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Journal articles on the topic 'Database tuning'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Shi, Lei, Tian Li, Lin Wei, Yongcai Tao, Cuixia Li, and Yufei Gao. "FASTune: Towards Fast and Stable Database Tuning System with Reinforcement Learning." Electronics 12, no. 10 (2023): 2168. http://dx.doi.org/10.3390/electronics12102168.

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Configuration tuning is vital to achieving high performance for a database management system (DBMS). Recently, automatic tuning methods using Reinforcement Learning (RL) have been explored to find better configurations compared with database administrators (DBAs) and heuristics. However, existing RL-based methods still have several limitations: (1) Excessive overhead due to reliance on cloned databases; (2) trial-and-error strategy may produce dangerous configurations that lead to database failure; (3) lack the ability to handle dynamic workload. To address the above challenges, a fast and sta

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Bhattarai, Sushil, and Suman Thapaliya. "A Novel Approach to Self-tuning Database Systems Using Reinforcement Learning Techniques." NPRC Journal of Multidisciplinary Research 1, no. 7 (2024): 143–49. https://doi.org/10.3126/nprcjmr.v1i7.72480.

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The rapid evolution of data-intensive applications has intensified the need for efficient and adaptive database systems. Traditional database tuning methods, relying on manual interventions and rule-based optimizations, often fall short in handling dynamic workloads and complex parameter interdependencies. This paper introduces a novel approach to self-tuning database systems using reinforcement learning (RL) techniques, enabling databases to autonomously optimize configurations such as indexing strategies, memory allocation, and query execution plans. The proposed framework significantly enha

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Šušter, Ivan, and Tamara Ranisavljević. "Optimization of MySQL database." Journal of Process Management and New Technologies 11, no. 1-2 (2023): 141–51. http://dx.doi.org/10.5937/jouproman2301141q.

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The performance of MySQL, a well-known open-source relational database management system used in a variety of sectors, including e-commerce, finance, and healthcare, can be improved through the use of physical programming and data tuning. While data tuning involves refining the database to increase efficiency, physical programming involves optimizing the physical storage of data. This article gives a general introduction of MySQL and its architecture, looks at the many methods and tools used in physical programming and data tuning, and talks about the advantages of these techniques and how the

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MBAIOSSOUM, Bery Leouro, Ladjel BELLATRECHE, Narkoy BATOUMA, and Ahmat Mahamat DAOUDA. "Database Tuning from Relational Database to Big Data." International Journal of Engineering Trends and Technology 71, no. 11 (2023): 90–99. http://dx.doi.org/10.14445/22315381/ijett-v71i11p209.

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Zhang, Xinyi, Zhuo Chang, Yang Li, et al. "Facilitating database tuning with hyper-parameter optimization." Proceedings of the VLDB Endowment 15, no. 9 (2022): 1808–21. http://dx.doi.org/10.14778/3538598.3538604.

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Recently, using automatic configuration tuning to improve the performance of modern database management systems (DBMSs) has attracted increasing interest from the database community. This is embodied with a number of systems featuring advanced tuning capabilities being developed. However, it remains a challenge to select the best solution for database configuration tuning, considering the large body of algorithm choices. In addition, beyond the applications on database systems, we could find more potential algorithms designed for configuration tuning. To this end, this paper provides a compreh

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Šušter, Ivan, and Tamara Ranisavljević. "OPTIMIZATION OF MYSQL DATABASE." Journal of process management and new technologies 11, no. 1-2 (2023): 141–51. http://dx.doi.org/10.5937/jpmnt11-44471.

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The performance of MySQL, a well-known open-source relational database management system used in a variety of sectors, including e-commerce, finance, and healthcare, can be improved through the use of physical programming and data tuning. While data tuning involves refining the database to increase efficiency, physical programming involves optimizing the physical storage of data. This article gives a general introduction of MySQL and its architecture, looks at the many methods and tools used in physical programming and data tuning, and talks about the advantages of these techniques and how the

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V. "QUERY TUNING IN ORACLE DATABASE." Journal of Computer Science 8, no. 11 (2012): 1889–96. http://dx.doi.org/10.3844/jcssp.2012.1889.1896.

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Trummer, Immanuel. "The case for NLP-enhanced database tuning." Proceedings of the VLDB Endowment 14, no. 7 (2021): 1159–65. http://dx.doi.org/10.14778/3450980.3450984.

