Relevant bibliographies by topics / Database Performance Tuning Query Tuning

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Database Performance Tuning Query Tuning'

Author: Grafiati
Published: 5 June 2025
Last updated: 15 July 2025

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Journal articles on the topic "Database Performance Tuning Query Tuning"

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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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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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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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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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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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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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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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Abbasi, Maryam, Marco V. Bernardo, Paulo Váz, José Silva, and Pedro Martins. "Adaptive and Scalable Database Management with Machine Learning Integration: A PostgreSQL Case Study." Information 15, no. 9 (2024): 574. http://dx.doi.org/10.3390/info15090574.

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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. Unli
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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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Dissertations / Theses on the topic "Database Performance Tuning Query Tuning"

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Burrell, Tiffany. "System Identification in Automatic Database Memory Tuning." Scholar Commons, 2010. https://scholarcommons.usf.edu/etd/1583.

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Databases are very complex systems that require database system administrators to perform system tuning in order to achieve optimal performance. Memory tuning is vital to the performance of a database system because when the database workload exceeds its memory capacity, the results of the queries running on a system are delayed and can cause substantial user dissatisfaction. In order to solve this problem, this thesis presents a platform modeled after a closed control feedback loop to control the level of multi-query processing. Utilizing this platform provides two key assets. First, the syst
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Vairaitė, Rūta. "Duomenų filtravimo ir atrankos sprendimų analizė." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2008. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2008~D_20080710_114048-38741.

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Esant dideliems saugomų duomenų kiekiams, yra svarbus našus jų apdorojimas, taigi, vartotojams reikia vis didesnio duomenų bazių našumo. Šiame darbe sprendžiama problema, kaip paskatinti duomenų bazes veikti greičiau, kai duomenų bazių lentelės turi labai daug įrašų. Todėl skiriamas dėmesys duomenų bazių spartos derinimui, ar duomenų bazių spartos optimizavimui. Išnagrinėjus duomenų bazių esamus spartinimo metodus ir priežastis, kurios mažina našumą, yra siūlomas metodas, kuris leidžia sparčiau apdoroti ir filtruoti duomenis bei greičiau pateikti vartotojui užklausos rezultatą. Darbui atlikti
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Paulíček, Martin. "Ladění výkonnosti databází." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2011. http://www.nusl.cz/ntk/nusl-237021.

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The objective of this thesis was to study problems of an insufficient database processing performance and possibilities how to improve the performance with database configuration file optimizations, more powerful hardware and parallel processing. The master thesis contains a description of relational databases, storage media and different forms of parallelism with its use in database systems. There is a description of the developed software for testing database performance. The program was used for testing several database configuration files, various hardware, different database systems (Post
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Mikulka, David. "Pokročilý nástroj pro monitorování Oracle Databáze." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2010. http://www.nusl.cz/ntk/nusl-237133.

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This master's thesis describes possibilities of Oracle Database 10g and 11g monitoring. It let the reader know about practical tools for monitoring and describes the database's internal catalogs preserving statistics and the information about running database instances within history. Next, it describes design of an Oracle database monitoring tool, describtion of its implementation and at the end its evaluation and comparison with other similar applications.
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Chen, Jian-Hua, and 陳建驊. "Database Clustering and Performance Tuning." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/69840222781538754919.

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碩士<br>明志科技大學<br>電機工程研究所<br>98<br>Now is a network blossoming and information bursting generation. Information is distributed on the network for access; therefore, the database system, for data requesting, transacting, and storing, is more and more important. The single database server system, used traditionally, is gradually not met to the requirements from a number of users. Thus the clustering mechanism becomes a better solution. The database server clustering makes the database servers distributed and then resolves the problem of access load, but it introduces the problem of data synchroniz
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Rahm, Erhard. "Automatisches, zielorientiertes Performance Tuning von Transaktionssystemen." 1996. https://ul.qucosa.de/id/qucosa%3A32017.

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Chou, Chia-Feng, and 周家豐. "MySQL database performance tuning using MMS(MySQL Monitor System)." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/98559713592775978443.

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碩士<br>國立中興大學<br>資訊科學與工程學系所<br>100<br>Nowadays, the requirement of system availability is getting higher and higher while the company e-operations become popular and indispensable. Because the database must be kept running 24 hours a day, 7 days a week, it’s necessary to implement a customized monitor system to record the server performance and database status. Most freewares from the Internet, supporting recording system status and database sessions, can provide system status information, but cannot identify the performance bottlenecks. In this thesis, we design a MySQL monitor system(MMS) whi
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Zhou, Jing. "Database performance analysis and tuning : a comparative study of TPC-H benchmark on Oracle and DB2." Thesis, 2003. http://spectrum.library.concordia.ca/2113/1/MQ77729.pdf.

