Advertisement

Algorithmic Decomposition of Tasks with a Large Amount of Data

  • Walery RogozaEmail author
  • Ann IshchenkoEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 889)

Abstract

The transformation of models and data to the form that allows their decomposition is called algorithmic decomposition. It is a necessary preparatory stage in many applications, allowing us to present data and object models in a form convenient for dividing the processes of solving problems into parallel or sequential stages with significantly less volumes of data. The paper deals with three problems of modeling objects of different nature, in which algorithmic decomposition is an effective tool for reducing the amount of the data being processed and for flexible adjustment of object models performed to improve the accuracy and reliability of the results of computer simulation. The discussion is accompanied by simple examples that allow the reader to offers a clearer view of the essence of the methods presented.

Keywords

Algorithmic decomposition Model reduction Time series Complex objects Computer simulation 

References

  1. 1.
    Leskovec, J., Rajaraman, A., Ullman, J.D.: Mining of Massive Datasets. Cambridge University Press (2014)Google Scholar
  2. 2.
    Weste, N., Harris, D.: CMOS VLSI Design. Addison-Wesley (2004)Google Scholar
  3. 3.
    Tikhonov, A.N.: Systems of differential equations containing small parameters in the derivatives. Mat. sb. 73(3), 575–586 (1952)MathSciNetGoogle Scholar
  4. 4.
    Rogoza, W.: Adaptive simulation of separable dynamical systems in the neural network basis. In: Pejas, J., Piegat, A. (eds.) Enhanced Methods in Computer Security, Biometrcic and Artificial Intelligence Systems, pp. 371–386. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  5. 5.
    Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. The MIT Press, Cambridge (2017)zbMATHGoogle Scholar
  6. 6.
    Rogoza, W.: Some models of problem adaptive systems. Pol. J. Environ. Stud. 16(#5B), 212–218 (2006)Google Scholar
  7. 7.
    Sze, S.M.: Physics of Semiconductor Devices, 2nd edn. Wiley (WIE), New York (1981)Google Scholar
  8. 8.
    Box, G., Jenkins, G.: Time Series Analysis: Forecasting and Control. Holden-Day, San Francisco (1970)zbMATHGoogle Scholar
  9. 9.
    Madala, H.R., Ivakhnenko, A.G.: Inductive Learning Algorithms for Complex Systems Modeling. CRC Press, Boca Raton (1994)zbMATHGoogle Scholar
  10. 10.
    Rogoza, W.: Deterministic method for the prediction of time series. In: Kobayashi, S., Piegat, A., Pejaś, J., El Fray, I., Kacprzyk, J (eds.) ACS 2016. AISC, vol. 534, pp. 68–80. Springer, Heidelberg (2017)Google Scholar
  11. 11.
    Miller, G.: Numerical Analysis for Engineers and Scientists. Cambridge University Press, Cambridge (2014)zbMATHGoogle Scholar
  12. 12.
    Rogoza, W., Zabłocki, M.: A feather forecasting system using intelligent BDI multiagent-based group method of data handling. In: Kobayashi, S., Piegat, A., Pejaś, J., El Fray, I., Kacprzyk, J (eds.) Hard and Soft Computing for Artificial Intelligence, Multimedia and Security. AISC, vol. 534, pp. 37–48. Springer, Heidelberg (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Faculty of Computer Science and Information TechnologyWest Pomeranian University of TechnologySzczecinPoland
  2. 2.Educational and Scientific Complex “Institute of Applied Systems Analysis” - ESC “IASA”The National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”KievUkraine

Personalised recommendations