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A large body of knowledge on database tuning is available in the form of natural language text. We propose to leverage natural language processing (NLP) to make that knowledge accessible to automated tuning tools. We describe multiple avenues to exploit NLP for database tuning, and outline associated challenges and opportunities. As a proof of concept, we describe a simple prototype system that exploits recent NLP advances to mine tuning hints from Web documents. We show that mined tuning hints improve performance of MySQL and Postgres on TPC-H, compared to the default configuration.

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Ellavarasan Asokan. "The Role of AI in Predictive Database Performance Tuning." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 356–69. https://doi.org/10.32628/cseit25112365.

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The integration of artificial intelligence into database performance tuning marks a pivotal evolution in data management practices. As traditional manual approaches by Database Administrators give way to predictive and autonomous systems, organizations are experiencing transformative benefits across multiple dimensions of database operations. AI technologies now enable workload prediction, automated indexing, anomaly detection, and resource optimization that far exceed human capabilities in both accuracy and efficiency. While challenges exist in implementing these systems—particularly regardin

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Trummer, Immanuel. "Database Tuning using Natural Language Processing." ACM SIGMOD Record 50, no. 3 (2021): 27–28. http://dx.doi.org/10.1145/3503780.3503788.

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Introduction. We have seen significant advances in the state of the art in natural language processing (NLP) over the past few years [20]. These advances have been driven by new neural network architectures, in particular the Transformer model [19], as well as the successful application of transfer learning approaches to NLP [13]. Typically, training for specific NLP tasks starts from large language models that have been pre-trained on generic tasks (e.g., predicting obfuscated words in text [5]) for which large amounts of training data are available. Using such models as a starting point redu

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OHTA, Jun, and Shigeru YAMAMOTO. "Database-Driven Tuning of PID Controllers." Transactions of the Society of Instrument and Control Engineers 40, no. 6 (2004): 664–69. http://dx.doi.org/10.9746/sicetr1965.40.664.

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Duan, Songyun, Vamsidhar Thummala, and Shivnath Babu. "Tuning database configuration parameters with iTuned." Proceedings of the VLDB Endowment 2, no. 1 (2009): 1246–57. http://dx.doi.org/10.14778/1687627.1687767.

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Gufron, Dian Muhammad, Muhammad Ramadhon, and Samidi Samidi. "Komparasi Database Performance Tuning Melalui Metode Object Relation Mapping pada SQL Server." Jurnal Pendidikan dan Teknologi Indonesia 4, no. 12 (2024): 709–12. https://doi.org/10.52436/1.jpti.537.

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Pada proses database performance tuning terdapat beberapa permasalahan apabila diterapkan pada sistem berbasis object relational mapping (ORM) yang menyebabkan efektivitas peningkatan kinerja tidak seefektif sebagaimana diterapkan pada relational database konvensional. Metode indexing dan table partitioning akan diterapkan dalam proses database performance tuning untuk mengukur efektivitas peningkatan kinerja database. Hasil penelitian menunjukkan bahwa database performance tuning dengan kombinasi metode indexing dan table partitioning berhasil meningkatkan kinerja database dengan peningkatan

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Nzenwata, Uchenna Jeremiah, Goodness Oluwamayokun Opateye, Noze-Otote Aisosa, et al. "Autonomous Database Systems – A Systematic Review of Self-Healing and Self-Tuning Database Systems." Asian Journal of Research in Computer Science 18, no. 7 (2025): 77–87. https://doi.org/10.9734/ajrcos/2025/v18i7721.

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Problem Statement: Autonomous database systems represent a significant change in the management of databases, utilizing Machine Learning (ML) and Artificial Intelligence (AI) in order to carry out self-healing and self-tuning with minimal human intervention. Objectives: This systematic review investigates the defining characteristics, AI/ML techniques, challenges and the future trends of self-healing and self-tuning autonomous databases. Methodology: The research questions were answered integrating findings from 35 current literatures between 2020 and 2025. These literatures were obtained from

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Barbosa, Diogo, Le Gruenwald, Laurent D’Orazio, and Jorge Bernardino. "QRLIT: Quantum Reinforcement Learning for Database Index Tuning." Future Internet 16, no. 12 (2024): 439. http://dx.doi.org/10.3390/fi16120439.

