{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T15:44:15Z","timestamp":1772120655675,"version":"3.50.1"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031703614","type":"print"},{"value":"9783031703621","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-70362-1_22","type":"book-chapter","created":{"date-parts":[[2024,8,29]],"date-time":"2024-08-29T03:03:03Z","timestamp":1724900583000},"page":"372-388","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["LATuner: An LLM-Enhanced Database Tuning System Based on\u00a0Adaptive Surrogate Model"],"prefix":"10.1007","author":[{"given":"Chongjiong","family":"Fan","sequence":"first","affiliation":[]},{"given":"Zhicheng","family":"Pan","sequence":"additional","affiliation":[]},{"given":"Wenwen","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Chengcheng","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Wei-Neng","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,8,22]]},"reference":[{"key":"22_CR1","doi-asserted-by":"crossref","unstructured":"Cai, B., et al.: HUNTER: an online cloud database hybrid tuning system for personalized requirements. In: SIGMOD, pp. 646\u2013659 (2022)","DOI":"10.1145\/3514221.3517882"},{"key":"22_CR2","doi-asserted-by":"crossref","unstructured":"Narayanan, D., Thereska, E., Ailamaki, A.: Continuous resource monitoring for self-predicting DBMS. In: MASCOTS, pp. 239\u2013248. IEEE (2005)","DOI":"10.1109\/MASCOTS.2005.21"},{"key":"22_CR3","unstructured":"Storm, A.J., Garcia-Arellano, C., Lightstone, S.S., Diao, Y., Surendra, M.: Adaptive self-tuning memory in DB2. In: PVLDB, pp. 1081\u20131092 (2006)"},{"key":"22_CR4","doi-asserted-by":"crossref","unstructured":"Zhu, Y., et al.: BestConfig: tapping the performance potential of systems via automatic configuration tuning. In: SoCC, pp. 338\u2013350 (2017)","DOI":"10.1145\/3127479.3128605"},{"key":"22_CR5","first-page":"1246","volume":"2","author":"S Duan","year":"2009","unstructured":"Duan, S., Thummala, V., Babu, S.: Tuning database configuration parameters with iTuned. PLVDB 2, 1246\u20131257 (2009)","journal-title":"PLVDB"},{"key":"22_CR6","doi-asserted-by":"crossref","unstructured":"Fekry, A., Carata, L., Pasquier, T., Rice, A., Hopper, A.: To tune or not to tune? In search of optimal configurations for data analytics. In: SIGKDD, pp. 2494\u20132504 (2020)","DOI":"10.1145\/3394486.3403299"},{"issue":"11","key":"22_CR7","first-page":"2953","volume":"15","author":"K Kanellis","year":"2022","unstructured":"Kanellis, K., Ding, C., Kroth, B., M\u00fcller, A., Curino, C., Venkataraman, S.: LlamaTune: sample-efficient DBMS configuration tuning. PVLDB 15(11), 2953\u20132965 (2022)","journal-title":"PVLDB"},{"key":"22_CR8","doi-asserted-by":"crossref","unstructured":"Kunjir, M., Babu, S.: Black or white? How to develop an autotuner for memory-based analytics. In: SIGMOD, pp. 1667\u20131683 (2020)","DOI":"10.1145\/3318464.3380591"},{"issue":"8","key":"22_CR9","first-page":"1939","volume":"17","author":"J Lao","year":"2024","unstructured":"Lao, J., et al.: GPTuner: a manual-reading database tuning system via GPT-guided Bayesian optimization. PVLDB 17(8), 1939\u20131952 (2024)","journal-title":"PVLDB"},{"issue":"12","key":"22_CR10","first-page":"2118","volume":"12","author":"G Li","year":"2019","unstructured":"Li, G., Zhou, X., Li, S., Gao, B.: QTune: a query-aware database tuning system with deep reinforcement learning. PVLDB 12(12), 2118\u20132130 (2019)","journal-title":"PVLDB"},{"key":"22_CR11","doi-asserted-by":"crossref","unstructured":"Van\u00a0Aken, D., Pavlo, A., Gordon, G.J., Zhang, B.: Automatic database management system tuning through large-scale machine learning. In: SIGMOD, pp. 1009\u20131024 (2017)","DOI":"10.1145\/3035918.3064029"},{"key":"22_CR12","doi-asserted-by":"crossref","unstructured":"Zhang, J., et al.: An end-to-end automatic cloud database tuning system using deep reinforcement learning. In: SIGMOD, pp. 415\u2013432 (2019)","DOI":"10.1145\/3299869.3300085"},{"key":"22_CR13","unstructured":"Wilson, A.G., Knowles, D.A., Ghahramani, Z.: Gaussian process regression networks. arXiv:1110.4411 (2011)"},{"issue":"1","key":"22_CR14","first-page":"3873","volume":"15","author":"T Desautels","year":"2014","unstructured":"Desautels, T., Krause, A., Burdick, J.W.: Parallelizing exploration-exploitation tradeoffs in gaussian process bandit optimization. JMLR 15(1), 3873\u20133923 (2014)","journal-title":"JMLR"},{"issue":"2","key":"22_CR15","first-page":"417","volume":"43","author":"T Gu","year":"2024","unstructured":"Gu, T., et al.: BBGP-SDFO: batch Bayesian and gaussian process enhanced subspace derivative free optimization for high-dimensional analog circuit synthesis. TCAD 43(2), 417\u2013430 (2024)","journal-title":"TCAD"},{"issue":"9","key":"22_CR16","doi-asserted-by":"publisher","first-page":"11283","DOI":"10.1109\/TPAMI.2023.3264741","volume":"45","author":"Q