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This project concentrates on the TPC-H benchmark on Oracle 9i Enterprise Edition (EE) and DB2 Universal Database Version 7.2 Enterprise Edition (EE) on Windows2000 operating system. The TPC-H benchmark is a decision-support benchmark, consisting of a set of queries and refresh functions in order to simulate a real environment. There are several size factors supported by TPC to represent the database size. In this project, we use 1GB and 10GB database size. Furthermore, the test results are used to compare the performance of Oracle and DB2 on the Windows2000 operating system. Performance tuning
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El, Gebaly Kareem. "Robustness in Automatic Physical Database Design." Thesis, 2007. http://hdl.handle.net/10012/3163.

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Automatic physical database design tools rely on ``what-if'' interfaces to the query optimizer to estimate the execution time of the training query workload under different candidate physical designs. The tools use these what-if interfaces to recommend physical designs that minimize the estimated execution time of the input training workload. Minimizing estimated execution time alone can lead to designs that are not robust to query optimizer errors and workload changes. In particular, if the optimizer makes errors in estimating the execution time of the workload queries, then the recommended
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Books on the topic "Database Performance Tuning Query Tuning"

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Fritchey, Grant. SQL Server 2012 Query Performance Tuning. Apress, 2012.

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Fritchey, Grant. SQL Server 2008 query performance tuning distilled. Apress, 2009.

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Krogh, Jesper Wisborg. MySQL 8 Query Performance Tuning. Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-5584-1.

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Fritchey, Grant. SQL Server Query Performance Tuning. Apress, 2014. http://dx.doi.org/10.1007/978-1-4302-6742-3.

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Dunham, Jeff. Database performance tuning handbook. McGraw-Hill, 1998.

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Fritchey, Grant. SQL Server 2012 Query Performance Tuning. Apress, 2012. http://dx.doi.org/10.1007/978-1-4302-4204-8.

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Fritchey, Grant. SQL Server 2017 Query Performance Tuning. Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3888-2.

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Fritchey, Grant. SQL Server 2022 Query Performance Tuning. Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8891-7.

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Gurry, Mark. Oracle Performance Tuning. 2nd ed. O'Reilly & Associates, 1996.

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Corrigan, Peter. Oracle Performance Tuning. O'Reilly & Associates, 1993.

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Book chapters on the topic "Database Performance Tuning Query Tuning"

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Fritchey, Grant. "Database Performance Testing." In SQL Server Query Performance Tuning. Apress, 2014. http://dx.doi.org/10.1007/978-1-4302-6742-3_24.

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Fritchey, Grant. "Database Engine Tuning Advisor." In SQL Server Query Performance Tuning. Apress, 2014. http://dx.doi.org/10.1007/978-1-4302-6742-3_10.

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Fritchey, Grant. "Database Workload Optimization." In SQL Server Query Performance Tuning. Apress, 2014. http://dx.doi.org/10.1007/978-1-4302-6742-3_25.

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Fritchey, Grant. "Database Engine Tuning Advisor." In SQL Server 2017 Query Performance Tuning. Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3888-2_10.

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Fritchey, Grant. "Database Engine Tuning Advisor." In SQL Server 2012 Query Performance Tuning. Apress, 2012. http://dx.doi.org/10.1007/978-1-4302-4204-8_5.

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Fritchey, Grant. "Database Performance Testing." In SQL Server 2017 Query Performance Tuning. Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3888-2_26.

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Fritchey, Grant. "Database Performance Testing." In SQL Server 2012 Query Performance Tuning. Apress, 2012. http://dx.doi.org/10.1007/978-1-4302-4204-8_15.

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Fritchey, Grant. "Database Workload Optimization." In SQL Server 2017 Query Performance Tuning. Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3888-2_27.

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Fritchey, Grant. "Database Workload Optimization." In SQL Server 2012 Query Performance Tuning. Apress, 2012. http://dx.doi.org/10.1007/978-1-4302-4204-8_16.

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Alapati, Sam R., Darl Kuhn, and Bill Padfield. "Implementing Query Hints." In Oracle Database 11g Performance Tuning Recipes. Apress, 2011. http://dx.doi.org/10.1007/978-1-4302-3663-4_14.

