{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T05:11:49Z","timestamp":1775538709057,"version":"3.50.1"},"reference-count":73,"publisher":"Association for Computing Machinery (ACM)","issue":"6","funder":[{"name":"National Key Research & Develop Plan","award":["2023YFB4503600"],"award-info":[{"award-number":["2023YFB4503600"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U23A20299, U24B20144, 62172424, 62276270, 62322214"],"award-info":[{"award-number":["U23A20299, U24B20144, 62172424, 62276270, 62322214"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Australian Research Council Discovery Early Career Researcher Award","award":["DE230100366"],"award-info":[{"award-number":["DE230100366"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. ACM Manag. Data"],"published-print":{"date-parts":[[2025,12,4]]},"abstract":"<jats:p>\n                    Database knob tuning is a long-standing challenge in the database community, aimed at enhancing the performance of database management systems (DBMSs) by minimizing latency and maximizing throughput. Manual tuning, which relies heavily on human expertise, is often inefficient and impractical for large-scale or dynamic deployments. Recent work has explored automating this process using machine learning (ML) and large language models (LLMs). However, existing methods typically require hundreds of workload replays or rely on extensive training data, leading to low tuning efficiency or high preparation costs. Moreover, they also risk generating invalid configurations that can degrade performance or even crash the database. To address these limitations, we introduce\n                    <jats:bold>AgentTune,<\/jats:bold>\n                    the first agent-based knob tuning framework powered by LLMs, designed for efficiency, adaptability, and reliability. AgentTune decomposes the tuning process into four specialized agents:\n                    <jats:italic toggle=\"yes\">Workload Analyzer,<\/jats:italic>\n                    <jats:italic toggle=\"yes\">Knob Selector,<\/jats:italic>\n                    <jats:italic toggle=\"yes\">Range Pruner,<\/jats:italic>\n                    and\n                    <jats:italic toggle=\"yes\">Configuration Recommender,<\/jats:italic>\n                    each responsible for a distinct subtask. These agents collaborate through structured prompt chaining. AgentTune first analyzes the input workload to identify impactful knobs and reconstructs their valid ranges to reduce the search space. It then employs a tree-based search strategy to efficiently explore the configuration space and identify suitable knob values.\n                  <\/jats:p>\n                  <jats:p>We conduct extensive evaluations across diverse workloads (public benchmarks and real-world workloads), metrics (latency and throughput), DBMSs (PostgreSQL, MySQL, and TiDB), hardware environments, and database scales. Experimental results demonstrate that, compared to existing baselines, AgentTune is able to identify superior configurations using significantly fewer workload replays. Furthermore, AgentTune rarely generates invalid configurations during the tuning process, ensuring reliability and suitability for real-world deployments.<\/jats:p>","DOI":"10.1145\/3769758","type":"journal-article","created":{"date-parts":[[2025,12,6]],"date-time":"2025-12-06T04:32:13Z","timestamp":1764995533000},"page":"1-29","source":"Crossref","is-referenced-by-count":0,"title":["AgentTune: An Agent-Based Large Language Model Framework for Database Knob Tuning"],"prefix":"10.1145","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-2620-7623","authenticated-orcid":false,"given":"Yiyan","family":"Li","sequence":"first","affiliation":[{"name":"Renmin University of China, Beijing, China and Key Laboratory of Data Engineering and Knowledge Engineering, MOE, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6610-3841","authenticated-orcid":false,"given":"Haoyang","family":"Li","sequence":"additional","affiliation":[{"name":"Renmin University of China, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2019-225X","authenticated-orcid":false,"given":"Jing","family":"Zhang","sequence":"additional","affiliation":[{"name":"Renmin University of China, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3503-4123","authenticated-orcid":false,"given":"Renata","family":"Borovica-Gajic","sequence":"additional","affiliation":[{"name":"University of Melbourne, Melbourne, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-7215-7367","authenticated-orcid":false,"given":"Shuai","family":"Wang","sequence":"additional","affiliation":[{"name":"ByteDance Inc., Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-2250-5528","authenticated-orcid":false,"given":"Tieying","family":"Zhang","sequence":"additional","affiliation":[{"name":"ByteDance Inc., San Jose, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3734-892X","authenticated-orcid":false,"given":"Jianjun","family":"Chen","sequence":"additional","affiliation":[{"name":"ByteDance Inc., San Jose, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-9122-4703","authenticated-orcid":false,"given":"Rui","family":"Shi","sequence":"additional","affiliation":[{"name":"ByteDance Inc., Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0089-1045","authenticated-orcid":false,"given":"Cuiping","family":"Li","sequence":"additional","affiliation":[{"name":"Renmin University of China, Beijing, China and Engineering Research Center of Database and Business Intelligence, MOE, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8132-9382","authenticated-orcid":false,"given":"Hong","family":"Chen","sequence":"additional","affiliation":[{"name":"Renmin University of China, Beijing, China and Key Laboratory of Data Engineering and Knowledge Engineering, MOE, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,12,5]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.18653\/V1\/N19-1388"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3035918.3064029"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.14778\/3450980.3450992"},{"key":"e_1_2_1_4_1","unstructured":"Anthropic. 2024. Introducing the next generation of Claude. (2024). Available at: https:\/\/www.anthropic.com\/news\/claude-3-family."},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3514221.3517882"},{"key":"e_1_2_1_6_1","volume-title":"Proceedings of the 33rd International Conference on Very Large Data Bases","author":"Chaudhuri Surajit","year":"2007","unstructured":"Surajit Chaudhuri and Vivek R. Narasayya. 2007. Self-Tuning Database Systems: A Decade of Progress. In Proceedings of the 33rd International Conference on Very Large Data Bases, University of Vienna, Austria, September 23-27, 2007, Christoph Koch, Johannes Gehrke, Minos N. Garofalakis, Divesh Srivastava, Karl Aberer, Anand Deshpande, Daniela Florescu, Chee Yong Chan, Venkatesh Ganti, Carl-Christian Kanne, Wolfgang Klas, and Erich J. Neuhold (Eds.). ACM, 3-14. http:\/\/www.vldb.org\/conf\/2007\/papers\/special\/p3-chaudhuri.pdf"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2010.121"},{"key":"e_1_2_1_8_1","unstructured":"Mark Chen Jerry Tworek Heewoo Jun Qiming Yuan Henrique Pond\u00e9 de Oliveira Pinto Jared Kaplan Harri Edwards Yuri Burda Nicholas Joseph Greg Brockman Alex Ray Raul Puri Gretchen Krueger Michael Petrov Heidy Khlaaf Girish Sastry Pamela Mishkin Brooke Chan Scott Gray Nick Ryder Mikhail Pavlov Alethea Power Lukasz Kaiser Mohammad Bavarian Clemens Winter Philippe Tillet Felipe Petroski Such Dave Cummings Matthias Plappert Fotios Chantzis Elizabeth Barnes Ariel Herbert-Voss William Hebgen Guss Alex Nichol Alex Paino Nikolas Tezak Jie Tang Igor Babuschkin Suchir Balaji Shantanu Jain William Saunders Christopher Hesse Andrew N. Carr Jan Leike Joshua Achiam Vedant Misra Evan Morikawa Alec Radford Matthew Knight Miles Brundage Mira Murati Katie Mayer Peter Welinder Bob McGrew Dario Amodei Sam McCandlish Ilya Sutskever and Wojciech Zaremba. 