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Selecting indexes capable of reducing the cost of query processing in database systems is a challenging task, especially in large-scale applications. Quantum computing has been investigated with promising results in areas related to database management, such as query optimization, transaction scheduling, and index tuning. Promising results have also been seen when reinforcement learning is applied for database tuning in classical computing. However, there is no existing research with implementation details and experiment results for index tuning that takes advantage of both quantum computing a

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Muhammad, Qasim Memon, He Jingsha, Memon Aasma, Gulzar Rana Khurram, and Salman Pathan Muhammad. "Query Processing for Time Efficient Data Retrieval." Indonesian Journal of Electrical Engineering and Computer Science 9, no. 3 (2018): 784–88. https://doi.org/10.11591/ijeecs.v9.i3.pp784-788.

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In database management system (DBMS) retrieving data through structure query language is an essential aspect to find better execution plan for performance. In this paper, we incorporated database objects to optimize query execution time and its cost by vanishing poorly SQL statements. We proposed a method of evolving and inserting database constraints as database objects embedded with queries either to add them for the sake of transactions required by user to detect those queries for the betterment of performance. We took analysis on several databases while processing queries itself and assimi

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Zhang, Xinyi, Hong Wu, Yang Li, et al. "An Efficient Transfer Learning Based Configuration Adviser for Database Tuning." Proceedings of the VLDB Endowment 17, no. 3 (2023): 539–52. http://dx.doi.org/10.14778/3632093.3632114.

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In recent years, a wide spectrum of database tuning systems have emerged to automatically optimize database performance. However, these systems require a significant number of workload runs to deliver a satisfactory level of database performance, which is time-consuming and resource-intensive. While many attempts have been made to address this issue by using advanced search optimizers, empirical studies have shown that no single optimizer can dominate the rest across tuning tasks with different characteristics. Choosing an inferior optimizer may significantly increase the tuning cost. Unfortun

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Oh, Seung-Jin, Jong-Hyeok Park, and Sang-Won Lee. "Database Tuning Techniques to Mitigate SSD-internal Interference among Multi-tenant Databases." Journal of KIISE 49, no. 5 (2022): 388–96. http://dx.doi.org/10.5626/jok.2022.49.5.388.

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Raihan Siddik, Muhammad, Mhd Arief Hasan, Andika Fajar Kesuma, Nurmala Sari, Shania Dwi Putri, and Qurrotul Uyun Harahap. "IMPLEMENTASI QUERY TUNING UNTUK PENINGKATAN PERFORMA PADA DATABASE BARANG MINI MARKET NAN." JATI (Jurnal Mahasiswa Teknik Informatika) 9, no. 2 (2025): 3183–87. https://doi.org/10.36040/jati.v9i2.13217.

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Query tuning merupakan suatu langkah optimasi performa database pada SQL Server. Query Tuning ini bertujuan untuk meningkatkan efisiensi eksekusi query dengan meminimalkan penggunaan sumber daya seperti waktu proses dan konsumsi memori. Dalam pengoperasiannya, Query Tuning melibatkan analisis query plan, indeks, serta penggunaan teknik-teknik seperti pembaruan statistik, restrukturisasi query, dan pengelolaan indeks yang tepat. Selain itu, fitur bawaan SQL Server seperti Database Engine Tuning Advisor dan Query Store memberikan panduan praktis dalam mengidentifikasi bottleneck performa. Dengan

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B., Rajan. "A PROACTIVE APPROACH FOR DATABASE PERFORMANCE TUNING." International Journal of Advanced Research 10, no. 01 (2022): 426–38. http://dx.doi.org/10.21474/ijar01/14058.

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The Oracle database is a trendsetter and highly tunable software product. Its very flexible to allow you to make small adjustments that affect the database performance. By tuning you can tailor its performance to best meet your needs. Performance tuning cannot be performed best after a system is put into production. To achieve performance targets of response time, and throughput, one must proactively analyze database design, implementation and tune ahead in the life cycle.

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Shahwan, Younis Ali, and Maseeh Hajar. "AI-Powered Database Management: Predictive Analytics for Performance Tuning." Engineering and Technology Journal 10, no. 05 (2025): 5100–5112. https://doi.org/10.5281/zenodo.15472012.

