The Impact of Network Characteristics on the Accuracy of Spatial Web Performance Forecasts

  • Leszek Borzemski
  • Anna Kamińska-Chuchmała
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 234)


Geostatistical methods are very useful tools for spatial forecasts in many research domains. The usage of these methods in the computer science domain is still in its infancy. There were attempts to use simulation and estimation geostatistical methods used in previous research to spatial Web systems performance forecasts. As the results are quite encouraging, therefore, authors decided to carry out further investigations in this field. This chapter presents an overview of the work concerning the use of geostatistical methods and the analysis of the impact of various factors on the accuracy of spatial Web performance forecasts for servers belonging to different Autonomous Systems (ASs). Forecasts were made by using geostatistical methods. The data for research was collected in active Internet Web Performance measurements carried out by software agents monitoring a group of Web servers. In this research, the network routes from agents in Gdańsk and Wrocław to European Web servers are considered.


Forecast Geostatistics Internet IoT Performance MWING WoT Web 


  1. 1.
    Internet of Things - An Action Plan for Europe. Commission of the European Communities, Brussels, COM (2009) 278Google Scholar
  2. 2.
    Uckelmann, D., Isenberg, M.A., Teucke, M., Halfar, H., Scholz-Reiter, B.: Autonomous control and the internet of things: Increasing robustness, scalability and agility in logistic networks. In: Ranasinghe, D.C., Sheng, Q.C., Zeadally, S. (eds.) Unique Radio Innovation for the 21st Century, pp. 163–181. Springer Berlin, Heidelberg (2010)Google Scholar
  3. 3.
    Guinard, D., Trifa, V., Wilde, E.: A resource oriented architecture for the web of things. In: Internet of Things (IOT), pp. 1–8 (2010)Google Scholar
  4. 4.
    Stirbu, V.: Towards a restful plug and play experience in the web of things. In: IEEE International Conference on Semantic Computing, pp. 512–517 (2008)Google Scholar
  5. 5.
    Matheron, G.: The Theory of Regionalized Variables and its Applications. Technical Report. Ecole nationale superieure des mines, Paris (1971)Google Scholar
  6. 6.
    Chiles, J.P., Delfiner, P.: Geostatistics: Modeling Spatial Uncertainty, 2nd edn. Wiley, New York (2012)CrossRefGoogle Scholar
  7. 7.
    Krige, D.: A statistical approach to some basic mine valuation problems on the Witwatersrand. J. Chem. Metall. Min. Soc. 52, 119–139 (1951)Google Scholar
  8. 8.
    Delfiner, P., Haas, A.: Over thirty years of petroleum geostatistics. In: Bilodeau, M., Meyer, F., Schmitt, M. (eds.) Space, Structure and Randomness, Lecture Notes in Statistics, vol. 183, pp. 89–104. Springer, New York (2005)Google Scholar
  9. 9.
    Parker, H.: Trends in geostatistics in the mining industry. In: Verly, G., David, D., Journel, A., Marechal, A. (eds.) Geostatistics for Natural Resources Characterization, NATO ASI series: Mathematical and Physical Sciences, pp. 915–934. D. Reidel, Dordrecht (1984)CrossRefGoogle Scholar
  10. 10.
    Deraisme, J., Bobbia, M., de Foquet, C.: Contribution of geostatistics to the study of risks related to air pollution. Advanced Air Pollution, InTech Rijeka (2011)Google Scholar
  11. 11.
    Inizian, M.: Geostatistical validation of a marine ecosystem model using in situ data. Technical Report. Centre de Geostatistique Ecole des Mines, Paris (2002)Google Scholar
  12. 12.
    Oliver, M.: Geostatistical Applications for Precision Agriculture. Springer Netherlands, Dordrecht (2010)CrossRefMATHGoogle Scholar
  13. 13.
    Tang, L., Hossain, F.: Understanding the dynamics of transfer of satellite rainfall error metrics from gauged to ungauged satellite gridboxes using interpolation methods. IEEE J.Sel. Top. Appl. Earth Observ. Remote Sens. 4(4), 844–856 (2011)CrossRefGoogle Scholar
