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Gotowe bibliografie tematyczne / Database Performance Tuning / Artykuły w czasopismach

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Artykuły w czasopismach na temat „Database Performance Tuning”

Autor: Grafiati

Data publikacji: 2 czerwca 2025

Data aktualizacji: 31 lipca 2025

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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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AzraJabeen, Mohamed Ali. "SQL Server Optimization-Best Practices for Maximizing Performance." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 8, no. 4 (2020): 1–10. https://doi.org/10.5281/zenodo.14535769.

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This paper explores the best practices for SQL Server optimization, offering a comprehensive guide to enhance the performance of database systems.In the data-driven world of today, sustaining high efficiency and responsiveness requires that SQL Server databases operate at their best.By addressing key aspects such as query tuning, indexing strategies, and resource management, it presents effective techniques to minimize latency and improve execution speed. It also highlights the importance of proper configuration, efficient use of memory, and effective database maintenance practices. Through th

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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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F.Rodd, S., Umakant P. Kulkarni, and A. R. Yardi. "Fuzzy Controlled Architecture for Performance Tuning of Database Management System." International Journal of Computer Applications 39, no. 5 (2012): 1–5. http://dx.doi.org/10.5120/4813-7050.

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Irfan, Rahmatul, and Cannavaro Yogi Pratama. "Improvement of Performance E-Learning Moodle Service in Vocational High School with Optimization of Web Server and Database Server." Elinvo (Electronics, Informatics, and Vocational Education) 9, no. 1 (2024): 52–63. http://dx.doi.org/10.21831/elinvo.v9i1.42878.

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Since COVID-19, online learning has taken on an increasingly prominent role. Moodle is a popular online learning platform. Its implementation necessitates various support components, including a web server and an e-learning database server. The purpose of this study is to examine the optimization of web servers and database servers when there are multiple large connections at SMK N 2 Depok's E-learning. A pre-experimental one-group pretest-posttest design was used to conduct experimental research before and after optimization. Response time and throughput performance variables are used to asse

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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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Vishnupriya, S. Devarajulu. "Key Solutions to Optimize Database SQL Queries." Journal of Scientific and Engineering Research 6, no. 12 (2019): 311–14. https://doi.org/10.5281/zenodo.13753398.

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Optimizing SQL Queries is crucial for enhancing the performance and efficiency of database-driven applications. This article explores key solutions to the performance issues in SQL queries, with code samples and detailed explanations. Best practices such as using indexes, avoiding unnecessary columns in SELECT statements, using schema names with object names, and optimizing joins and subqueries and other solutions are discussed. By following these optimization techniques, developers can provide more efficient database with improved application performance.

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

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<p class="TTPAbstract">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 process

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Murali Natti. "Optimizing oracle database performance: Reducing row migration and enhancing access efficiency by tuning PCT Free and PCT Used." International Journal of Science and Research Archive 14, no. 2 (2025): 124–26. https://doi.org/10.30574/ijsra.2025.14.2.0577.

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In Oracle databases, efficient data storage and retrieval are paramount for maintaining high performance, especially in systems with large datasets and frequent updates. A critical aspect of database performance is the management of data storage within blocks, which directly impacts how rows are stored and accessed. Oracle uses parameters such as PCT Free and PCT Used to control space allocation and manage how data is stored within database blocks. Improperly configured settings for these parameters can lead to significant performance degradation, especially in terms of row migration. Row migr

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Murali Natti. "Optimizing oracle database performance: Reducing row migration and enhancing access efficiency by tuning PCT Free and PCT Used." International Journal of Science and Research Archive 12, no. 2 (2024): 3014–16. https://doi.org/10.30574/ijsra.2024.12.2.0577.

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In Oracle databases, efficient data storage and retrieval are paramount for maintaining high performance, especially in systems with large datasets and frequent updates. A critical aspect of database performance is the management of data storage within blocks, which directly impacts how rows are stored and accessed. Oracle uses parameters such as PCT Free and PCT Used to control space allocation and manage how data is stored within database blocks. Improperly configured settings for these parameters can lead to significant performance degradation, especially in terms of row migration. Row migr

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Rodd, S. F., U. P. Kulkarni, and A. R. Yardi. "Adaptive neuro-fuzzy technique for performance tuning of database management systems." Evolving Systems 4, no. 2 (2013): 133–43. http://dx.doi.org/10.1007/s12530-013-9072-y.

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Bharat Kumar Dokka and Er Vikhyat Gupta. "Cloud Database Migration and Modernization." International Journal for Research Publication and Seminar 16, no. 1 (2025): 326–43. https://doi.org/10.36676/jrps.v16.i1.140.

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Cloud database migration and modernization are now a priority for organizations that aim to harness the flexibility, scalability, and cost savings of cloud technology. In spite of extensive research on the topic during 2015-2024, issues still exist in migrating legacy databases in an efficient manner, with security, performance optimization, and regulatory compliance being the major issues. The research has discussed various migration approaches such as lift-and-shift, re-platforming, and re-architecting each with a set of trade-offs. Although the cloud-native databases provide elasticity and

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Richly, Keven, Rainer Schlosser, and Martin Boissier. "Budget-Conscious Fine-Grained Configuration Optimization for Spatio-Temporal Applications." Proceedings of the VLDB Endowment 15, no. 13 (2022): 4079–92. http://dx.doi.org/10.14778/3565838.3565858.

