【English | 中文】
Continuously update the autonomous database works based on our past tutorials.
Kindly let us know if we have missed any great papers. Thank you!
Conference deadlines: https://github.com/ccfddl/ccf-deadlines
(Note conference postponement may not be promptly synchronized, so just consider it as a reference.)
- 0. Survey and Tutorial (16)
- 1. Database Configuration
- 2. Query Optimization
- 3. Workload Scheduling (2)
- 4. Database Design
- 5. Database Monitoring (9)
- 6. Database Diagnosis
- 7. General Techniques
- 8. Database Frameworks (16)
- 9. Demonstrations (13)
- S1. LLM x DB (32)
- S2. AI Resources (5)
- S3. Data And SQLs (3)
Great talks you should not miss >>
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]
BestConfig: Tapping the Performance Potential of Systems via Automatic Configuration Tuning
Zhu Y, Liu J, Guo M, et al. SoCC, 2017. [paper]
An Efficient Transfer Learning Based Configuration Adviser for Database Tuning 
Xinyi Zhang, Hong Wu, Yang Li, et al. VLDB, 2023. [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]
DBSP: Automatic Incremental View Maintenance for Rich Query Languages
Mihai Budiu, Tej Chajed, Frank McSherry, et al. VLDB, 2023. [paper]
Siddiqui, Tarique and Jo, Saehan and Wu, Wentao and Wang, Chi and Narasayya, Vivek and Chaudhuri, Surajit. SIGMOD, 2022 [paper]
Chaudhuri, Surajit and Narasayya, Vivek and Ganesan, Prasanna. VLDB, 2023 [paper]
Chaudhuri, Surajit and Gupta, Ashish Kumar and Narasayya, Vivek. SIGMOD, 2022 [paper]
Deep, Shaleen and Gruenheid, Anja and Koutris, Paraschos and Naughton, Jeffrey and Viglas, Stratis. VLDB, 2020 [paper]
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Suboti{'c}, Pavle and Jordan, Herbert and Chang, Lijun and Fekete, Alan and Scholz, Bernhard. VLDB, 2018 [paper]
Yadav, Ritwik and Valluri, Satyanarayana R. and Zaït, Mohamed. ICDE, 2023 [paper]
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Basu, Debabrota and Lin, Qian and Chen, Weidong and Vo, Hoang Tam and Yuan, Zihong and Senellart, Pierre and Bressan, St{'e}phane. Database and Expert Systems Applications, 2015 [paper] [code]
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Lan, Hai and Bao, Zhifeng and Peng, Yuwei. CIKM, 2020 [paper] [code]
Paludo Licks, Gabriel and Colleoni Couto, Julia and de F{'a}tima Miehe, Priscilla and De Paris, Renata and Dubugras Ruiz, Duncan and Meneguzzi, Felipe. Applied Intelligence, 2020 [paper] [code]
Sadri, Zahra and Gruenwald, Le and Leal, Eleazar. ICDEW, 2020 [paper] [code]
Lai, Sichao and Wu, Xiaoying and Wang, Senyang and Peng, Yuwei and Peng, Zhiyong. APWeb and WAIM, 2021 [paper]
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Kossmann, Jan and Kastius, Alexander and Schlosser, Rainer. EDBT, 2022 [paper] [code]
Wu, Wentao and Wang, Chi and Siddiqui, Tarique and Wang, Junxiong and Narasayya, Vivek and Chaudhuri, Surajit and Bernstein, Philip A. SIGMOD, 2022 [paper]
Schnaitter, Karl and Polyzotis, Neoklis. ICDE, 2009 [paper]
K. Sattler, I. Geist, and E. Schallehn. VLDB, 2003 [paper]
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Zhou, Xuanhe and Liu, Luyang and Li, Wenbo and Jin, Lianyuan and Li, Shifu and Wang, Tianqing and Feng, Jianhua. ICDE, 2022 [paper] [code]
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Lum, Vincent Y and Ling, Huei. Proceedings of the 1971 26th annual conference [paper]
Mario Schkolnick. SIGMOD, 1975 [paper]
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Mario Schkolnick. Information Systems, 1975 [paper]
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Siddiqui, Tarique and Wu, Wentao and Narasayya, Vivek and Chaudhuri, Surajit. VDLB, 2022 [paper]
Shi, Jiachen and Cong, Gao and Li, Xiao-Li. VLDB, 2022 [paper] [code]
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Self-Selecting, Self-Tuning, Incrementally Optimized Indexes
Graefe, Goetz and Kuno, Harumi. EDBT, 2010 [paper]
Concurrency control for adaptive indexing
Graefe, Goetz and Halim, Felix and Idreos, Stratos and Kuno, Harumi and Manegold, Stefan. VLDB, 2012 [paper]
Database Cracking
Idreos, Stratos and Kersten, Martin L and Manegold, Stefan and others. CIDR, 2007 [paper]
Merging what's cracked, cracking what's merged: adaptive indexing in main-memory column-stores
Idreos, Stratos and Manegold, Stefan and Kuno, Harumi and Graefe, Goetz. VLDB, 2011 [paper]
Stochastic Database Cracking: Towards Robust Adaptive Indexing in Main-Memory Column-Stores
Halim, Felix and Idreos, Stratos and Karras, Panagiotis and Yap, Roland H. C.. VLDB, 2012 [paper]
Holistic Indexing in Main-Memory Column-Stores
Petraki, Eleni and Idreos, Stratos and Manegold, Stefan. SIGMOD, 2015 [paper]
Predictive indexing
Arulraj, Joy and Xian, Ran and Ma, Lin and Pavlo, Andrew. arXiv, 2019 [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]
(note other interesting problems like text2SQL are not within the scope)
[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]
[Program Synthesis] Rui Dong, Jie Liu, Yuxuan Zhu, Cong Yan, Barzan Mozafari, Xinyu Wang. SlabCity: Whole-Query Optimization Using Program Synthesis. VLDB, 2023: 3151-3164. [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]
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Analyzing How BERT Performs Entity Matching.
