Gotowe bibliografie tematyczne / Database Performance Tuning

Spis treści

  1. Artykuły w czasopismach
  2. Rozprawy doktorskie
  3. Książki
  4. Części książek
  5. Streszczenia konferencji

Gotowa bibliografia na temat „Database Performance Tuning”

Autor: Grafiati
Data publikacji: 2 czerwca 2025
Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "Database Performance Tuning"

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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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Rozprawy doktorskie na temat "Database Performance Tuning"

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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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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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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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Książki na temat "Database Performance Tuning"

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

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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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Jackie, Zhu Wei-Dong, and International Business Machines Corporation. International Technical Support Organization., eds. Performance tuning for Content Manager. IBM, International Technical Support Organization, 2003.

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Alapati, Sam R., Darl Kuhn, and Bill Padfield. Oracle Database 12c Performance Tuning Recipes. Apress, 2013. http://dx.doi.org/10.1007/978-1-4302-6188-9.

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

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Habeeb, Zulfiqer. Oracle8i DBA: Performance tuning. Coriolis Group Books, 2001.

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Alur, Nagraj. DB2 UDB/WebSphere performance tuning guide. IBM Corp., International Tecnical Support Organization, 2002.

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Pack, Charles A. Oracle 9i database: Performance tuning exam guide. Osborne/McGraw-Hill, 2002.

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Mittra, Sitansu S. Database performance tuning and optimization: Using Oracle. Springer-Verlag, 2003.

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Części książek na temat "Database Performance Tuning"

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Alapati, Sam R. "Performance Tuning: Tuning the Instance." In Expert Oracle9i Database Administration. Apress, 2003. http://dx.doi.org/10.1007/978-1-4302-0773-3_19.

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Klein, Scott, and Herve Roggero. "Performance Tuning." In Pro SQL Database for Windows Azure. Apress, 2012. http://dx.doi.org/10.1007/978-1-4302-4396-0_11.

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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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Vallath, Murali. "Tuning the Database." In Expert Oracle RAC Performance Diagnostics and Tuning. Apress, 2014. http://dx.doi.org/10.1007/978-1-4302-6710-2_9.

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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 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 Query Performance Tuning. Apress, 2014. http://dx.doi.org/10.1007/978-1-4302-6742-3_10.

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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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Streszczenia konferencji na temat "Database Performance Tuning"

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Peck Lee, Sai, and Dzemal Zildzic. "Oracle Database Workload Performance Measurement and Tuning Toolkit." In InSITE 2006: Informing Science + IT Education Conference. Informing Science Institute, 2006. http://dx.doi.org/10.28945/2965.

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Database tuning practice is mainly conducted as a consequence of user’s complaints on the performance. There is a need for a reactive monitoring and tuning tool enabling a real-time overview of the main resource consumers in order to detect and solve performance bottlenecks. With an assessment of the Oracle’s high-availability database, in terms of the main architectural components and their impact on performance, we have developed a Java tool for the efficient and resource-effective tuning of Oracle databases. Our tool, called Workload Performance Monitoring and Tuning (WPMT), enables proacti
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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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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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Rabinovitch, Gennadi, and David Wiese. "Non-linear Optimization of Performance Functions for Autonomic Database Performance Tuning." In Third International Conference on Autonomic and Autonomous Systems (ICAS'07). IEEE, 2007. http://dx.doi.org/10.1109/conielecomp.2007.89.

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Belknap, Peter, Benoit Dageville, Karl Dias, and Khaled Yagoub. "Self-Tuning for SQL Performance in Oracle Database 11g." In 2009 IEEE 25th International Conference on Data Engineering (ICDE). IEEE, 2009. http://dx.doi.org/10.1109/icde.2009.165.

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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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Lima, Maria I. V., Victor A. E. de Farias, Francisco D. B. S. Praciano, and Javam C. Machado. "Workload-aware Parameter Selection and Performance Prediction for In-memory Databases." In Simpósio Brasileiro de Banco de Dados. Sociedade Brasileira de Computação - SBC, 2018. http://dx.doi.org/10.5753/sbbd.2018.22228.

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In-memory databases, just as hard drive ones, may offer hundreds of customizable settings, making the task of system tuning overwhelming for a database administrator. Even worse, the number of parameters continues to grow over the years and they can affect performance in a not intuitive manner. Models that capture their behavior can assist automatic tuning mechanisms to obtain optimal performance. In this work, we propose a learning-based approach to select the most meaningful parameters and generate a performance model based on both the workload and the database configurations. Experimental r
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Rodd, S. F., and U. P. Kulkarni. "Adaptive Self-Tuning Techniques for Performance Tuning of Database Systems: A Fuzzy-Based Approach." In 2013 2nd International Conference on Advanced Computing, Networking and Security (ADCONS). IEEE, 2013. http://dx.doi.org/10.1109/adcons.2013.49.

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Nimalasena, Asanga, and Vladimir Getov. "Performance tuning of database systems using a context-aware approach." In 2014 9th International Conference on Computer Engineering & Systems (ICCES). IEEE, 2014. http://dx.doi.org/10.1109/icces.2014.7030936.

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Dias, Lucas B., Maristela Holanda, Ruben C. Huacarpuma, and Rafael T. de Sousa Jr. "NoSQL Database Performance Tuning for IoT Data - Cassandra Case Study." In 3rd International Conference on Internet of Things, Big Data and Security. SCITEPRESS - Science and Technology Publications, 2018. http://dx.doi.org/10.5220/0006782702770284.

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