2021. Evaluating Large Language Models Trained on Code. CoRR Vol. abs\/2107.03374 (2021). arXiv:2107.03374 https:\/\/arxiv.org\/abs\/2107.03374"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.18653\/V1\/P18-1008"},{"key":"e_1_2_1_10_1","volume-title":"The Twelfth International Conference on Learning Representations, ICLR 2024","author":"Chen Xinyun","year":"2024","unstructured":"Xinyun Chen, Maxwell Lin, Nathanael Sch\u00e4rli, and Denny Zhou. 2024. Teaching Large Language Models to Self-Debug. In The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024. OpenReview.net. https:\/\/openreview.net\/forum?id=KuPixIqPiq"},{"key":"e_1_2_1_11_1","volume-title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv:1810.04805 [cs.CL] https:\/\/arxiv.org\/abs\/1810.04805","author":"Devlin Jacob","year":"2019","unstructured":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv:1810.04805 [cs.CL] https:\/\/arxiv.org\/abs\/1810.04805"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.14778\/1453856.1453976"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.14778\/1687627.1687767"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-70362-1_22"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.14778\/3681954.3681960"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.14778\/3641204.3641221"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3709652"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.14778\/3503585.3503586"},{"key":"e_1_2_1_19_1","unstructured":"Major Hayden. 2024. MySQLTuner - A script to review and tune your MySQL installation. https:\/\/github.com\/major\/MySQLTuner-perl. Accessed: 2025-07-22."},{"key":"e_1_2_1_20_1","volume-title":"The Curious Case of Neural Text Degeneration. In 8th International Conference on Learning Representations, ICLR 2020","author":"Holtzman Ari","year":"2020","unstructured":"Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2020. The Curious Case of Neural Text Degeneration. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020. OpenReview.net. https:\/\/openreview.net\/forum?id=rygGQyrFvH"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.14778\/3415478.3415535"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.2311.05232"},{"key":"e_1_2_1_23_1","doi-asserted-by":"crossref","unstructured":"Xinmei Huang Haoyang Li Jing Zhang Xinxin Zhao Zhiming Yao Yiyan Li Tieying Zhang Jianjun Chen Hong Chen and Cuiping Li. 2025. E2ETune: End-to-End Knob Tuning via Fine-tuned Generative Language Model. arXiv:2404.11581 [cs.AI] https:\/\/arxiv.org\/abs\/2404.11581","DOI":"10.14778\/3773731.3773732"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.14778\/3551793.3551844"},{"key":"e_1_2_1_25_1","unstructured":"Alexey Kopytov. 2024. Scriptable database and system performance benchmark. (2024). Available at: https:\/\/github.com\/akopytov\/sysbench\/."},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.14778\/3659437.3659449"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.14778\/2850583.2850594"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.14778\/3352063.3352129"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1609\/AAAI.V37I11.26535"},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3654930"},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","unstructured":"Yujia Li David H. Choi Junyoung Chung Nate Kushman Julian Schrittwieser R\u00e9mi 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. Competition-Level Code Generation with AlphaCode. CoRR Vol. abs\/2203.07814 (2022). doi:10.48550\/ARXIV.2203.07814 arXiv:2203.07814","DOI":"10.48550\/ARXIV.2203.07814"},{"key":"e_1_2_1_32_1","unstructured":"Yiyan Li Haoyang Li Zhao Pu Jing Zhang Xinyi Zhang Tao Ji Luming Sun Cuiping Li and Hong Chen. 2024a. Is Large Language Model Good at Database Knob Tuning? A Comprehensive Experimental Evaluation. arXiv:2408.02213 [cs.DB] https:\/\/arxiv.org\/abs\/2408.02213"},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.2404.12872"},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE53745.2022.00195"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.2403.09060"},{"key":"e_1_2_1_36_1","volume-title":"Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023","author":"Liu Junling","year":"2023","unstructured":"Junling Liu, Peilin Zhou, Yining Hua, Dading Chong, Zhongyu Tian, Andrew Liu, Helin Wang, Chenyu You, Zhenhua Guo, Lei Zhu, and Michael Lingzhi Li. 2023. Benchmarking Large Language Models on CMExam - A comprehensive Chinese Medical Exam Dataset. 