Donelan, H.; Pattinson, C.; Palmer-Brown, D. and Lee, S.W.
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This paper presents a novel neural network method for the analysis and interpretation of data that describes user interaction with a training tool. The method is applied to the interaction between trainee network managers and a simulated network management system. A simulation based approach to the task of efficiently training network managers, through the use of a simulated network, was originally presented by Pattinson . The motivation was to provide a tool for exposing trainee network managers to a life like situation, where both normal network operation and ‘fault’ scenarios could be simulated in order to train the network manager. The data logged by this system describes the detailed interaction between trainee network manager and simulated network. The work presented here provides an analysis of this interaction data that enables an assessment of the capabilities of the network manager as well as an understanding of how the network management tasks are being approached. A neural network architecture [Lee et al, 2004] is adapted and implemented in order to perform an exploratory data analysis of the interaction data. The neural network architecture employs a novel form of continuous self-organisation to discover key features, and thus provide new insights into the data.
|Item Type:||Journal Article|
|Keywords:||Network management; neural networks; data analysis; self-organisation; unsupervised learning|
|Academic Unit/Department:||Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM)
|Depositing User:||Helen Donelan|
|Date Deposited:||23 Oct 2006|
|Last Modified:||02 Aug 2016 13:01|
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