Artificial Intelligence Elements in Data Mining from Remote Experiments

  • Lukas Pálka
  • Franz Schauer
  • Ivan Zelinka
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 289)


In spite of the fact that remote laboratories have been existing for at least three decades, virtually no attention has been devoted to the accumulated data analysis of this new means of education. The paper deals with the data analysis, gathered in the Datacentre (DTC) implemented with the Laboratory Management System (RLMS), connected in turn to remote laboratories and remote experiments. In particular, we concentrate and describe a new model of experiment data analysis, based on the principles of artificial intelligence, based on the criterion function in need. The leading idea of the model functioning is during the procedure of rig(s) recognition i.e Data weighting: Data recognition: Data preparation: Phenomenon modelling: Model and measurement data comparison: Result deployment, where the artificial intellingence is integrated with steps of Data weighting by association and regression using neuron network. Benefit of the suggested method is its speed and efficiency and thus using it for the optimization of individual remote experiments and ther efficiency.Paper may serve as an inspiring source for the development in the field of remote laboratories, but it may influence in the similar areas of data mining.


ISES analysis data Measureserver remote experiment data mining 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Faculty of Applied InformaticsTomas Bata University in ZlínZlínCzech Republic
  2. 2.University of TrnavaTrnavaSlovak Republic
  3. 3.VŠB-Technical University of OstravaOstrava-PorubaCzech Republic

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