Fetching the paper…
Reading the bibliography…
Database knob tuning is a significant challenge for database administrators, as it involves tuning a large number of configuration knobs with continuous or discrete values to achieve optimal database performance.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 1901
Earlier work this paper cites.
A probabilistic justification for using tf x idf term weighting in information retrieval
Djoerd Hiemstra. 2000 · 2000
Earlier work this paper cites.
SQL Memory Management in Oracle9i. In Proceedings of 28th International Conference on Very Large Data Bases, VLDB 2002, Hong Kong, August 20-23, 2002 . Morgan Kaufmann, 962–973
Benoît Dageville and Mohamed Zaït. 2002 · 2002
Earlier work this paper cites.
Optimizing hyperparameters of support vector machines by genetic algorithms.. In IC-AI , Vol. 74. 82
Stefan Lessmann, Robert Stahlbock, and Sven F Crone. 2005 · 2005
Earlier work this paper cites.
The star schema benchmark (SSB)
Patrick E O’Neil, Elizabeth J O’Neil, and Xuedong Chen. 2007 · 2007
Earlier work this paper cites.
Tuning database configuration parameters with ituned
Songyun Duan, Vamsidhar Thummala, and Shivnath Babu. 2009 · 2009
Earlier work this paper cites.
Sequential model-based optimization for general algorithm configuration. In Learning and Intelligent Optimization: 5th International Conference, LION 5, Rome, Italy, January 17-21, 2011. Selected Papers 5 . Springer, 507–523
Frank Hutter, Holger H Hoos, and Kevin Leyton-Brown. 2011 · 2011
Earlier work this paper cites.
Oltp-bench: An extensible testbed for benchmarking relational databases
Djellel Eddine Difallah, Andrew Pavlo, Carlo Curino, and Philippe Cudre-Mauroux. 2013 · 2013
Earlier work this paper cites.
OpenTuner: an extensible framework for program autotuning. In International Conference on Parallel Architectures and Compilation, PACT ’14, Edmonton, AB, Canada, August 24-27, 2014 . ACM, 303–316
Jason Ansel, Shoaib Kamil, Kalyan Veeramachaneni, Jonathan Ragan-Kelley, Jeffrey Bosboom, Una-May O’Reilly, and Saman P. Amarasinghe. 2014 · 2014
Earlier work this paper cites.
Deterministic policy gradient algorithms. In International conference on machine learning . Pmlr, 387–395
David Silver, Guy Lever, Nicolas Heess, Thomas Degris, Daan Wierstra, and Martin Riedmiller. 2014 · 2014
Earlier work this paper cites.
Efficient and robust automated machine learning
Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Springenberg, Manuel Blum, and Frank Hutter. 2015 · 2015
Earlier work this paper cites.
How good are query optimizers, really?
Viktor Leis, Andrey Gubichev, Atanas Mirchev, Peter Boncz, Alfons Kemper, and Thomas Neumann. 2015 · 2015
Earlier work this paper cites.
The synthetic data vault. In 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA) . IEEE, 399–410
Neha Patki, Roy Wedge, and Kalyan Veeramachaneni. 2016 · 2016
Earlier work this paper cites.
Automatic database management system tuning through large-scale machine learning. In Proceedings of the 2017 ACM international conference on management of data . 1009–1024
Dana Van Aken, Andrew Pavlo, Geoffrey J Gordon, and Bohan Zhang. 2017 · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Earlier work this paper cites.
Tao Yu, Rui Zhang, Kai Yang, Michihiro Yasunaga, Dongxu Wang, Zifan Li, James Ma, Irene Li, Qingning Yao, Shanelle Roman, et al · 2018
Earlier work this paper cites.