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As data volumes and query complexities grow in modern applications, ensuring optimal database performance has become increasingly challenging. Traditional manual tuning approaches are reactive, time-consuming, and often lack adaptability to dynamic workloads. This paper explores the integration of Artificial Intelligence (AI) and predictive analytics into database management systems (DBMS) for proactive performance tuning. By leveraging machine learning models, such as regression analysis and anomaly detection, AI-powered systems can forecast performance degradation, recommend tuning actions,

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Shasha, Dennis, Philippe Bonnet, and Nancy Hartline Bercich. "Database tuning principles, experiments, and troubleshooting techniques." ACM SIGMOD Record 33, no. 2 (2004): 115–16. http://dx.doi.org/10.1145/1024694.1024720.

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23

Colley, Derek, and Clare Stanier. "Identifying New Directions in Database Performance Tuning." Procedia Computer Science 121 (2017): 260–65. http://dx.doi.org/10.1016/j.procs.2017.11.036.

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Lao, Jiale, Yibo Wang, Yufei Li, et al. "GPTuner: An LLM-Based Database Tuning System." ACM SIGMOD Record 54, no. 1 (2025): 101–10. https://doi.org/10.1145/3733620.3733641.

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Selecting appropriate values for the configurable knobs of Database Management Systems (DBMS) is essential to improve performance. But because the complexity of this task has surpassed the abilities of even the best human experts, the database community turns to machine learning (ML)- based automatic tuning systems. However, these systems still incur significant tuning costs or only yield suboptimal performance, attributable to their overly high reliance on black-box optimization and the lack of integration with domain knowledge, such as DBMS manuals and forum discussions. Hence, we propose GP

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Indrajani, Indrajani. "Analisis dan Penerapan Metode Tuning pada Basis Data Funding." ComTech: Computer, Mathematics and Engineering Applications 6, no. 1 (2015): 143. http://dx.doi.org/10.21512/comtech.v6i1.2299.

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The purpose of this research is to analyze, design, and implement tuning for the funding database, which include data mart processing that will be used in the formation of analytical reports. The research method used is literature study from a variety of journals, books, e-books, and articles on the internet. Fact findingtechniques are also done by analyzing, collecting, and examining the documents, interviews, and observations. Other the research methods are also used to analyze and design database such as SQL tuning, Partitioning, and Indexing. The results obtained from this research is an i

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Kanellis, Konstantinos, Cong Ding, Brian Kroth, Andreas Müller, Carlo Curino, and Shivaram Venkataraman. "LlamaTune." Proceedings of the VLDB Endowment 15, no. 11 (2022): 2953–65. http://dx.doi.org/10.14778/3551793.3551844.

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Tuning a database system to achieve optimal performance on a given workload is a long-standing problem in the database community. A number of recent works have leveraged ML-based approaches to guide the sampling of large parameter spaces (hundreds of tuning knobs) in search for high performance configurations. Looking at Microsoft production services operating millions of databases, sample efficiency emerged as a crucial requirement to use tuners on diverse workloads. This motivates our investigation in LlamaTune, a tuner design that leverages domain knowledge to improve the sample efficiency

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Huynh, Andy, Harshal A. Chaudhari, Evimaria Terzi, and Manos Athanassoulis. "Endure." Proceedings of the VLDB Endowment 15, no. 8 (2022): 1605–18. http://dx.doi.org/10.14778/3529337.3529345.

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Log-Structured Merge trees (LSM trees) are increasingly used as the storage engines behind several data systems, frequently deployed in the cloud. Similar to other database architectures, LSM trees consider information about the expected workload (e.g., reads vs. writes, point vs. range queries) to optimize their performance via tuning. However, operating in a shared infrastructure like the cloud comes with workload uncertainty due to the fast-evolving nature of modern applications. Systems with static tuning discount the variability of such hybrid workloads and hence provide an inconsistent a

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Syed, Ashraf. "Performance Analysis of Oracle APEX Applications in Multi-Tenant Cloud Environments." International Scientific Journal of Engineering and Management 04, no. 04 (2025): 1–9. https://doi.org/10.55041/isjem03278.

