Deep Reinforcement Learning
Han, Y., Li, G., Yuan, H., & Sun, J. ICDE, 2021. [paper]
AutoView: An Autonomous Materialized View Management System with Encoder-Reducer
Han, Y., Li, G., Yuan, H. and Sun, J., TKDE, 2022. [paper]
Dynamic Materialized View Management using Graph Neural Network
Yue Han, Chengliang Chai, Jiabin Liu, Guoliang Li, Chuangxian Wei, Chaoqun Zhan. ICDE 2023. [paper]
A novel coral reefs optimization algorithm for materialized view selection in data warehouse environments
Azgomi, H. and Sohrabi, M.K., Applied Intelligence, 2019, 49, pp.3965-3989. [paper]
[EA & B] Jan Kossmann, Stefan Halfpap, Marcel Jankrift, Rainer Schlosser: Magic mirror in my hand, which is the best in the land? An Experimental Evaluation of Index Selection Algorithms. Proc. VLDB Endow. 13(11): 2382-2395 (2020) [paper]
[Industry, Microsoft Azure] Sudipto Das, Miroslav Grbic, Igor Ilic, Isidora Jovandic, Andrija Jovanovic, Vivek R. Narasayya, Miodrag Radulovic, Maja Stikic, Gaoxiang Xu, Surajit Chaudhuri: Automatically Indexing Millions of Databases in Microsoft Azure SQL Database. SIGMOD Conference 2019: 666-679 [paper]
[Industry, Meta] Ritwik Yadav, Satyanarayana R. Valluri, Mohamed Zait: AIM: A practical approach to automated index management for SQL databases. ICDE 2023 [paper]
[Heuristic-based, AutoAdmin] Surajit Chaudhuri, Vivek R. Narasayya: An Efficient Cost-Driven Index Selection Tool for Microsoft SQL Server. VLDB 1997: 146-155 [paper]
[Heuristic-based, DB2Advis] Gary Valentin, Michael Zuliani, Daniel C. Zilio, Guy M. Lohman, Alan Skelley: DB2 Advisor: An Optimizer Smart Enough to Recommend Its Own Indexes. ICDE 2000: 101-110 [paper]
[Heuristic-based, Relaxation] Nicolas Bruno, Surajit Chaudhuri: Automatic Physical Database Tuning: A Relaxation-based Approach. SIGMOD Conference 2005: 227-238 [paper]
[Heuristic-based, COLT] Karl Schnaitter, Serge Abiteboul, Tova Milo, Neoklis Polyzotis: On-Line Index Selection for Shifting Workloads. ICDE Workshops 2007: 459-468 [paper]
[Heuristic-based, Extend] Rainer Schlosser, Jan Kossmann, Martin Boissier: Efficient Scalable Multi-attribute Index Selection Using Recursive Strategies. ICDE 2019: 1238-1249 [paper]
[Learning-based, DQN] Hai Lan, Zhifeng Bao, Yuwei Peng: An Index Advisor Using Deep Reinforcement Learning. CIKM 2020: 2105-2108 [paper]
[Learning-based, DQN] Zahra Sadri, Le Gruenwald, Eleazar Leal: Online Index Selection Using Deep Reinforcement Learning for a Cluster Database. ICDE Workshops 2020: 158-161 [paper]
[Learning-based, DQN] Gabriel Paludo Licks, Júlia Mara Colleoni Couto, Priscilla de Fátima Miehe, Renata De Paris, Duncan Dubugras A. Ruiz, Felipe Meneguzzi: SmartIX: A Database Indexing Agent based on Reinforcement Learning. Appl. Intell. 50(8): 2575-2588 (2020) [paper]
[Learning-based, DQN] Vishal Sharma, Curtis E. Dyreson, Nicholas Flann: MANTIS: Multiple Type and Attribute Index Selection using Deep Reinforcement Learning. IDEAS 2021: 56-64 [paper]
[Learning-based, DQN] Yu Yan, Shun Yao, Hongzhi Wang, Meng Gao: Index selection for NoSQL database with deep reinforcement learning. Inf. Sci. 561: 20-30 (2021) [paper]
[Learning-based, DQN] Vishal Sharma, Curtis E. Dyreson: Indexer++: Workload-aware Online Index Tuning with Transformers and Reinforcement Learning. SAC 2022: 372-380 [paper]