Qtune: A query-aware database tuning system with deep reinforcement learning
Guoliang Li, Xuanhe Zhou, Shifu Li, and Bo Gao. 2019 · 2019
Earlier work this paper cites.
ibtune: Individualized buffer tuning for large-scale cloud databases
Jian Tan, Tieying Zhang, Feifei Li, Jie Chen, Qixing Zheng, Ping Zhang, Honglin Qiao, Yue Shi, Wei Cao, and Rui Zhang. 2019 · 2019
Earlier work this paper cites.
Huggingface’s transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, et al · 2019
Earlier work this paper cites.
An end-to-end automatic cloud database tuning system using deep reinforcement learning. In Proceedings of the 2019 International Conference on Management of Data . 415–432
Ji Zhang, Yu Liu, Ke Zhou, Guoliang Li, Zhili Xiao, Bin Cheng, Jiashu Xing, Yangtao Wang, Tianheng Cheng, Li Liu, et al · 2019
Earlier work this paper cites.
Hebo: Heteroscedastic evolutionary bayesian optimisation
Alexander I Cowen-Rivers, Wenlong Lyu, Zhi Wang, Rasul Tutunov, Hao Jianye, Jun Wang, and Haitham Bou Ammar. 2020 · 2020
Earlier work this paper cites.
TaPas: Weakly Supervised Table Parsing via Pre-training. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, July 5-10, 2020 , Dan Jurafsky, Joyce Chai, Natalie Schluter, and Joel R. Tetreault (Eds.). Association for Computational Linguistics, 4320–4333
Jonathan Herzig, Pawel Krzysztof Nowak, Thomas Müller, Francesco Piccinno, and Julian Martin Eisenschlos. 2020 · 2020
Earlier work this paper cites.
Black or white? how to develop an autotuner for memory-based analytics. In Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data . 1667–1683
Mayuresh Kunjir and Shivnath Babu. 2020 · 2020
Earlier work this paper cites.
ZeRO: memory optimizations toward training trillion parameter models. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2020, Virtual Event / Atlanta, Georgia, USA, November 9-19, 2020 . IEEE/ACM, 20
Samyam Rajbhandari, Jeff Rasley, Olatunji Ruwase, and Yuxiong He. 2020 · 2020
Earlier work this paper cites.
Solving black-box optimization challenge via learning search space partition for local Bayesian optimization. In NeurIPS 2020 Competition and Demonstration Track . PMLR, 77–85
Mikita Sazanovich, Anastasiya Nikolskaya, Yury Belousov, and Aleksei Shpilman. 2021 · 2020
Earlier work this paper cites.
Database meets artificial intelligence: A survey
Xuanhe Zhou, Chengliang Chai, Guoliang Li, and Ji Sun. 2020 · 2020
Earlier work this paper cites.
Learning to Optimize Black-Box Functions with Extreme Limits on the Number of Function Evaluations. In Learning and Intelligent Optimization: 15th International Conference, LION 15, Athens, Greece, June 20–25, 2021, Revised Selected Papers 15 . Springer, 7–24
Carlos Ansótegui, Meinolf Sellmann, Tapan Shah, and Kevin Tierney. 2021 · 2021
Earlier work this paper cites.
Cgptuner: a contextual gaussian process bandit approach for the automatic tuning of it configurations under varying workload conditions
Stefano Cereda, Stefano Valladares, Paolo Cremonesi, and Stefano Doni. 2021 · 2021
Earlier work this paper cites.
Evaluating Large Language Models Trained on Code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Pondé de Oliveira Pinto, Jared Kaplan, Harrison Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, and et al. 2021b · 2021
Earlier work this paper cites.
FinQA: A Dataset of Numerical Reasoning over Financial Data. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021, Virtual Event / Punta Cana, Dominican Republic, 7-11 November, 2021 , Marie-Francine Moens, Xuanjing Huang, Lucia Specia, and Scott Wen-tau Yih (Eds.). Association for Computational Linguistics, 3697–3711
Zhiyu Chen, Wenhu Chen, Charese Smiley, Sameena Shah, Iana Borova, Dylan Langdon, Reema Moussa, Matt Beane, Ting-Hao Huang, Bryan R. Routledge, and William Yang Wang. 2021a · 2021
Earlier work this paper cites.