M Paganelli, F Del Buono, A Baraldi, F Guerra. VLDB 2022 [paper]
Schema Matching using Pre-Trained Language Models.
Y Zhang, A Floratou, J Cahoon, S Krishnan, AC Müller, D Banda, F Psallidas, JM Patel. ICDE 2023 [paper]
DB-BERT: a Database Tuning Tool that "Reads the Manual".
I Trummer. SIGMOD 2022 [paper]
Querying Large Language Models with SQL [Vision].
Mohammed Saeed, Nicola De Cao, Paolo Papotti. arXiv 2023. [pdf].
CAESURA: Language Models as Multi-Modal Query Planners.
Matthias Urban, Carsten Binnig. arXiv 2023. [pdf].
Towards Multi-Modal DBMSs for Seamless Querying of Texts and Tables.
Matthias Urban, Carsten Binnig. arXiv 2023. [pdf].
Multimodal Neural Databases.
Giovanni Trappolini, Andrea Santilli, Emanuele Rodolà, Alon Halevy, Fabrizio Silvestri. arXiv 2023. [pdf].
ChatDB: Augmenting LLMs with Databases AS Their Symbolic Memory.
Chenxu Hu, Jie Fu, Chenzhuang Du, Simian Luo, Junbo Zhao, Hang Zhao. arXiv 2023. [pdf].
Chat2DB 
From Large Language Models to Databases and Back A discussion on research and education.
Sihem Amer-Yahia, Angela Bonifati, Lei Chen, Guoliang Li, Kyuseok Shim, Jianliang Xu, Xiaochun Yang. arXiv 2023. [pdf]
Table-GPT: Table-tuned GPT for Diverse Table Tasks.
Peng Li, Yeye He, Dror Yashar, et al. arXiv 2023. [pdf]
Efficient Memory Management for Large Language Model Serving with PagedAttention.
Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, et al. arXiv 2023. [pdf].
PDFTriage: Question Answering over Long, Structured Documents.
Jon Saad-Falcon, Joe Barrow, Alexa Siu, et al. arXiv, 2023. [pdf].
Automatic Root Cause Analysis via Large Language Models for Cloud Incidents.
Yinfang Chen, Huaibing Xie, Minghua Ma, et al. arXiv 2023. [pdf]
LLM As DBA [Vision]. 
D-Bot: Database Diagnosis System using Large Language Models.
Xuanhe Zhou, Guoliang Li, Zhaoyan Sun, Zhiyuan Liu, Weize Chen, et al. arXiv 2023. [pdf] [code].
GPTuner: A Manual-Reading Database Tuning System via GPT-Guided Bayesian Optimization.
Jiale Lao, Yibo Wang, Yufei Li, et al. arXiv, 2023. [pdf] [code]
DBCopilot: Scaling Natural Language Querying to Massive Databases.
Tianshu Wang, Hongyu Lin, Xianpei Han, et al. arXiv, 2023. [pdf]
LLM-SQL-Solver: Can LLMs Determine SQL Equivalence?
Fuheng Zhao, Lawrence Lim, Ishtiyaque Ahmad, et al. arXiv, 2023. [pdf]
OpsEval: A Comprehensive Task-Oriented AIOps Benchmark for Large Language Models
Yuhe Liu, Changhua Pei, Longlong Xu, et al. arXiv, 2023. [pdf]
Partially Filtered NLP Papers
Prompt Engineering for LLMs
https://www.promptingguide.ai/papers
Deployed AI Algorithms
https://github.com/labmlai/annotated_deep_learning_paper_implementations
LLM Statistics
https://aistratagems.com/large-language-model-llm-statistics/
Brain in a Vat: On Missing Pieces Towards Artificial General Intelligence in Large Language Models
Yuxi Ma, Chi Zhang, Song-Chun Zhu. arXiv 2023. [pdf].
https://github.com/cmu-db/benchbase
https://github.com/cwida/public_bi_benchmark/tree/dev/master