Intelligent Expertise Classification approach: An innovative artificial intelligence approach to accelerate network data visualization

Abstract

In order to visualize huge network traffic, data visualization applications are being developed and used to complement network data visualization. With today's network data visualization tools, it is only possible to view small portions of data and consuming lots of time to process the data. The network data process time can be reduced with the innovative artificial intelligence approach, which can effectively accelerate the network data visualization and consequently classify network data into different level of details according diverse computer users' expertise level on network. In the last few years, many visualization tools have been developed; either suffers from time to process the network data or the low understanding from the network data visualization efficiency. In this paper, we proposed an innovative intelligence approach, namely Intelligent Expertise Classification Algorithm (IECA) based on diverse computer users' expertise level in order to improve the network data process time as well as the understanding level among computer users. The approach architecture details and its requirements such as expertise level from diverse computer users and processed data from data mining classification will be discussed. Numbers of experiments have been carried out on 100 computer users from different fields and different level of computer expertise to evaluate the approach effectiveness. It features fast intelligent expertise classification and support network data understanding performance.