WATuning: a workload-aware tuning system with attention-based deep reinforcement learning
Jia-Ke Ge, Yan-Feng Chai, and Yun-Peng Chai. 2021 · 2021
Earlier work this paper cites.
One model to rule them all: towards zero-shot learning for databases
Benjamin Hilprecht and Carsten Binnig. 2021 · 2021
Earlier work this paper cites.
Bao: Making Learned Query Optimization Practical. In SIGMOD ’21: International Conference on Management of Data, Virtual Event, China, June 20-25, 2021 . ACM, 1275–1288
Ryan Marcus, Parimarjan Negi, Hongzi Mao, Nesime Tatbul, Mohammad Alizadeh, and Tim Kraska. 2021 · 2021
Earlier work this paper cites.
Ember: No-Code Context Enrichment via Similarity-Based Keyless Joins
Sahaana Suri, Ihab F. Ilyas, Christopher Ré, and Theodoros Rekatsinas. 2021 · 2021
Cited alongside, same era.
RPT: Relational Pre-trained Transformer Is Almost All You Need towards Democratizing Data Preparation
Nan Tang, Ju Fan, Fangyi Li, Jianhong Tu, Xiaoyong Du, Guoliang Li, Samuel Madden, and Mourad Ouzzani. 2021 · 2021
Cited alongside, same era.
An inquiry into machine learning-based automatic configuration tuning services on real-world database management systems
Dana Van Aken, Dongsheng Yang, Sebastien Brillard, Ari Fiorino, Bohan Zhang, Christian Bilien, and Andrew Pavlo. 2021 · 2021
Cited alongside, same era.
Xingchen Wan, Vu Nguyen, Huong Ha, Binxin Ru, Cong Lu, and Michael A Osborne. 2021 · 2021
Cited alongside, same era.
UDO: universal database optimization using reinforcement learning
Haoran Luo, Zichen Tang, Shiyao Peng, Yikai Guo, Wentai Zhang, Chenghao Ma, Guanting Dong, Meina Song, Wei Lin, et al · 2023
Later among the works it cites.
OpenAI. 2023 · 2023
Later among the works it cites.
DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction. In Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023 , Alice Oh, Tristan Naumann, Amir Globerson, Kate Saenko, Moritz Hardt, and Sergey Levine (Eds.)
Mohammadreza Pourreza and Davood Rafiei. 2023 · 2023
Later among the works it cites.
ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs
Yujia Qin, Shihao Liang, Yining Ye, Kunlun Zhu, Lan Yan, Yaxi Lu, Yankai Lin, Xin Cong, Xiangru Tang, Bill Qian, Sihan Zhao, Runchu Tian, Ruobing Xie, Jie Zhou, Mark Gerstein, Dahai Li, Zhiyuan Liu, and Maosong Sun. 2023 · 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Junxiong Wang, Immanuel Trummer, and Debabrota Basu. 2021 · 2021
Cited alongside, same era.
Restune: Resource oriented tuning boosted by meta-learning for cloud databases. In Proceedings of the 2021 international conference on management of data . 2102–2114
Xinyi Zhang, Hong Wu, Zhuo Chang, Shuowei Jin, Jian Tan, Feifei Li, Tieying Zhang, and Bin Cui. 2021 · 2021
Cited alongside, same era.
ODBO: Bayesian optimization with search space prescreening for directed protein evolution
Lixue Cheng, Ziyi Yang, Changyu Hsieh, Benben Liao, and Shengyu Zhang. 2022 · 2022
Cited alongside, same era.
PaLM: Scaling Language Modeling with Pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, and et al. 2022 · 2022
Cited alongside, same era.
OmniTab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2022, Seattle, WA, United States, July 10-15, 2022 . Association for Computational Linguistics, 932–942
Zhengbao Jiang, Yi Mao, Pengcheng He, Graham Neubig, and Weizhu Chen. 2022 · 2022
Cited alongside, same era.