  14. 14.
    Amiri, A., Gerdtham, U.: Relationship between exports, imports, and economic growth in france: evidence from cointegration analysis and granger causality with using geostatistical models. munich personal repec archive chapter no. 34190 (2011)Google Scholar
  15. 15.
    Kamińska-Chuchmała, A., Wilczyński, A.: Application simulation methods to spatial electric load forecasting. Rynek Energii 80(1), 2–9 (2009) (in Polish)Google Scholar
  16. 16.
    Kamińska-Chuchmała, A., Wilczyński, A.: Spatial electric load forecasting in transmission networks with sequential gaussian simulation method. Rynek Energii 92(1), 35–40 (2011)Google Scholar
  17. 17.
    Wang, Y., Zhuang, D., Liu, H.: Spatial distribution of floating car speed. J.Transp. Syst. Eng. Inf. Technol. 12(1), 36–41 (2012)Google Scholar
  18. 18.
    Borzemski, L., Kamińska-Chuchmała, A.: Client-perceived web performance knowledge discovery through turning bands method. Cybern. Syst. 43(4), 354–368 (2012)CrossRefGoogle Scholar
  19. 19.
    Borzemski, L., Kamińska-Chuchmała, A.: Distributed web systems performance forecasting using turning bands method. IEEE Trans. Industr. Inf. 9(1), 254–261 (2013)CrossRefGoogle Scholar
  20. 20.
    Borzemski, L., Kamińska-Chuchmała, A.: Knowledge engineering relating to spatial web performance forecasting with sequential gaussian simulation method. In: Graña, M., Toro, C., Posada, J., Howlett, R., Jain, L.C. (eds.) Advances in Knowledge-Based and Intelligent Information and Engineering Systems, Frontiers in Artificial Intelligence and Applications, vol. 243, pp. 1439–1448. IOS Press, Amsterdam (2012)Google Scholar
  21. 21.
    Borzemski, L., Kamińska-Chuchmała, A.: Web performance forecasting with kriging method. In: Ali, M., Bosse, T., Hindriks, K.V., Hoogendoorn, M., Jonker, C.M., Treur, J. (eds.) Contemporary Challenges and Solutions in Applied Artificial Intelligence, Studies in Computational Intelligence. vol. 489, pp. 149–154, Springer-Verlag (2013)Google Scholar
  22. 22.
    Borzemski, L.: The experimental design for data mining to discover web performance issues in a wide area network. Cybern. Syst. 41(1), 31–45 (2010)CrossRefMATHGoogle Scholar
  23. 23.
    Borzemski, L., Cichocki, L., Fraś, M., Kliber, M., Nowak, Z.: MWING: A multiagent system for web site measurements. In: Nguyen, N.T., Grzech, A., Howlett, R.J., Jain, L.C. (eds.) Agent and Multi-Agent Systems: Technologies and Applications, Lecture Notes in Computer Science, vol. 4496, pp. 278–287. Springer Berlin Heidelberg (2007)Google Scholar
  24. 24.
    Borzemski, L., Danielak, M., Kamińska-Chuchmała, A.: Short-term spatio-temporal forecasts of web performance by means of turning bands method. In: Nguyen, N.T., Hoang, K., Jedrzejowicz, P. (eds.) Computational Collective Intelligence. Technologies and Applications, Lecture Notes in Computer Science, vol. 7654, pp. 132–141. Springer Berlin Heidelberg (2012)Google Scholar
  25. 25.
    Leuangthong, O., Khan, K., Deutsch, C.: Solved Problems in Geostatistics. Wiley, New Jersey (2008)MATHGoogle Scholar
  26. 26.
    Lantuejoul, C.: Geostatistical Simulation: Models and Algorithms. Springer Berlin, Heidelberg (2002)CrossRefGoogle Scholar
  27. 27.
    Wackernagel, H.: Multivariate Geostatistics. Springer Berlin, Heidelberg (2003)CrossRefMATHGoogle Scholar
  28. 28.
    Borzemski, L., Kamińska-Chuchmała, A.: Knowledge discovery about web performance with geostatistical turning bands method. In: Koenig, A., Dengel, A., Hinkelmann, K., Kise, K., Howlett, R.J., Jain, L.C. (eds.) Knowlege-Based and Intelligent Information and Engineering Systems, Lecture Notes in Computer Science, vol. 6882, pp. 581–590. Springer Berlin Heidelberg (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Institute of InformaticsWrocław Uniwersity of TechnologyWrocławPoland

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