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Based on the performance requirements of modern spatio-temporal data mining applications, in-memory database systems are often used to store and process the data. To efficiently utilize the scarce DRAM capacities, modern database systems support various tuning possibilities to reduce the memory footprint (e.g., data compression) or increase performance (e.g., additional indexes). However, the selection of cost and performance balancing configurations is challenging due to the vast number of possible setups consisting of mutually dependent individual decisions. In this paper, we introduce a nov

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Vilaplana, Jordi, Francesc Solsona, Ivan Teixido, et al. "Database Constraints Applied to Metabolic Pathway Reconstruction Tools." Scientific World Journal 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/967294.

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Our group developed two biological applications,Biblio-MetReSandHomol-MetReS, accessing the same database of organisms with annotated genes.Biblio-MetReSis a data-mining application that facilitates the reconstruction of molecular networks based on automated text-mining analysis of published scientific literature.Homol-MetReSallows functional (re)annotation of proteomes, to properly identify both the individual proteins involved in the process(es) of interest and their function. It also enables the sets of proteins involved in the process(es) in different organisms to be compared directly. The

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Sai Reddy Anugu. "Comprehensive tuning guide: IBM MDM, PME algorithm, services, and configurations." International Journal of Science and Research Archive 13, no. 2 (2024): 043–47. http://dx.doi.org/10.30574/ijsra.2024.13.2.2097.

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The research paper provides comprehensive guidance on tuning specifics to maximize the efficiency of IBM MDM, particularly in high-performance environments. It addresses several advanced topics and tuning guidelines, focusing on the architectural framework and the details required for sustained performance. Including the recommendations to Handle Long-Chained Duplicates in Suspect Duplicate Processing (SDP), Minimize Deadlocks During Duplicate Processing, and Advanced Database Configurations for High-Performance WebSphere Application Server (WAS) Java Virtual Machine (JVM) and Cache Optimizati

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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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Kazheen, S. . Muhammad, and Maseeh Yasin Hajar. "Scalable Database Solutions in the Cloud Era: Challenges and Best Practices." Engineering and Technology Journal 10, no. 05 (2025): 5192–204. https://doi.org/10.5281/zenodo.15532508.

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Cloud computing has revolutionized data management, creating demand for highly scalable and adaptive database systems. Traditional architecture has given way to cloud-native databases that offer elasticity, modularity, and real-time responsiveness. This paper reviews modern approaches to building scalable cloud databases, highlighting critical challenges and emerging solutions. Key advancements include microservices-based architecture and intelligent tuning systems like CDBTune and HUNTER, which use AI to optimize performance under dynamic workloads. Security is addressed through homomorphic e

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Lao, Jiale, Yibo Wang, Yufei Li, et al. "GPTuner: A Manual-Reading Database Tuning System via GPT-Guided Bayesian Optimization." Proceedings of the VLDB Endowment 17, no. 8 (2024): 1939–52. http://dx.doi.org/10.14778/3659437.3659449.

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Modern database management systems (DBMS) expose hundreds of configurable knobs to control system behaviours. Determining the appropriate values for these knobs to improve DBMS performance is a long-standing problem in the database community. As there is an increasing number of knobs to tune and each knob could be in continuous or categorical values, manual tuning becomes impractical. Recently, automatic tuning systems using machine learning methods have shown great potentials. However, existing approaches still incur significant tuning costs or only yield sub-optimal performance. This is beca

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Aljwari, Fatima Khalil. "External vs. Internal: An Essay on Machine Learning Agents for Autonomous Database Management Systems." European Journal of Computer Science and Information Technology 10, no. 5 (2022): 24–31. http://dx.doi.org/10.37745/ejcsit.2013/vol10n52431.

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There are many possible ways to configure database management systems (DBMSs) have challenging to manage and set.The problem increased in large-scale deployments with thousands or millions of individual DBMS that each have their setting requirements. Recent research has explored using machine learning-based (ML) agents to overcome this problem's automated tuning of DBMSs. These agents extract performance metrics and behavioral information from the DBMS and then train models with this data to select tuning actions that they predict will have the most benefit. This paper discusses two engineerin

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Madathala, Harikrishna, Balaji Barmavat, and Srinivasa Rao Thumala. "Performance Optimization of SAP HANA using AI-based Workload Predictions." International Journal of Innovative Research in Science,Engineering and Technology 12, no. 12 (2023): 15315–26. http://dx.doi.org/10.15680/ijirset.2023.1212047.

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This research paper explores the application of artificial intelligence (AI) techniques for optimizing the performance of SAP HANA databases through predictive workload analysis and dynamic resource allocation. SAP HANA, as an in-memory, column-oriented relational database management system, presents unique challenges in performance tuning due to its complex architecture and diverse workload patterns. We propose a novel framework that leverages machine learning models to predict future workloads and intelligently allocate resources in real-time. Our approach demonstrates significant improvemen

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BATORY, DON, and DEVANG VASAVADA. "SOFTWARE COMPONENTS FOR OBJECT-ORIENTED DATABASE SYSTEMS." International Journal of Software Engineering and Knowledge Engineering 03, no. 02 (1993): 165–92. http://dx.doi.org/10.1142/s0218194093000082.

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Genesis is a software system generator for database management systems that relies exclusively on as-is large scale component reuse. We review the general model of software components on which Genesis is based and discuss component libraries for relational database systems that we have implemented. We then explain how we have evolved Genesis and its libraries to be able to synthesize object-oriented database systems. We study a subproblem of creating “self-tuning” software systems by examining the performance of selected components for object-oriented database systems.

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