[Learning-based, MAB] R. Malinga Perera, Bastian Oetomo, Benjamin I. P. Rubinstein, Renata Borovica-Gajic: DBA bandits: Self-driving index tuning under ad-hoc, analytical workloads with safety guarantees. ICDE 2021: 600-611 [paper]
[Learning-based, MAB] R. Malinga Perera, Bastian Oetomo, Benjamin I. P. Rubinstein, Renata Borovica-Gajic: HMAB: Self-Driving Hierarchy of Bandits for Integrated Physical Database Design Tuning. Proc. VLDB Endow. 16(2): 216-229 (2022) [paper]
[Learning-based, MCTS] Xuanhe Zhou, Luyang Liu, Wenbo Li, Lianyuan Jin, Shifu Li, Tianqing Wang, Jianhua Feng: AutoIndex: An Incremental Index Management System for Dynamic Workloads. ICDE 2022: 2196-2208 [paper]
[Learning-based, MCTS] Wentao Wu, Chi Wang, Tarique Siddiqui, Junxiong Wang, Vivek R. Narasayya, Surajit Chaudhuri, Philip A. Bernstein: Budget-aware Index Tuning with Reinforcement Learning. SIGMOD Conference 2022: 1528-1541 [paper]
[Optimization, Learned Cost] Bailu Ding, Sudipto Das, Ryan Marcus, Wentao Wu, Surajit Chaudhuri, Vivek R. Narasayya: AI Meets AI: Leveraging Query Executions to Improve Index Recommendations. SIGMOD Conference 2019: 1241-1258 [paper]
[Optimization, Learned Cost] Jiachen Shi, Gao Cong, Xiaoli Li: Learned Index Benefits: Machine Learning Based Index Performance Estimation. Proc. VLDB Endow. 15(13): 3950-3962 (2022) [paper]
[Optimization, Learned Cost] Jianling Gao, Nan Zhao, Ning Wang, Shuang Hao: SmartIndex: An Index Advisor with Learned Cost Estimator. CIKM 2022: 4853-4856 [paper]
[Optimization, Workload Summarization] Tarique Siddiqui, Saehan Jo, Wentao Wu, Chi Wang, Vivek R. Narasayya, Surajit Chaudhuri: ISUM: Efficiently Compressing Large and Complex Workloads for Scalable Index Tuning. SIGMOD Conference 2022: 660-673 [paper]
[Optimization, What-if Call] Tarique Siddiqui, Wentao Wu, Vivek R. Narasayya, Surajit Chaudhuri: DISTILL: Low-Overhead Data-Driven Techniques for Filtering and Costing Indexes for Scalable Index Tuning. Proc. VLDB Endow. 15(10): 2019-2031 (2022) [paper]
Automating physical database design in a parallel database.
Jun Rao, Chun Zhang, Nimrod Megiddo, Guy M. Lohman. SIGMOD, 2002. [paper]
Schism: a Workload-Driven Approach to Database Replication and Partitioning.
Carlo Curino, Yang Zhang, Evan P. C. Jones, Samuel Madden. PVLDB, 2010. [paper]
Locality-aware partitioning in parallel database systems.
Erfan Zamanian, Carsten Binnig, Abdallah Salama. SIGMOD, 2015. [paper]
Query centric partitioning and allocation for partially replicated database systems.
Tilmann Rabl, Hans-Arno Jacobsen. SIGMOD, 2017. [paper]
Workload-driven horizontal partitioning and pruning for large HTAP systems. 
Martin Boissier, Kurzynski Daniel. ICDE Workshop, 2018. [paper]
Towards learning a partitioning advisor with deep reinforcement learning.
Benjamin Hilprecht, Carsten Binnig, Uwe Röhm. aiDM@SIGMOD, 2019. [paper]
Automated vertical partitioning with deep reinforcement learning.
Campero Durand G, Piriyev R, Pinnecke M, et al. ADBIS, 2019. [paper]
Fast and effective distribution-key recommendation for amazon redshift.
Panos Parchas, Yonatan Naamad, Peter Van Bouwel, et al. PVLDB, 2020. [paper]
Adaptive partitioning and indexing for in situ query processing.
Olma, M., Karpathiotakis, M., Alagiannis, I., Athanassoulis, et al. VLDB Journal. [paper]
Learning a Partitioning Advisor for Cloud Databases.
Benjamin Hilprecht, Carsten Binnig, Uwe Röhm. SIGMOD, 2020. [paper]
Grep: A Graph Learning Based Database Partitioning System.
