Technology trends analysis and forecasting application based on decision tree and statistical feature analysis

Analyzing mass information and supporting foresight are very important task but they are extremely time-consuming work. In addition, information analysis and forecasting about the science and technology are also very critical tasks for researchers, government officers, businessman, etc. Some related studies recently have been executed and semi-automatic tools have been developed actively. Many researchers, annalists, and businessmen also generally use those tools for strategic decision making. However, existing projects and tools are based on subjective opinions from several experts and most of tools simply explain current situations, not forecasting near future trends. Therefore, in this paper, we propose a technology trends analysis and forecasting model based on quantitative analysis and several text mining technologies for effective, systematic, and objective information analysis and forecasting technology trends. Additionally, we execute a comparative evaluation between the suggested model and Gartner’s forecasting model for validating the suggested model because the Gartner’s model is widely and generally used for information analysis and forecasting.

Author(s): Jinhyung Kim , Myunggwon Hwang , Do-Heon Jeong, , Hanmin Jung
Organization(s): Korea Institute of Science and Technology Information (KISTI)
Source: Expert Systems with Applications (www.sciencedirect.com/science/article/pii/S0957417412007191)
Year: 2012

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