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Article

  • Title

    Prediction of a relational database’s operation in the information system

  • Authors

    Kungurtsev Аlexey B.
    Zinovatnaya Svitlana L.
    Munzer Al Abdo

  • Subject

    COMPUTER AND INFORMATION NETWORKS AND SYSTEMS. MANUFACTURING AUTOMATION

  • Year 2015
    Issue 1(45)
    UDC 004.62.051
    DOI 10.15276/opu.1.45.2015.19
    Pages 113-120
  • Abstract

    A necessary condition of any information system’s efficient operation is to ensure that response time to user requests satisfies the subject area requirements. Actual is the problem of automated methods for choosing the tools reducing the execution time of queries to the database. Formal description of the system’s state and development gives the possibility to apply the most effective ways of speeding up queries in time when there is a need to improve system performance. While researching some dependences are obtained, allowing to predict the intensity of requests entering the information system, to determine the trend of system’s loading, to identify peak periods and set of the most commonly used tables and fields. The results on system operative features researching provide the possibility to predict system’s behavior and to take timely measures to maintain the required level of performance.

  • Keywords information system, relational database, query, test, modeling, prediction
  • Viewed: 701 Dowloaded: 14
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  • References

    Література
    1.    A Framework for Testing Database Applications / D. Chays, S. Dan, P.G. Frankl, F.I. Vokolos, and E.J. Weyuker // Proceedings of the 2000 ACM SIGSOFT international symposium on Software testing and analysis (ISSTA 2000), August 21–24, 2000, Portland, Oregon, USA. — New York: ACM Press, 2000. — PP. 147—157.
    2.    E-NAXOS DataGen 2005: Test Data Generator [Електронний ресурс] / Olivier Dahan; E-Naxos. — Режим доступу: http://www.e-naxos.com/blog/default.aspx (Дата звернення: 20.11.2014).
    3.    IBM InfoSphere Optim Test Data Management [Електронний ресурс] / IBM. — Режим доступу: http://www-01.ibm.com/software/data/optim/core/test-data-management-solution/ (Дата звернення: 20.11.2014).
    4.    Justus, S. An empirical validation of the suite of metrics for object-relational data modelling / S. Justus, K. Iyakutti // International Journal of Intelligent Information and Database Systems. — 2011. — Vol. 5, No. 1. — PP. 49—80.
    5.    Кунгурцев, А.Б. Средства автоматизированного заполнения баз данных информационных систем для проведения тестирования запросов / А.Б. Кунгурцев, А.А. Блажко, А.Ю. Левченко // Електромашинобуд. та електрообладн. — 2009. — Вип. 72. — С. 201—204.
    6.    Daniel, L. Digital Forensics for Legal Professionals: Understanding Digital Evidence from the Warrant to the Courtroom / L. Daniel, L. Daniel. — Waltham: Syngress, 2011. – 368 p.
    7.    Osman, R. Database system performance evaluation models: A survey / R. Osman, W.J. Knottenbelt // Performance Evaluation. — 2012. — Vol. 69, Issue 10. — PP. 471—493.
    8.    Коротаев, А.В. Законы истории. Математическое моделирование развития Мир-Системы. Демография, экономика, культура / А.В. Коротаев [и др.] ; Рос. гос. гуманитарный ун-т. Факультет истории, политологии и права, РАН, Центр цивилизационных и региональных исследований. Институт востоковедения. — 2-е изд., испр. и доп. — М.: КомКнига, 2007. — 224 с.
    9.    Кунгурцев, А.Б. Определение параметров периодического включения/выключения материализованных представлений в информационных системах / А.Б. Кунгурцев, Ю.Н. Возовиков, Нгуен Чан Куок Винь // Вост.-Европ. журн. передовых технологий. — 2012. — № 4/2 (58). — С. 42—45.
    10.    Artail, H. SQL query space and time complexity estimation for multidimensional queries / H. Artail, H. El Amine, F. Sakkal // International Journal of Intelligent Information and Database Systems. — 2008. — Vol. 2, No. 4. — PP. 460—480.
    11.    Hastie, T. The Elements of Statistical Learning: Data Mining, Inference, and Prediction / T. Hastie, R. Tibshirani, J. Friedman. — 2nd Edition. — New York: Springer, 2009. — 745 p.

    References
    1.    Chays, D., Dan, S., Frankl, P.G., Vokolos, F.I., & Weyuker, E.J. (2000). A framework for testing database applications. In M.J. Harrold (Ed.), Proceedings of the 2000 ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA 2000) (pp. 147—157). New York: ACM Press.
    2.    Dahan, O. (n.d.). E-NAXOS DataGen 2005: Test Data Generator [Web log post]. Retrieved from http://www.e-naxos.com/blog/default.aspx
    3.    IBM (n.d.). IBM InfoSphere Optim Test Data Management. Retrieved from http://www-01.ibm.com/software/data/optim/core/test-data-management-solution/
    4.    Justus, S., & Iyakutti, K. (2011). An empirical validation of the suite of metrics for object-relational data modeling. International Journal of Intelligent Information and Database Systems, 5(1), 49—80.
    5.    Kungurtsev, A.B., Blazhko, A.A., & Levchenko, A.Yu. (2009). Tools of database filling automation in information system for query testing. Electrical Machine-Building and Electrical Equipment, 72, 201—204.
    6.    Daniel, L., & Daniel, L. (2011). Digital Forensics for Legal Professionals: Understanding Digital Evidence from the Warrant to the Courtroom. Waltham: Syngress.
    7.    Osman, R., & Knottenbelt, W.J. (2012). Database system performance evaluation models: A survey. Performance Evaluation, 69(10), 471—493.
    8.    Korotaev, A.V., Malkov, A.S., & Khalturina, D.A. (2007). Law of History. Mathematical Modeling of Historical Macroprocesses. Demography, Economics, War (2nd Ed.). Moscow: KomKniga.
    9.    Kungurtsev, A., Vozovikov, Yu., & Nguyen Tran Quoc Vinh (2012). Determination of the parameters of periodic ON/OFF materialized view in the information system. Eastern-European Journal of Enterprise Technologies, 4(2), 42—45.
    10.    Artail, H., El Amine, H., & Sakkal, F. (2008). SQL query space and time complexity estimation for multidimensional queries. International Journal of Intelligent Information and Database Systems, 2(4), 460—480.
    11.    Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd Ed.). New York: Springer.

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