Xuanhe Zhou, Guoliang Li, Jianhua Feng, et al. SIGMOD, 2023. [paper] [demo]
Universal Database Optimization using Reinforcement Learning
Wang J, Trummer I, Basu D. VLDB, 2021. [paper]
A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning
Xinyi Zhang, Zhuo Chang, HONG WU, et al. SIGMOD, 2023. [paper]
（请注意，其他有趣的问题，比如text2SQL，不在范围之内）
[Rewrite Rules] Béatrice Finance, Georges Gardarin. A Rule-Based Query Rewriter in an Extensible DBMS. ICDE 1991. [paper]
[Rewrite Rules] Hamid Pirahesh, Joseph M. Hellerstein, Waqar Hasan. Extensible/Rule Based Query Rewrite Optimization in Starburst. SIGMOD Conference 1992. [paper]
[Cost/Heuristic Rewrite] Rafi Ahmed, Allison W. Lee, Andrew Witkowski, et al. Cost-Based Query Transformation in Oracle. VLDB 2006: 1026-1036. [paper]
[Heuristic Rewrite] De Araújo, A. H. M., Monteiro, J. M., Antônio, J., De Macêdo, F., Tavares, J. A., Brayner, A., & Lifschitz, S. (2014). ARe-SQL: An Online, Automatic and Non-Intrusive Approach for Rewriting SQL Queries. JIDM, 2014. [paper]
[Semantic Equivalence] Shumo Chu, Konstantin Weitz, Alvin Cheung, Dan Suciu. HoTTSQL: proving query rewrites with univalent SQL semantics. PLDI 2017: 510-524. [paper]
[Optimization Engine] Begoli, E., Camacho-Rodríguez, J., Hyde, J., Mior, M. J., & Lemire, D. (2018). Apache calcite: A foundational framework for optimized query processing over heterogeneous data sources. SIGMOD, 2018. [paper]
[Map-Reduce Rewrite] Partho Sarthi, Kaushik Rajan, Akash Lal, Abhishek Modi, et al. Generalized Sub-Query Fusion for Eliminating Redundant I/O from Big-Data Queries. OSDI 2020: 209-224. [paper]
[Streaming] Wentao Wu, Philip A. Bernstein, Alex Raizman, Christina Pavlopoulou. Cost-based Query Rewriting Techniques for Optimizing Aggregates Over Correlated Windows. CoRR abs/2008.12379 (2020) [paper]
[Rewrite Rules] Zhaoguo Wang, Zhou Zhou, Yicun Yang, Haoran Ding, Gansen Hu, Ding Ding, Chuzhe Tang, Haibo Chen, Jinyang Li. WeTune: Automatic Discovery and Verification of Query Rewrite Rules. SIGMOD Conference 2022: 94-107. [paper]
[Rewrite Rules] Qiushi Bai, Sadeem Alsudais, Chen Li. QueryBooster: Improving SQL Performance Using Middleware Services for Human-Centered Query Rewriting. VLDB, 2023. [paper]
[Predicate Rewrite] Qi Zhou, Joy Arulraj, Shamkant B. Navathe, William Harris, Jinpeng Wu. Sia : Optimizing Queries using Learned Predicates. SIGMOD, 2021. [paper]
[Rewrite Strategy] Xuanhe Zhou, Guoliang Li, Chengliang Chai, Jianhua Feng. A Learned Query Rewrite System using Monte Carlo Tree Search. VLDB, 2022. [paper]
[Card, Query-based] Dutt, A., Wang, C., Nazi, A., Kandula, S., Narasayya, V., & Chaudhuri, S. (2018). Selectivity estimation for range predicates using lightweight models. Proceedings of the VLDB Endowment, 12(9), 1044–1057, 2018. [paper]
[Card, Query-based] Kipf A, Kipf T, Radke B, et al. Learned cardinalities: Estimating correlated joins with deep learning. CIDR, 2019. [paper]
[Card, Query-based] Woltmann L, Hartmann C, Thiele M, et al. Cardinality estimation with local deep learning models. aiDM, 2019. [paper]
[Card, Query-based] Hayek, R., & Shmueli, O. (2020). NN-based Transformation of Any SQL Cardinality Estimator for Handling DISTINCT, AND, OR and NOT. arXiv， 2020. [paper]
[Card, Query-based] Tzoumas K, Deshpande A, Jensen C S. Lightweight graphical models for selectivity estimation without independence assumptions[J]. Proceedings of the VLDB Endowment, 4(11): 852-863, 2011. [paper]