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The rapid adoption of multi-tenant cloud environments has transformed how enterprises deploy database- driven applications, offering cost efficiency and streamlined management. Oracle Application Express (APEX), a leading low-code platform integrated with Oracle Database, is increasingly utilized in such setups to develop scalable web applications. This paper investigates the performance of APEX applications within multi-tenant cloud environments, leveraging Oracle Database Multitenant architecture. We configured a container database (CDB) hosting multiple pluggable databases (PDBs), each runn

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Van Aken, Dana, Dongsheng Yang, Sebastien Brillard, et al. "An inquiry into machine learning-based automatic configuration tuning services on real-world database management systems." Proceedings of the VLDB Endowment 14, no. 7 (2021): 1241–53. http://dx.doi.org/10.14778/3450980.3450992.

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Modern database management systems (DBMS) expose dozens of configurable knobs that control their runtime behavior. Setting these knobs correctly for an application's workload can improve the performance and efficiency of the DBMS. But because of their complexity, tuning a DBMS often requires considerable effort from experienced database administrators (DBAs). Recent work on automated tuning methods using machine learning (ML) have shown to achieve better performance compared with expert DBAs. These ML-based methods, however, were evaluated on synthetic workloads with limited tuning opportuniti

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Li, Zhongliang, Yaofeng Tu, and Zongmin Ma. "A Sample-Aware Database Tuning System With Deep Reinforcement Learning." Journal of Database Management 35, no. 1 (2023): 1–25. http://dx.doi.org/10.4018/jdm.333519.

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Based on the relationship between client load and overall system performance, the authors propose a sample-aware deep deterministic policy gradient model. Specifically, they improve sample quality by filtering out sample noise caused by the fluctuations of client load, which accelerates the model convergence speed of the intelligent tuning system and improves the tuning effect. Also, the hardware resources and client load consumed by the database in the working process are added to the model for training. This can enhance the performance characterization ability of the model and improve the re

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Martani, Marlene, Hanny Juwitasary, and Arya Nata Gani Putra. "Analisis Alat Bantu Tuning Fisikal Basis Data pada Sql Server 2008." ComTech: Computer, Mathematics and Engineering Applications 5, no. 1 (2014): 334. http://dx.doi.org/10.21512/comtech.v5i1.2628.

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Nowadays every company has been faced with a business competition that requires the company to survive and be superior to its competitors. One strategy used by many companies is to use information technology to run their business processes. The use of information technology would require a storage which commonly referred to as a database to store and process data into useful information for the company. However, it was found that the greater the amount of data in the database, then the speed of the resulting process will decrease because the time needed to access the data will be much longer.

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Rodd, S. F., and Umakant P. Kulkarni. "Adaptive self-tuning techniques for performance tuning of database systems: a fuzzy-based approach with tuning moderation." Soft Computing 19, no. 7 (2014): 2039–45. http://dx.doi.org/10.1007/s00500-014-1389-3.

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Peck Lee, Sai, and Dzemal Zildzic. "Oracle Database Workload Performance Measurement and Tuning Toolkit." Issues in Informing Science and Information Technology 3 (2006): 371–81. http://dx.doi.org/10.28945/898.

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Mózsi, Krisztián, and Attila Kiss. "A Session-based Approach to Autonomous Database Tuning." Acta Polytechnica Hungarica 17, no. 1 (2020): 7–24. http://dx.doi.org/10.12700/aph.17.1.2020.1.1.

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Hanafy, A. A. R., and M. M. F. Sakr. "Database-Aided Tuning for the BOF Static Model." IFAC Proceedings Volumes 20, no. 9 (1987): 537–42. http://dx.doi.org/10.1016/s1474-6670(17)55763-8.

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Cesarini, Francesca, Michele Missikoff, and Giovanni Soda. "An expert system approach for database application tuning." Data & Knowledge Engineering 8, no. 1 (1992): 35–55. http://dx.doi.org/10.1016/0169-023x(92)90004-u.

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Zhao, De Yu. "Research on Improving Oracle Query Performance in MES." Applied Mechanics and Materials 201-202 (October 2012): 39–42. http://dx.doi.org/10.4028/www.scientific.net/amm.201-202.39.