LlamaTune: sample-efficient DBMS configuration tuning
Konstantinos Kanellis, Cong Ding, Brian Kroth, Andreas Müller, Carlo Curino, and Shivaram Venkataraman. 2022 · 2022
Cited alongside, same era.
Competition-Level Code Generation with AlphaCode
Yujia Li, David H. Choi, Junyoung Chung, Nate Kushman, Julian Schrittwieser, Rémi Leblond, Tom Eccles, James Keeling, Felix Gimeno, Agustin Dal Lago, Thomas Hubert, Peter Choy, Cyprien de Masson d’Autume, Igor Babuschkin, Xinyun Chen, Po-Sen Huang, Johannes Welbl, Sven Gowal, Alexey Cherepanov, James Molloy, Daniel J. Mankowitz, Esme Sutherland Robson, Pushmeet Kohli, Nando de Freitas, Koray Kavukcuoglu, and Oriol Vinyals. 2022 · 2022
Cited alongside, same era.
SMAC3: A versatile Bayesian optimization package for hyperparameter optimization
Marius Lindauer, Katharina Eggensperger, Matthias Feurer, André Biedenkapp, Difan Deng, Carolin Benjamins, Tim Ruhkopf, René Sass, and Frank Hutter. 2022 · 2022
Cited alongside, same era.
Later among the works it cites.
Code llama: Open foundation models for code
Baptiste Roziere, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Xiaoqing Ellen Tan, Yossi Adi, Jingyu Liu, Tal Remez, Jérémy Rapin, et al · 2023
Later among the works it cites.
Llama 2: Open foundation and fine-tuned chat models, 2023
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 2023
Later among the works it cites.
Llama 2: Open Foundation and Fine-Tuned Chat Models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, and et al. 2023a · 2023
Later among the works it cites.
Can Large Language Models Predict Data Correlations from Column Names?
Immanuel Trummer. 2023a · 2023
Later among the works it cites.
Demonstrating GPT-DB: Generating Query-Specific and Customizable Code for SQL Processing with GPT-4
Immanuel Trummer. 2023b · 2023
Later among the works it cites.
From bert to gpt-3 codex: harnessing the potential of very large language models for data management
Immanuel Trummer. 2023c · 2023
Later among the works it cites.
Large Language Models are Versatile Decomposers: Decomposing Evidence and Questions for Table-based Reasoning. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2023, Taipei, Taiwan, July 23-27, 2023 . ACM, 174–184
Yunhu Ye, Binyuan Hui, Min Yang, Binhua Li, Fei Huang, and Yongbin Li. 2023 · 2023
Later among the works it cites.
An Efficient Transfer Learning Based Configuration Adviser for Database Tuning
Xinyi Zhang, Hong Wu, Yang Li, Zhengju Tang, Jian Tan, Feifei Li, and Bin Cui. 2023 · 2023
Later among the works it cites.
Automatic Database Knob Tuning: A Survey
Xinyang Zhao, Xuanhe Zhou, and Guoliang Li. 2023 · 2023
Later among the works it cites.
Aojun Zhou, Ke Wang, Zimu Lu, Weikang Shi, Sichun Luo, Zipeng Qin, Shaoqing Lu, Anya Jia, Linqi Song, Mingjie Zhan, and Hongsheng Li. 2023c · 2023
Later among the works it cites.
Xuanhe Zhou, Guoliang Li, and Zhiyuan Liu. 2023a · 2023
Later among the works it cites.
D-Bot: Database Diagnosis System using Large Language Models
Xuanhe Zhou, Guoliang Li, Zhaoyan Sun, Zhiyuan Liu, Weize Chen, Jianming Wu, Jiesi Liu, Ruohang Feng, and Guoyang Zeng. 2023b · 2023
Later among the works it cites.