[Card, Query-based] Xiao Hu, Yuxi Liu, Haibo Xiu, Pankaj K. Agarwal, Debmalya Panigrahi, Sudeepa Roy, Jun Yang. Selectivity Functions of Range Queries are Learnable. SIGMOD, 2022. [paper]
[Card, Query-based, Adaptability] Beibin Li, Yao Lu, Srikanth Kandula: Warper: Efficiently Adapting Learned Cardinality Estimators to Data and Workload Drifts. SIGMOD Conference 2022: 1920-1933 [paper]
[Card, Query-based, Robust Encoding & Training] Negi, Parimarjan, Ziniu Wu, Andreas Kipf, Nesime Tatbul, Ryan Marcus, Sam Madden, Tim Kraska, and Mohammad Alizadeh: Robust Query Driven Cardinality Estimation under Changing Workloads. VLDB, 2023. [paper]
[Card, Data-based] Leis, V., Radke, B., Gubichev, A., Kemper, A., & Neumann, T. (2017). Cardinality estimation done right: Index-based join sampling. CIDR, 2017. [paper]
[Card, Data-based] Yang, Z., Liang, E., Kamsetty, A., Wu, C., Duan, Y., Chen, X., … Stoica, I. (2019). Deep Unsupervised Cardinality Estimation. VLDB, 2019. [paper]
[Card, Data-based] Yang, Z., Kamsetty, A., Luan, S., Liang, E., Duan, Y., Chen, X., & Stoica, I. (2020). Neurocard: One cardinality estimator for all tables. Proceedings of the VLDB Endowment, 14(1), 61–73, 2020. [paper]
[Card, Data-based] Zhu R, Wu Z, Han Y, et al. FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation[J]. arXiv preprint arXiv:2011.09022, 2020. [paper]
[Card, Data-based] Wu Z, Shaikhha A, Zhu R, et al. BayesCard: Revitilizing Bayesian Frameworks for Cardinality Estimation. arXiv preprint arXiv: 2012.14743, 2020. [paper]
[Card, Data-based] Hilprecht, B., Schmidt, A., Kulessa, M., Molina, A., Kersting, K., & Binnig, C. (2020). DeepDB: Learn from data, not from queries! VLDB, 13(7), 992–1005, 2020. [paper]
[Card, Data-based] Yongjoo Park, Shucheng Zhong, and Barzan Mozafari. Quicksel: Quick selectivity learning with mixture models. SIGMOD 2020. [paper]
[Card, Data-based] Lu Y, Kandula S, König A C, et al. Pre-training summarization models of structured datasets for cardinality estimation[J]. Proceedings of the VLDB Endowment, 2021. [paper]
[Card, Data-based] Zhu, R., Wu, Z., Han, Y., Zeng, K., Pfadler, A., Qian, Z., … Cui, B. (2020). FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation. VLDB, 2021. [paper]
[Card, Data-based] Jiayi Wang, Chengliang Chai, Jiabin Liu, Guoliang Li. FACE: A Normalizing Flow based Cardinality Estimator. VLDB 2022. [paper]
[Card, Data-based] Yao Lu, Srikanth Kandula, Arnd Christian König, Surajit Chaudhuri. Pre-training summarization models of structured datasets for cardinality estimation. VLDB 2022. [paper]
[Card, Query&Data-based] Wu P, Cong G. A Unified Deep Model of Learning from both Data and Queries for Cardinality Estimation. SIGMOD. 2021: 2009-2022. [paper]
[Card] Parimarjan Negi, Ryan C. Marcus, Andreas Kipf, Hongzi Mao, Nesime Tatbul, Tim Kraska, Mohammad Alizadeh. Flow-Loss: Learning Cardinality Estimates That Matter. VLDB Endow, 14(11): 2019-2032, 2021. [paper]
[Card, Model Selection] Jintao Zhang, Chao Zhang, Guoliang Li, Chengliang Chai. AutoCE: An Accurate and Efficient Model Advisor for Learned Cardinality Estimation. ICDE, 2023. [paper]
[Card] Xiaoye Miao, Yangyang Wu, Jiazhen Peng, et al. Efficient and Effective Cardinality Estimation for Skyline Family. SIGMOD, 2023. [paper]
[Card] Ziniu Wu, Parimarjan Negi, Mohammad Alizadeh, Tim Kraska, Samuel Madden. FactorJoin: A New Cardinality Estimation Framework for Join Queries. SIGMOD, 2023. [paper]
[Card, Query-based] Fang Wang, Xiao Yan, Man Lung Yiu, Shuai Li, Zunyao Mao, and Bo Tang. Speeding Up End-to-end Query Execution via Learning-based Progressive Cardinality Estimation. SIGMOD, 2023. [paper]