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The tuning for Oracle database system is vital to the normal running of the whole system, but it is a complicated work. SQL statement tuning is a very critical aspect of database performance tuning. It is an inherently complex activity requiring a high level of expertise in several domains: query optimization, to improve the execution plan selected by the query optimizer, access design to identify missing access structures and SQL design to restructure and simplify the text of a badly written SQL statement. In this paper, the author analyzes the execution procedure of oracle optimizer, and res

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Chavali, LN, Lal Hmingliana, Brindha Senthil Kumar, and P. Lakshmi Narayana. "An Approach to Fine Tuning Database Performance in Application Software." Science & Technology Journal 9, no. 1 (2021): 10–13. http://dx.doi.org/10.22232/stj.2021.09.01.02.

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Database tuning is crucial step to enhance the performance of the application software. There are many tools available in Microsoft to evaluate the performance of the stored procedures and identify them. This paper presents a comparative performance of actual and tuned sample stored procedures in SQL Server 2008 R2 of Application software (Microsoft). The results showed there is a marginal gain in the efficiency after the database tuning.

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Ab Razak, Nur Aishah, and Shahnorbanun Sahran. "Lightweight Micro-Expression Recognition on Composite Database." Applied Sciences 13, no. 3 (2023): 1846. http://dx.doi.org/10.3390/app13031846.

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The potential of leveraging micro-expression in various areas such as security, health care and education has intensified interests in this area. Unlike facial expression, micro-expression is subtle and occurs rapidly, making it imperceptible. Micro-expression recognition (MER) on composite dataset following Micro-Expression Grand Challenge 2019 protocol is an ongoing research area with challenges stemming from demographic variety of the samples as well as small and imbalanced dataset. However, most micro-expression recognition (MER) approaches today are complex and require computationally exp

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Giannakouris, Victor, та Immanuel Trummer. "λ-Tune: Harnessing Large Language Models for Automated Database System Tuning". Proceedings of the ACM on Management of Data 3, № 1 (2025): 1–26. https://doi.org/10.1145/3709652.

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We introduce λ-Tune, a framework that leverages Large Language Models (LLMs) for automated database system tuning. The design of λ-Tune is motivated by the capabilities of the latest generation of LLMs. Different from prior work, leveraging LLMs to extract tuning hints for single parameters, λ-Tune generates entire configuration scripts, based on a large input document, describing the tuning context. λ-Tune generates alternative configurations, using a principled approach to identify the best configuration, out of a small set of candidates. In doing so, it minimizes reconfiguration overheads a

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Oluwafemi Oloruntoba. "AI-Driven autonomous database management: Self-tuning, predictive query optimization, and intelligent indexing in enterprise it environments." World Journal of Advanced Research and Reviews 25, no. 2 (2025): 1558–80. https://doi.org/10.30574/wjarr.2025.25.2.0534.

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The rapid growth of enterprise data and the increasing complexity of modern database systems have necessitated a shift from traditional manual database management to autonomous, AI-driven solutions. AI-driven autonomous database management systems (ADBMS) leverage machine learning, predictive analytics, and automation to optimize database performance, reduce administrative overhead, and enhance scalability in enterprise IT environments. Traditional database management approaches often suffer from inefficiencies related to query performance, indexing, workload tuning, and anomaly detection, lea

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Samidi and Hariyanto. "Performance Tuning Oracle 11g Database Melalui Inisial Paramater, Structure Database dan SQL Tuning. Studi Pada ERP SISFORBUN Dana Pensiun Perkebunan (DAPENBUN)." Techno.Com 22, no. 2 (2023): 400–408. http://dx.doi.org/10.33633/tc.v22i2.7831.

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Dana Pensiun Perkebunan (DAPENBUN) sebagai pengelola manfaat pensiun bagi karyawan PTPN seluruh Indonesia beserta lembaga afiliasi dengan jumlah peserta per 31 Desember 2021 sebanyak 284.934 orang. Aplikasi SISFORBUN ini digunakan untuk memproses manfaat pensiun bagi seluruh peserta. Aplikasi berbasis web ini menggunakan database Oracle 11g dan sudah digunakan sejak tahun 2013, dengan seiring berjalanya waktu perkembangan data semakin banyak dengan jumlah record terbesar dalam satu table sebesar 26.696.667 record data, sehingga performa proses data semakin menurun. Atas dasar permasalahan ters

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Ragab, Rasha, and Abdulrahman Altahhan. "Fine-Tuning of Small/Medium LLMs for Business QA on Structured Data." International Journal on Natural Language Computing 13, no. 3 (2024): 01–17. http://dx.doi.org/10.5121/ijnlc.2024.13301.