Lu","year":"2023","unstructured":"Lu, Q., Polyzos, K.D., Li, B., Giannakis, G.B.: Surrogate modeling for Bayesian optimization beyond a single Gaussian process. TPAMI 45(9), 11283\u201311296 (2023)","journal-title":"TPAMI"},{"issue":"7","key":"22_CR17","first-page":"1241","volume":"14","author":"D Van Aken","year":"2021","unstructured":"Van Aken, D., et al.: An inquiry into machine learning-based automatic configuration tuning services on real-world database management systems. PVLDB 14(7), 1241\u20131253 (2021)","journal-title":"PVLDB"},{"issue":"9","key":"22_CR18","first-page":"1808","volume":"15","author":"X Zhang","year":"2022","unstructured":"Zhang, X., et al.: Facilitating database tuning with hyper-parameter optimization: a comprehensive experimental evaluation. PVLDB 15(9), 1808\u20131821 (2022)","journal-title":"PVLDB"},{"issue":"2","key":"22_CR19","doi-asserted-by":"publisher","DOI":"10.1007\/s11432-021-3578-6","volume":"66","author":"S Huang","year":"2023","unstructured":"Huang, S., Qin, Y., Zhang, X., Tu, Y., Li, Z., Cui, B.: Survey on performance optimization for database systems. Sci. China Inf. Sci. 66(2), 121102 (2023)","journal-title":"Sci. China Inf. Sci."},{"key":"22_CR20","doi-asserted-by":"crossref","unstructured":"Trummer, I.: DB-BERT: a database tuning tool that \u201creads the manual\u201d. In: SIGMOD, pp. 190\u2013203 (2022)","DOI":"10.1145\/3514221.3517843"},{"key":"22_CR21","unstructured":"Liu, T., Astorga, N., Seedat, N., van\u00a0der Schaar, M.: Large language models to enhance Bayesian optimization. arXiv:2402.03921 (2024)"},{"key":"22_CR22","doi-asserted-by":"crossref","unstructured":"Zhang, X., et al.: ResTune: resource oriented tuning boosted by meta-learning for cloud databases. In: SIGMOD, pp. 2102\u20132114 (2021)","DOI":"10.1145\/3448016.3457291"},{"key":"22_CR23","unstructured":"Kanellis, K., Alagappan, R., Venkataraman, S.: Too many knobs to tune? Towards faster database tuning by pre-selecting important knobs. In: HotStorage (2020)"},{"key":"22_CR24","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1007\/s10182-010-0144-z","volume":"94","author":"M Petelet","year":"2009","unstructured":"Petelet, M., Iooss, B., Asserin, O., Loredo, A.: Latin hypercube sampling with inequality constraints. AStA Adv. Stat. Anal. 94, 325\u2013339 (2009)","journal-title":"AStA Adv. Stat. Anal."},{"key":"22_CR25","unstructured":"Bergstra, J., Bardenet, R., Bengio, Y., K\u00e9gl, B.: Algorithms for hyper-parameter optimization. In: NIPS, vol. 24 (2011)"},{"key":"22_CR26","unstructured":"Berahas, A.S., Nocedal, J., Tak\u00e1c, M.: A multi-batch L-BFGS method for machine learning. In: NIPS, vol. 29 (2016)"},{"key":"22_CR27","unstructured":"Chen, J., Mueller, J.: Quantifying uncertainty in answers from any language model and enhancing their trustworthiness arXiv:2308.16175 (2023)"},{"key":"22_CR28","unstructured":"Lin, Z., Trivedi, S., Sun, J.: Generating with confidence: uncertainty quantification for black-box large language models. arXiv:2305.19187 (2023)"},{"key":"22_CR29","unstructured":"Tanneru, S.H., Agarwal, C., Lakkaraju, H.: Quantifying uncertainty in natural language explanations of large language models. arXiv:2311.03533 (2023)"},{"key":"22_CR30","unstructured":"Xiong, M., et al.: Can LLMs express their uncertainty? An empirical evaluation of confidence elicitation in LLMs. arXiv:2306.13063 (2023)"},{"key":"22_CR31","unstructured":"Zhou, H., et al.: Batch calibration: rethinking calibration for in-context learning and prompt engineering. In: ICLR (2024)"},{"key":"22_CR32","doi-asserted-by":"crossref","unstructured":"Lu, Y., Bartolo, M., Moore, A., Riedel, S., Stenetorp, P.: Fantastically ordered prompts and where to find them: overcoming few-shot prompt order sensitivity. arXiv:2104.08786 (2021)","DOI":"10.18653\/v1\/2022.acl-long.556"},{"key":"22_CR33","unstructured":"Zhao, T.Z., Wallace, E., Feng, S., Klein, D., Singh, S.: Calibrate before use: improving few-shot performance of language models. arXiv:2102.09690 (2021)"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases. Research Track"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-70362-1_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T17:49:08Z","timestamp":1756921748000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-70362-1_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031703614","9783031703621"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-70362-1_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"22 August 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vilnius","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lithuania","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 September 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2024.ecmlpkdd.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}