Automatic Discovery of Technology Networks for Industrial-Scale R&D IT Projects via Data Mining

Industrial-Scale R&D IT Projects depend on many sub-technologies which need to be understood and have their risks analysed before the project can begin for their success. When planning such an industrial-scale project, the list of technologies and the associations of these technologies with each other is often complex and form a network. Discovery of this network of technologies is time consuming for a human to perform, due to the large number of technologies and due to the fact that the technologies are constantly changing. In this paper, a method is provided for the automatic discovery of the network of associations of Industrial IT technologies as a networked graph, using data mining and web-mining algorithms. The proposed process is an approach to form a dynamic weighted graph of technologies. A numeric value is calculated as similarity between technologies. A combination of data mining and web mining techniques have been used to achieve the results. The main objective is to invent a computerized reproducible method so that by the help of it, technological relation can be extracted and updated constantly. This method consists of six phases, of which four phases are performed automatically by novel algorithms introduced in this paper. The analysis of more than 8 million terms suggests that the proposed method provides acceptable results. This paper also provided recommendations to improve the suggested method.