Lero: A Learning-to-Rank Query Optimizer
Rong Zhu, Wei Chen, Bolin Ding, Xingguang Chen, Andreas Pfadler, Ziniu Wu, and Jingren Zhou. 2023 · 2023
Later among the works it cites.
PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation. In Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2, ASPLOS 2024, La Jolla, CA, USA, 27 April 2024- 1 May 2024 . ACM, 929–947
Jason Ansel, Edward Z. Yang, Horace He, Natalia Gimelshein, Animesh Jain, Michael Voznesensky, Bin Bao, Peter Bell, David Berard, Evgeni Burovski, Geeta Chauhan, Anjali Chourdia, Will Constable, Alban Desmaison, Zachary DeVito, Elias Ellison, Will Feng, Jiong Gong, Michael Gschwind, Brian Hirsh, Sherlock Huang, Kshiteej Kalambarkar, Laurent Kirsch, Michael Lazos, Mario Lezcano, Yanbo Liang, Jason Liang, Yinghai Lu, C. K. Luk, Bert Maher, Yunjie Pan, Christian Puhrsch, Matthias Reso, Mark Saroufim, Marcos Yukio Siraichi, Helen Suk, Shunting Zhang, Michael Suo, Phil Tillet, Xu Zhao, Eikan Wang, Keren Zhou, Richard Zou, Xiaodong Wang, Ajit Mathews, William Wen, Gregory Chanan, Peng Wu, and Soumith Chintala. 2024 · 2024
Closest in time.
Introducing the next generation of Claude
Anthropic. 2024 · 2024
Closest in time.
Qwen2 Github
Alibaba Cloud. 2024 · 2024
Closest in time.
Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al · 2024
Closest in time.
DeepSeek-Coder: When the Large Language Model Meets Programming–The Rise of Code Intelligence
Daya Guo, Qihao Zhu, Dejian Yang, Zhenda Xie, Kai Dong, Wentao Zhang, Guanting Chen, Xiao Bi, Yu Wu, YK Li, et al · 2024
Closest in time.
CodeS: Towards Building Open-source Language Models for Text-to-SQL
Haoyang Li, Jing Zhang, Hanbing Liu, Ju Fan, Xiaokang Zhang, Jun Zhu, Renjie Wei, Hongyan Pan, Cuiping Li, and Hong Chen. 2024b · 2024
Closest in time.
Can llm already serve as a database interface? a big bench for large-scale database grounded text-to-sqls
Jinyang Li, Binyuan Hui, Ge Qu, Jiaxi Yang, Binhua Li, Bowen Li, Bailin Wang, Bowen Qin, Ruiying Geng, Nan Huo, et al · 2024
Closest in time.
Introducing Meta Llama 3: The most capable openly available LLM to date
Meta. 2024 · 2024
Closest in time.
GPT-4 Turbo and GPT-4
OpenAI. 2024 · 2024
Closest in time.
Tool Learning with Large Language Models: A Survey
Changle Qu, Sunhao Dai, Xiaochi Wei, Hengyi Cai, Shuaiqiang Wang, Dawei Yin, Jun Xu, and Ji-Rong Wen. 2024 · 2024
Closest in time.
Talking about large language models
Murray Shanahan. 2024 · 2024
Closest in time.
DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Mingchuan Zhang, Y. K. Li, Y. Wu, and Daya Guo. 2024 · 2024
Closest in time.
Judging llm-as-a-judge with mt-bench and chatbot arena
Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, et al · 2024
Closest in time.
DB-GPT: Large Language Model Meets Database
Xuanhe Zhou, Zhaoyan Sun, and Guoliang Li. 2024 · 2024
Closest in time.
Flow-loss: learning cardinality estimates that matter
Parimarjan Negi, Ryan Marcus, Andreas Kipf, Hongzi Mao, Nesime Tatbul, Tim Kraska, and Mohammad Alizadeh. 2021 · 2032
Closest in time.