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Polish Information Processing Society
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Annals of Computer Science and Information Systems, Volume 11

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

A Database Performance Polynomial Multiple Regression Model

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DOI: http://dx.doi.org/10.15439/2017F416

Citation: Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 11, pages 743747 ()

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Abstract. Modelling of a database performance depending on numerous factors is the first step towards its optimization. The linear regression model with optional parameters was created. Regression equation coefficients are optimized with the Flower Pollination metaheuristic algorithm. The algorithm is executed with numerous possible execution parameter combinations and results are discussed. Potential obstacles are discussed and alternative modelling approaches are mentioned.

References

  1. A. Nowosielski, P. A. Kowalski, and P. Kulczycki, “The column-oriented database partitioning optimization based on the natural computing algorithms,” in 2015 Federated Conference on Computer Science and Information Systems, FedCSIS 2015, Łódź, Poland, September 13-16, 2015, 2015. http://dx.doi.org/10.15439/2015F262 pp. 1035–1041. [Online]. Available: http://dx.doi.org/10.15439/2015F262
  2. A. Nowosielski, P. A. Kowalski, and P. Kulczycki, “The column- oriented data store performance considerations,” in Computer Science and Information Systems (FedCSIS), 2016 Federated Conference on. IEEE, 2016, pp. 877–881.
  3. X.-S. Yang, “Flower Pollination Algorithm for Global Optimization,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, vol. 7445 LNCS, pp. 240–249. ISBN 9783642328930. [Online]. Available: http://link.springer.com/10.1007/978-3-642-32894-7 27
  4. C. R. Rao and H. Toutenburg, “Linear models,” in Linear models. Springer, 1995, pp. 23–24.
  5. C. Blum and X. Li, “Swarm Intelligence in Optimization,” Swarm Intelli gence Introduction and Applications, pp. 43–85, 2008. http://dx.doi.org/10.1007/978-3-540-74089-6
  6. T. Santhanam and A. C. Subhajini, “Radial Basis Function Neural Network.”
  7. S. E. VT and Y. C. Shin, “Radial basis function neural network for approximation and estimation of nonlinear stochastic dynamic systems,” IEEE Transactions on Neural Networks, vol. 5, no. 4, pp. 594–603, 1994.
  8. L. Xu, A. Krzyżak, and A. Yuille, “On radial basis function nets and kernel regression: statistical consistency, convergence rates, and receptive field size,” Neural Networks, vol. 7, no. 4, pp. 609–628, 1994.
  9. P. Kulczycki, “Kernel Estimators in Systems Analysis,” WNT, Warsaw, 2005.
  10. V. Egozcue and J. J. Tolosana, “Lecture Notes on Compositional Data Analysis,” vol. 962, no. 2003, p. 96, 2007. [Online]. Available: http://dugi-doc.udg.edu/handle/10256/297
  11. S. Khan, “Predictive distribution of regression vector and residual sum of squares for normal multiple regression model,” Communications in Statistics-Theory and Methods, vol. 33, no. 10, pp. 2423–2441, 2005.
  12. S. Łukasik and P. A. Kowalski, “Study of Flower Pollination Algorithm for Continuous Optimization,” in Intelligent Systems'2014. Springer, 2015, pp. 451–459. [Online]. Available: http://dx.doi.org/10.1007/ 978-3-319-11313-5 40
  13. A. H. Gandomi and A. H. Alavi, “Krill herd: A new bio-inspired optimization algorithm,” Communications in Nonlinear Science and Numerical Simulation, vol. 17, no. 12, pp. 4831–4845, 2012. doi: 10.1016/j.cnsns.2012.05.010. [Online]. Available: http://dx.doi.org/10. 1016/j.cnsns.2012.05.010
  14. P. A. Kowalski and S. Łukasik, “Experimental Study of Selected Parameters of the Krill Herd Algorithm,” in Intelligent Systems'2014. Springer, 2015, pp. 473–485. [Online]. Available: http://dx.doi.org/10. 1007/978-3-319-11313-5 42
  15. K. Hron, P. Filzmoser, and K. Thompson, “Linear regression with compositional explanatory variables,” Journal of applied statistics, vol. 39, no. 5, pp. 1115–28, 2012. http://dx.doi.org/10.1080/0266476YYxxxxxxxx. [Online]. Available: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2712304&tool=pmcentrez&rendertype=abstract