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Conference papers on the topic "Database Performance Tuning Query Tuning"

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Fuentes, Alain D., Ana Carolina Almeida, Rogério Luís de Carvalho Costa, Vanessa Braganholo, and Sérgio Lifschitz. "Database Tuning with Partial Indexes." In Simpósio Brasileiro de Banco de Dados. Sociedade Brasileira de Computação - SBC, 2018. http://dx.doi.org/10.5753/sbbd.2018.22229.

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Database tuning usually involves indexes, materialized views, partitioning, query rewriting and other techniques. One strategy that presents good results for performance improvements is the use of partial indexes. However, partial indexes have not been used for database tuning in the past. This is because the search space for partial indexes is exponential in the number of attributes and tuples of the table. In this paper, we address this problem by proposing an optimized strategy to select partial indexes. The optimization relies on reducing the amount of logic reads. We explain how to select
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Zadorozhny, Vladimir, Divyasheel Sharma, Prashant Krishnmurthy, and Alexandros Labrinidis. "Tuning query performance in mobile sensor databases." In the 6th international conference. ACM Press, 2005. http://dx.doi.org/10.1145/1071246.1071285.

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Ivanov, V. V., N. I. Pikuleva, A. Sh Khafizova, E. Sh Kremleva, and O. V. Panchenko. "Performance Research of Object-Relational Mapping Technologies in Interaction to MySQL." In International scientific and practical conference “Smart cities and sustainable development of regions” (SMARTGREENS 2024). Crossref, 2025. https://doi.org/10.63550/iceip.2025.1.1.018.

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This paper examines the performance of popular object-relational mapping (ORM) technologies such as Hibernate for Java, Entity Framework for .NET, Django ORM for Python, and Sequelize for Node.js, focusing on their interaction with the MySQL database management system. The analysis covers key aspects including data management, query complexity and scalability, providing valuable insights into the advantages and disadvantages of each technology in different usage scenarios. The article details the process of configuring and using each ORM, offering practical tips for optimizing performance and
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Abdelaziem, O. E., A. Nasser Khafagy, and T. A. Yehia. "Innovative Approach of Generative AI for Automating Technical Bid Evaluations in Oil Companies." In Mediterranean Offshore Conference. SPE, 2024. http://dx.doi.org/10.2118/223359-ms.

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Summary The process of outlining a scope of work and evaluating technical bids in the oil and gas industry is commonly burdensome, labor-intensive, and susceptible to human bias. This paper introduces an AI-assistant chatbot based on the power of open-source large language models (LLMs), natural language processing (NLP), and data analytics, to aid in automating the entire workflow of technical tendering processes, facilitating an improved decision support system (DSS), and mitigating potential subjectivity. The workflow starts with loading documents in the format of scanned PDF files. Firstly
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Yanfei Lv, Huihong He, Hong Zhang, Zhe Liu, and Yasong Zheng. "Olap query performance tuning in Spark." In Third International Conference on Cyberspace Technology (CCT 2015). Institution of Engineering and Technology, 2015. http://dx.doi.org/10.1049/cp.2015.0832.

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Myalapalli, Vamsi Krishna, Thirumala Padmakumar Totakura, and Sunitha Geloth. "Augmenting database performance via SQL tuning." In 2015 International Conference on Energy Systems and Applications. IEEE, 2015. http://dx.doi.org/10.1109/icesa.2015.7503305.

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Zeng, Xiaoqing, Dahan Lin, and Qin Xu. "Query Performance Tuning in Supply Chain Analytics." In 2011 Fourth International Joint Conference on Computational Sciences and Optimization (CSO). IEEE, 2011. http://dx.doi.org/10.1109/cso.2011.212.

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Fang, Xiang, Yi Zou, Yange Fang, Zhen Tang, Hui Li, and Wei Wang. "A Query-Level Distributed Database Tuning System with Machine Learning." In 2022 IEEE International Conference on Joint Cloud Computing (JCC). IEEE, 2022. http://dx.doi.org/10.1109/jcc56315.2022.00012.

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ZHAO, ZUNHAN, Wen Ye, and PEIYU DUAN. "Tuning Database Parameters Using Query Perception and Evolutionary Reinforcement Learning." In CCEAI 2024: 2024 8th International Conference on Control Engineering and Artificial Intelligence. ACM, 2024. http://dx.doi.org/10.1145/3640824.3640869.

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Wiese, David, and Gennadi Rabinovitch. "Knowledge Management in Autonomic Database Performance Tuning." In 2009 Fifth International Conference on Autonomic and Autonomous Systems. IEEE, 2009. http://dx.doi.org/10.1109/icas.2009.20.

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