[ EA&B ] Wang, X., Qu, C., Wu, W., Wang, J., & Zhou, Q. (2021). Are We Ready For Learned Cardinality Estimation? Proc. VLDB Endow. 14(9): 1640-1654 (2021). [paper]
[ EA&B ] Sun, J., Zhang, J., Sun, Z., Li, G., & Tang, N. (n.d.). Learned Cardinality Estimation : A Design Space Exploration and a Comparative Evaluation [ EA & B ]. 14(1). VLDB, 2022. [paper]
[ EA&B ] Yuxing Han, Ziniu Wu, Peizhi Wu, et al. Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation Yuxing. VLDB, 2022. [paper]
[ EA&B ] Kyoungmin Kim, Jisung Jung, In Seo, Wook-Shin Han, Kangwoo Choi, Jaehyok Chong: Learned Cardinality Estimation: An In-depth Study. SIGMOD Conference 2022: 1214-1227 [paper]
[ EA&B ] Harmouch, H., & Naumann, F. (2018). Cardinality Estimation: An Experimental Survey. Pvldb, 11(4), 4999–512, 2017. [paper]
[Cost] Marcus, R., & Papaemmanouil, O. (2019). Plan-Structured Deep Neural Network Models for Query Performance Prediction. 1733–1746. [paper]
[Cost] Sun, J., & Li, G. (n.d.). An End-to-End Learning-based Cost Estimator. VLDB, 2020. [paper]
[Cost] Benjamin Hilprecht, Carsten Binnig. Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction. VLDB, 2022. [paper]
Continuously Adaptive Query Processing
Ron Avnur, Joseph M. Hellerstein. Eddies. SIGMOD, 2000. [paper]
How Good Are Query Optimizers, Really?
Leis, V., Gubichev, A., Mirchev, A., Boncz, P., Kemper, A., & Neumann, T. Proceedings of the VLDB Endowment (2016), 9(3), 204–215. [paper]
Neo: A Learned query optimizer
Marcus, R., Negi, P., Mao, H., Zhang, C., Alizadeh, M., Kraska, T., … Tatbul, N. (2018). Proceedings of the VLDB Endowment, 12(11), 1705–1718, 2018. [paper]
Deep reinforcement learning for join order enumeration
Marcus, R., & Papaemmanouil, O. (2018). Proceedings of the 1st International Workshop on Exploiting Artificial Intelligence Techniques for Data Management, AiDM 2018, 0–3. [paper]
SkinnerDB : Regret-Bounded Query Evaluation via Reinforcement Learning
Trummer, I., Wang, J., Maram, D., Moseley, S., Jo, S., & Antonakakis, J. (n.d.). SIGMOD, 2019. [paper]
Progressive Join Algorithms Considering User Preference
Ding, M., Chen, S., & Manegold, S. (2021). CIDR, 2021. [paper]
Reinforcement Learning with Tree-LSTM for Join Order Selection
Yu, X., Li, G., Tang, N. (n.d.). ICDE, 2020. [paper]
Towards a Learning Optimizer for Shared Clouds
Chenggang Wu, Alekh Jindal, Saeed Amizadeh, Hiren Patel, Wangchao Le, Shi Qiao, Sriram Rao. Proc. VLDB Endow. 12(3): 210-222, 2018. [paper]
SQL Plan Observability through Hints in Oracle Autonomous Database
Pasupuleti, K., Park, M., & Valluri, S. (n.d.).
Bao: Making Learned Query Optimization Practical
Marcus, R., Negi, P., Mao, H., Tatbul, N., Alizadeh, M., & Kraska, T. (2020). SIGMOD, 2021. [paper]
Steering Query Optimizers: A Practical Take on Big Data Workloads
Parimarjan Negi, Matteo Interlandi, Ryan Marcus, Mohammad Alizadeh, Tim Kraska, Marc Friedman, Alekh Jindal. SIGMOD, 2021. [paper]
SkinnerMT: Parallelizing for Efficiency and Robustness in Adaptive Query Processing on Multicore Platforms
Ziyun Wei, Immanuel Trummer. PVLDB, 2022. [paper]
Learning a Query Optimizer Without Expert Demonstrations
Zongheng Yang, Wei-Lin Chiang, Sifei Luan, Gautam Mittal, Michael Luo, Ion Stoica. Balsa. SIGMOD, 2022 [paper]
Workload-driven, Lazy Discovery of Data Dependencies for Query Optimization
Jan Kossmann. CIDR, 2022 [paper]
LOGER: A Learned Optimizer towards Generating Efficient and Robust Query Execution Plans
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