• The Journal of Society for e-Business Studies
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• v.7 no.1
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• pp.107-127
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• 2002

Database system parameter tuning is one of database system tuning that achieve to improve performance of database system with application program tuning and data model tuning. By parameter tuning adjusts value of entry that is staled in data dictionary's parameter file that is included to database system, it is thing which make relevant database system can display performance of most suitable. And, it is that achievement is one o( possible tuning method immediately without occurrence of additional expense or involved hardware for database system performance elevation and ashes composition of software. But, it is actuality that administration about parameter practical use is not achieved, and is using Default Value of parameter that database management system offers just as it is systematically. So, this paper presents parameter tuning process that can ：achieve Parameter tuning of database system that is operating present systematically, and parameter tuning process each activity important input urea and tuning achievement product. And explain about effect and result that happen by sort database system performance and parameters that it is affinity systematically, and grasp relationships between parameter, and change parameter of string database system. And not that parameter uses contents that specify by fixing when establish database administration system, is going to emphasize and explain that must utilize changing continuously during database system operation. It changes parameter entry value how in various kinds different operation environment and present if must apply, and will arrange effect that this parameter enoy value alteration gets in performance liking into account point that is actuality that is using parameter that define database administrators when install the database system just as it is continually without alteration.

• Journal of KIISE:Databases
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• v.31 no.4
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• pp.373-383
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• 2004

There have been an emerging interests in the importance of database tuning techniques under modem database environments in which very large-scale data should be managed. In Particular. database performance parameters should be tuned to reflect system loads appropriately. This paper presents two parameter tuning strategies, namely throughput-based and response-time-based, which tune each performance parameter accordingly. The proposed techniques are applied to two commercial database systems in the TPC-W benchmark to see the effectiveness of those methods. The results show that they can help improve system performance considerably.

• The KIPS Transactions:PartD
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• v.11D no.5
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• pp.1041-1048
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• 2004

Database logs contain various information on database operations, but they are used to recover database systems from failures generally. This paper proposes a log analysis tool that provides useful information for database tuning. This tool provides users with information on work-load organization, database schemas, and resources usages of queries. This paper describes the tool in views of its architecture, functions, implementation, and verification. The tool is verified by running the TPC-W benchmark, and representative analysis results are also presented.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.3
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• pp.27-33
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• 2010

One of the most critical success factor in introducing and operating an integrated information system is the performance management. In this study, we propose some SQL tuning techniques, applicable to optimize the performance of relational database systems. We showed effectiveness of the techniques by applying to a database system of a medium scale company.

• International Journal of Computer Science & Network Security
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• v.23 no.10
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• pp.164-168
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• 2023

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 engineering approaches for integrating ML agents in a DBMS. The first is to build an external tuning controller that treats the DBMS as a black box. The second is to incorporate the ML agents natively in the DBMS's architecture.

• Journal of Information Processing Systems
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• v.18 no.3
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• pp.374-388
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• 2022

Database systems usually have many parameters that must be configured by database administrators and users. RocksDB achieves fast data writing performance using a log-structured merged tree. This database has many parameters associated with write and space amplifications. Write amplification degrades the database performance, and space amplification leads to an increased storage space owing to the storage of unwanted data. Previously, it was proven that significant performance improvements can be achieved by tuning the database parameters. However, tuning the multiple parameters of a database is a laborious task owing to the large number of potential configuration combinations. To address this problem, we selected the important parameters that affect the performance of RocksDB using random forest. We then analyzed the effects of the selected parameters on write and space amplifications using analysis of variance. We used a genetic algorithm to obtain optimized values of the major parameters. The experimental results indicate an insignificant reduction (-5.64%) in the execution time when using these optimized values; however, write amplification, space amplification, and data processing rates improved considerably by 20.65%, 54.50%, and 89.68%, respectively, as compared to the performance when using the default settings.

• The Journal of the Korea Contents Association
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• v.10 no.4
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• pp.84-90
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• 2010

DBMS is used for a range of applications from data warehousing through on-line transaction processing. As a result of this demand, DBMS has continued to grow in terms of its size. This growth invokes the most important issue of manually tuning the performance of DBMS. The DBMS tuning should be adaptive to the type of the workload put upon it. But, identifying workloads in mixed database applications might be quite difficult. Therefore, a method is necessary for identifying workloads in the mixed database environment. In this paper, we propose a SVM workload classifier to automatically identify a DBMS workload. Database workloads are collected in TPC-C and TPC-W benchmark while changing the resource parameters. Parameters for SVM workload classifier, C and kernel parameter, were chosen experimentally. The experiments revealed that the accuracy of the proposed SVM workload classifier is about 9% higher than that of Decision tree, Naive Bayes, Multilayer perceptron and K-NN classifier.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.5
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• pp.757-764
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• 2022

Recently the car tuning has become a trailblazing and creative culture that expresses the personality of the owner. In this paper, the "Car-Vatar", which is the compound word formed from the words "Car" and "Avatar", has been developed to investigate car tuning on the metaverse engineering platform. The Car-Vatar has been developed as a web-based vehicle dynamic simulation service for providing information about car tuning. That has been focused on investigating diverse vehicular performances, such as acceleration, braking, handling and fuel efficiency, according to the tuning vehicles and tuning parts on the virtual engineering platform. The Car-Vatar platform has provided two major services; one is real-time 3D tuning information system for the dress-up and performance-up tuning parts, the other is diverse vehicle dynamics system for the performance-up tuning parts. To check the validation of the Car-Vatar platform, the comparison between virtual simulation results and driving test results has been discussed on various driving environments.

• The KIPS Transactions:PartD
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• v.11D no.4
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• pp.747-754
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• 2004

Database administrators should be aware of performance characteristics of database systems in order to manage database system effectively. The usages of system resources in database systems could be quite different under database workloads. The objective of this paper is to identify and analyze performance characteristics of database systems in different workloads, which could help database tuners tune database systems Under the TPC-C and TPC-W workloads, which represent typical workloads of online transaction processing and electronic commerce respectively, we investigated usage types of resource that are determined by fourteen performance indicator, and are behaved in response to changes of four tuning parameters (data buffer, private memory, I/O process, shared memory). Eight out of the fourteen performance indicators cleary show the performance differences under the workloads. Changes of data buffer parameter give a influences to database system. The tuning parameter that affects the system performance significantly is the database buffer size in the both workloads.

• Journal of KIISE:Databases
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• v.32 no.1
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• pp.86-99
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• 2005

In this paper, we suggest a system which automatically verifies and tunes transaction processing database applications based on program analysis technology. This system automatically verifies two kinds of transaction processing errors. The first case is the un-closed transaction. In this case, data is not updated as expected or performance of overall system can decrease seriously by locking some database tables until the process terminates. The second case is the miss-use of transaction isolation(inking) level. This causes runtime exception or abnormal termination of the program depending on runtime environment. This system automatically tunes two kinds of inefficient definition of transaction processing which decrease the performance of overall system. The first case happens when opened transaction is closed too late. And the second case happens when transaction isolation level is set too high.