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Enabling business users to directly query their data sources is a significant advantage for organisations. The majority of enterprise data is housed within databases, requiring extensive procedures that involve intermediary layers for reporting and its related customization. The concept of enabling natural language queries, where a chatbot can interpret user questions into database queries and promptly return results, holds promise for expediting decision-making and enhancing business responsiveness. This approach empowers experienced users to swiftly obtain data-driven insights. The integrati

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International, Journal on Natural Language Computing (IJNLC). "Fine-Tuning of Small/Medium LLMs for Business QA on Structured Data." International Journal on Natural Language Computing (IJNLC) 13, no. 3 (2024): 1–17. https://doi.org/10.5121/ijnlc.2024.13301.

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Enabling business users to directly query their data sources is a significant advantage for organisations. The majority of enterprise data is housed within databases, requiring extensive procedures that involve intermediary layers for reporting and its related customization. The concept of enabling natural language queries, where a chatbot can interpret user questions into database queries and promptly return results, holds promise for expediting decision-making and enhancing business responsiveness. This approach empowers experienced users to swiftly obtain data-driven insights. The integrati

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Santosh Jaini. "Autonomous Databases: Leveraging Machine Learning and Neural Networks for Predictive Query Optimization, Self-Tuning, and Index Optimization in Multi-RDBMS Systems." International Journal for Research Publication and Seminar 13, no. 2 (2022): 378–86. http://dx.doi.org/10.36676/jrps.v13.i2.1600.

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Autonomous databases are the new fad in modern database systems. The database systems are managed by machine learning and neural networks for query prediction, self-tuning, and self-indexing. These systems decrease intervention in multi-relational database management systems (multi-RDBMS). This paper analyses the relevance of ML and NN in optimizing the queries and automating the working of databases. Either simulation results of the tested benchmark queries or real-time use cases show the extent of the query processing speed increase and its accuracy. However, problems like implementing these

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Desai, Janvi. "Self-Optimizing Database Architecture." International Journal for Research in Applied Science and Engineering Technology 9, no. 11 (2021): 1675–78. http://dx.doi.org/10.22214/ijraset.2021.39071.

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Abstract: Over the most recent decades, analysts and database service providers have fabricated devices to help DBAs (Database Administrators) in various parts of framework tuning and the actual design of the database. Most of this past work, regardless, is fragmented on the grounds that it expects people to come up with an official agreement or judgement about any modifications to the data in the database and fix issues after they happen rather than preventing such cases from taking place or adjusting to these changes automatically. What is required for a really "self-driving" database manage

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Bianchi, Alexander, Andrew Chai, Vincent Corvinelli, Parke Godfrey, Jarek Szlichta, and Calisto Zuzarte. "Db2une: Tuning Under Pressure via Deep Learning." Proceedings of the VLDB Endowment 17, no. 12 (2024): 3855–68. http://dx.doi.org/10.14778/3685800.3685811.

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Modern database systems including IBM Db2 have numerous parameters, "knobs," that require precise configuration to achieve optimal workload performance. Even for experts, manually "tuning" these knobs is a challenging process. We present Db2une, an automatic query-aware tuning system that leverages deep learning to maximize performance while minimizing resource usage. Via a specialized transformer-based query-embedding pipeline we name QBERT, Db2une generates context-aware representations of query workloads to feed as input to a stability-oriented, on-policy deep reinforcement learning model.

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Raharjo, Yosua Dwi. "Performance Tuning for Optimal Backup Process on Database Server." International Journal of Advanced Trends in Computer Science and Engineering 9, no. 4 (2020): 6591–97. http://dx.doi.org/10.30534/ijatcse/2020/349942020.

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Lopes, Stéphane, Jean-Marc Petit, and Farouk Toumani. "Discovering interesting inclusion dependencies: application to logical database tuning." Information Systems 27, no. 1 (2002): 1–19. http://dx.doi.org/10.1016/s0306-4379(01)00027-8.

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Chen, Andrew N. K. "Robust optimization for performance tuning of modern database systems." European Journal of Operational Research 171, no. 2 (2006): 412–29. http://dx.doi.org/10.1016/j.ejor.2004.09.024.

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