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.). http:\/\/papers.nips.cc\/paper_files\/paper\/2023\/hash\/a48ad12d588c597f4725a8b84af647b5-Abstract-Datasets_and_Benchmarks.html"},{"key":"e_1_2_1_37_1","volume-title":"Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017","author":"Scott","year":"2017","unstructured":"Scott M. Lundberg and Su-In Lee. 2017. A Unified Approach to Interpreting Model Predictions. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA, Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, and Roman Garnett (Eds.). 4765-4774. https:\/\/proceedings.neurips.cc\/paper\/2017\/hash\/8a20a8621978632d76c43dfd28b67767-Abstract.html"},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/167293.167637"},{"key":"e_1_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.2410.05229"},{"key":"e_1_2_1_40_1","unstructured":"OpenAI. 2023. GPT-4 Technical Report. CoRR Vol. abs\/2303.08774 (2023). doi:10.48550\/ARXIV.2303.08774 arXiv:2303.08774"},{"key":"e_1_2_1_41_1","unstructured":"OpenAI. 2024. Hello gpt-4o. (2024). Available at: https:\/\/openai.com\/index\/hello-gpt-4o\/."},{"key":"e_1_2_1_42_1","doi-asserted-by":"crossref","unstructured":"Long Ouyang Jeffrey Wu Xu Jiang Diogo Almeida Carroll Wainwright Pamela Mishkin Chong Zhang Sandhini Agarwal Katarina Slama Alex Ray et al. 2022. Training language models to follow instructions with human feedback. Advances in neural information processing systems Vol. 35 (2022) 27730-27744.","DOI":"10.52202\/068431-2011"},{"key":"e_1_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1080\/00207548808947840"},{"key":"e_1_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.14778\/3476311.3476411"},{"key":"e_1_2_1_45_1","unstructured":"PingCAP. 2024. TiDB v8.2 documentation. https:\/\/docs-archive.pingcap.com\/zh\/tidb\/v8.2\/ Version 8.2."},{"key":"e_1_2_1_46_1","volume-title":"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","author":"Pourreza Mohammadreza","year":"2023","unstructured":"Mohammadreza Pourreza and Davood Rafiei. 2023. 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.). http:\/\/papers.nips.cc\/paper_files\/paper\/2023\/hash\/72223cc66f63ca1aa59edaec1b3670e6-Abstract-Conference.html"},{"key":"e_1_2_1_47_1","volume-title":"The Twelfth International Conference on Learning Representations, ICLR 2024","author":"Qin Yujia","year":"2024","unstructured":"Yujia Qin, Shihao Liang, Yining Ye, Kunlun Zhu, Lan Yan, Yaxi Lu, Yankai Lin, Xin Cong, Xiangru Tang, Bill Qian, Sihan Zhao, Lauren Hong, Runchu Tian, Ruobing Xie, Jie Zhou, Mark Gerstein, Dahai Li, Zhiyuan Liu, and Maosong Sun. 2024. ToolLLM: Facilitating Large Language Models to Master 16000 Real-world APIs. In The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024. OpenReview.net. https:\/\/openreview.net\/forum?id=dHng2O0Jjr"},{"key":"e_1_2_1_48_1","unstructured":"Alec Radford Jeffrey Wu Rewon Child David Luan Dario Amodei Ilya Sutskever et al. 2019. Language models are unsupervised multitask learners. OpenAI blog Vol. 1 8 (2019) 9."},{"key":"e_1_2_1_49_1","volume-title":"Multitask Prompted Training Enables Zero-Shot Task Generalization. In The Tenth International Conference on Learning Representations, ICLR 2022","author":"Sanh Victor","year":"2022","unstructured":"Victor Sanh, Albert Webson, Colin Raffel, Stephen H. Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Arun Raja, Manan Dey, M Saiful Bari, Canwen Xu, Urmish Thakker, Shanya Sharma Sharma, Eliza Szczechla, Taewoon Kim, Gunjan Chhablani, Nihal V. Nayak, Debajyoti Datta, Jonathan Chang, Mike Tian-Jian Jiang, Han Wang, Matteo Manica, Sheng Shen, Zheng Xin Yong, Harshit Pandey, Rachel Bawden, Thomas Wang, Trishala Neeraj, Jos Rozen, Abheesht Sharma, Andrea Santilli, Thibault F\u00e9vry, Jason Alan Fries, Ryan Teehan, Teven Le Scao, Stella Biderman, Leo Gao, Thomas Wolf, and Alexander M. Rush. 2022. Multitask Prompted Training Enables Zero-Shot Task Generalization. In The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022. OpenReview.net. https:\/\/openreview.net\/forum?id=9Vrb9D0WI4"},{"key":"e_1_2_1_50_1","volume-title":"Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023","author":"Schick Timo","year":"2023","unstructured":"Timo Schick, Jane Dwivedi-Yu, Roberto Dess\u00ec, Roberta Raileanu, Maria Lomeli, Eric Hambro, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom. 2023. Toolformer: Language Models Can Teach Themselves to Use Tools. 