{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T08:59:13Z","timestamp":1768899553900,"version":"3.49.0"},"reference-count":14,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2024,9,18]],"date-time":"2024-09-18T00:00:00Z","timestamp":1726617600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Funds through FCT (Foundation for Science and Technology)","award":["UIDB\/05583\/2020"],"award-info":[{"award-number":["UIDB\/05583\/2020"]}]},{"name":"National Funds through FCT (Foundation for Science and Technology)","award":["CEECINST\/00077\/2021"],"award-info":[{"award-number":["CEECINST\/00077\/2021"]}]},{"name":"National Funds through FCT (Foundation for Science and Technology)","award":["UIDB\/50008\/2020"],"award-info":[{"award-number":["UIDB\/50008\/2020"]}]},{"name":"Research Center in Digital Services (CISeD)","award":["UIDB\/05583\/2020"],"award-info":[{"award-number":["UIDB\/05583\/2020"]}]},{"name":"Research Center in Digital Services (CISeD)","award":["CEECINST\/00077\/2021"],"award-info":[{"award-number":["CEECINST\/00077\/2021"]}]},{"name":"Research Center in Digital Services (CISeD)","award":["UIDB\/50008\/2020"],"award-info":[{"award-number":["UIDB\/50008\/2020"]}]},{"name":"Instituto Polit\u00e9cnico de Viseu","award":["UIDB\/05583\/2020"],"award-info":[{"award-number":["UIDB\/05583\/2020"]}]},{"name":"Instituto Polit\u00e9cnico de Viseu","award":["CEECINST\/00077\/2021"],"award-info":[{"award-number":["CEECINST\/00077\/2021"]}]},{"name":"Instituto Polit\u00e9cnico de Viseu","award":["UIDB\/50008\/2020"],"award-info":[{"award-number":["UIDB\/50008\/2020"]}]},{"name":"FCT (Foundation for Science and Technology)","award":["UIDB\/05583\/2020"],"award-info":[{"award-number":["UIDB\/05583\/2020"]}]},{"name":"FCT (Foundation for Science and Technology)","award":["CEECINST\/00077\/2021"],"award-info":[{"award-number":["CEECINST\/00077\/2021"]}]},{"name":"FCT (Foundation for Science and Technology)","award":["UIDB\/50008\/2020"],"award-info":[{"award-number":["UIDB\/50008\/2020"]}]},{"name":"FCT\/MCTES through national funds","award":["UIDB\/05583\/2020"],"award-info":[{"award-number":["UIDB\/05583\/2020"]}]},{"name":"FCT\/MCTES through national funds","award":["CEECINST\/00077\/2021"],"award-info":[{"award-number":["CEECINST\/00077\/2021"]}]},{"name":"FCT\/MCTES through national funds","award":["UIDB\/50008\/2020"],"award-info":[{"award-number":["UIDB\/50008\/2020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>The increasing complexity of managing modern database systems, particularly in terms of optimizing query performance for large datasets, presents significant challenges that traditional methods often fail to address. This paper proposes a comprehensive framework for integrating advanced machine learning (ML) models within the architecture of a database management system (DBMS), with a specific focus on PostgreSQL. Our approach leverages a combination of supervised and unsupervised learning techniques to predict query execution times, optimize performance, and dynamically manage workloads. Unlike existing solutions that address specific optimization tasks in isolation, our framework provides a unified platform that supports real-time model inference and automatic database configuration adjustments based on workload patterns. A key contribution of our work is the integration of ML capabilities directly into the DBMS engine, enabling seamless interaction between the ML models and the query optimization process. This integration allows for the automatic retraining of models and dynamic workload management, resulting in substantial improvements in both query response times and overall system throughput. Our evaluations using the Transaction Processing Performance Council Decision Support (TPC-DS) benchmark dataset at scale factors of 100 GB, 1 TB, and 10 TB demonstrate a reduction of up to 42% in query execution times and a 74% improvement in throughput compared with traditional approaches. Additionally, we address challenges such as potential conflicts in tuning recommendations and the performance overhead associated with ML integration, providing insights for future research directions. This study is motivated by the need for autonomous tuning mechanisms to manage large-scale, heterogeneous workloads while answering key research questions, such as the following: (1) How can machine learning models be integrated into a DBMS to improve query optimization and workload management? (2) What performance improvements can be achieved through dynamic configuration tuning based on real-time workload patterns? Our results suggest that the proposed framework significantly reduces the need for manual database administration while effectively adapting to evolving workloads, offering a robust solution for modern large-scale data environments.<\/jats:p>","DOI":"10.3390\/info15090574","type":"journal-article","created":{"date-parts":[[2024,9,18]],"date-time":"2024-09-18T09:49:19Z","timestamp":1726652959000},"page":"574","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Adaptive and Scalable Database Management with Machine Learning Integration: A PostgreSQL Case Study"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9011-0734","authenticated-orcid":false,"given":"Maryam","family":"Abbasi","sequence":"first","affiliation":[{"name":"Applied Research Institute, Polytechnic of Coimbra, 3045-093 Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0046-8685","authenticated-orcid":false,"given":"Marco V.","family":"Bernardo","sequence":"additional","affiliation":[{"name":"Instituto de Telecomunica\u00e7\u00f5es, 6201-001 Covilh\u00e3, Portugal"},{"name":"Polytechnic of Viseu, Department of Informatics, 3504-510 Viseu, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1745-8937","authenticated-orcid":false,"given":"Paulo","family":"V\u00e1z","sequence":"additional","affiliation":[{"name":"Polytechnic of Viseu, Department of Informatics, 3504-510 Viseu, Portugal"},{"name":"Research Center in Digital Services (CISeD), Polytechnic of Viseu, 3504-510 Viseu, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7285-8282","authenticated-orcid":false,"given":"Jos\u00e9","family":"Silva","sequence":"additional","affiliation":[{"name":"Polytechnic of Viseu, Department of Informatics, 3504-510 Viseu, Portugal"},{"name":"Research Center in Digital Services (CISeD), Polytechnic of Viseu, 3504-510 Viseu, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2118-1440","authenticated-orcid":false,"given":"Pedro","family":"Martins","sequence":"additional","affiliation":[{"name":"Polytechnic of Viseu, Department of Informatics, 3504-510 Viseu, Portugal"},{"name":"Research Center in Digital Services (CISeD), Polytechnic of Viseu, 3504-510 Viseu, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Oprea, S.V., B\u00e2ra, A., Marales, R.C., and Florescu, M.S. 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Syst."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Siddiqui, T., and Wu, W. (2023). ML-Powered Index Tuning: An Overview of Recent Progress and Open Challenges. arXiv.","DOI":"10.1145\/3641832.3641836"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1221","DOI":"10.14778\/3339490.3339503","article-title":"iBTune: Individualized Buffer Tuning for Large-scale Cloud Databases","volume":"12","author":"Tan","year":"2019","journal-title":"Proc. VLDB Endow."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1910","DOI":"10.14778\/3229863.3236222","article-title":"A Demonstration of the OtterTune Automatic Database Management System Tuning Service","volume":"11","author":"Zhang","year":"2018","journal-title":"Proc. VLDB Endow."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.14778\/3421424.3421425","article-title":"Benchmarking learned indexes","volume":"14","author":"Marcus","year":"2020","journal-title":"Proc. VLDB Endow."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/15\/9\/574\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:58:42Z","timestamp":1760111922000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/15\/9\/574"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,18]]},"references-count":14,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["info15090574"],"URL":"https:\/\/doi.org\/10.3390\/info15090574","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,18]]}}}