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.). http:\/\/papers.nips.cc\/paper_files\/paper\/2023\/hash\/d842425e4bf79ba039352da0f658a906-Abstract-Conference.html"},{"key":"e_1_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.14778\/3339490.3339503"},{"key":"e_1_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1996.tb02080.x"},{"key":"e_1_2_1_53_1","unstructured":"Transaction Processing Performance Council (TPC). 2010. TPC Benchmark C Standard Specification. http:\/\/www.tpc.org\/tpcc\/ Version 5.11.0."},{"key":"e_1_2_1_54_1","unstructured":"Transaction Processing Performance Council (TPC). 2024. TPC Benchmark DS Standard Specification. https:\/\/www.tpc.org\/tpcds\/default5.asp Version 4.0.0."},{"key":"e_1_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1145\/3514221.3517843"},{"key":"e_1_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00778-023-00831-y"},{"key":"e_1_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.14778\/3402707.3402724"},{"key":"e_1_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.14778\/3450980.3450992"},{"key":"e_1_2_1_59_1","volume-title":"Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is All you Need. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA, Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, and Roman Garnett (Eds.). 5998-6008. https:\/\/proceedings.neurips.cc\/paper\/2017\/hash\/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html"},{"key":"e_1_2_1_60_1","unstructured":"Oleksii Vasyliev. 2024. Pgtune - tuning PostgreSQL config by your hardware. https:\/\/github.com\/le0pard\/pgtune. Accessed: 2025-07-22."},{"key":"e_1_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.2309.07864"},{"key":"e_1_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.2501.15383"},{"key":"e_1_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.31193\/ssap.01.9787509752807"},{"key":"e_1_2_1_64_1","volume-title":"ReAct: Synergizing Reasoning and Acting in Language Models. In The Eleventh International Conference on Learning Representations, ICLR 2023","author":"Yao Shunyu","year":"2023","unstructured":"Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik R. Narasimhan, and Yuan Cao. 2023. ReAct: Synergizing Reasoning and Acting in Language Models. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenReview.net. https:\/\/openreview.net\/forum?id=WE_vluYUL-X"},{"key":"e_1_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1007\/S00778-021-00670-9"},{"key":"e_1_2_1_66_1","doi-asserted-by":"publisher","DOI":"10.14778\/3538598.3538604"},{"key":"e_1_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.1145\/3448016.3457291"},{"key":"e_1_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.1145\/3514221.3526176"},{"key":"e_1_2_1_69_1","doi-asserted-by":"publisher","DOI":"10.14778\/3632093.3632114"},{"key":"e_1_2_1_70_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2023.3266893"},{"key":"e_1_2_1_71_1","doi-asserted-by":"publisher","DOI":"10.14778\/3529337.3529349"},{"key":"e_1_2_1_72_1","doi-asserted-by":"publisher","DOI":"10.14778\/3675034.3675043"},{"key":"e_1_2_1_73_1","doi-asserted-by":"publisher","DOI":"10.1145\/3127479.3128605"}],"container-title":["Proceedings of the ACM on Management of Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3769758","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T04:30:18Z","timestamp":1775536218000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3769758"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,4]]},"references-count":73,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,12,4]]}},"alternative-id":["10.1145\/3769758"],"URL":"https:\/\/doi.org\/10.1145\/3769758","relation":{},"ISSN":["2836-6573"],"issn-type":[{"value":"2836-6573","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,4]]}}}