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

机译:基于决策树和统计特征分析的技术趋势分析与预测应用

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摘要

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 mode! is widely and generally used for information analysis and forecasting.
机译:分析大量信息并支持远见卓识是非常重要的任务,但它们是非常耗时的工作。此外,对科学技术的信息分析和预测对于研究人员,政府官员,商人等也是非常关键的任务。最近已经进行了一些相关研究,并且积极开发了半自动工具。许多研究人员,历史学家和商人通常也将这些工具用于战略决策。但是,现有的项目和工具是基于多位专家的主观意见,并且大多数工具仅能解释当前情况,而不能预测近期趋势。因此,在本文中,我们提出一种基于定量分析和几种文本挖掘技术的技术趋势分析和预测模型,以进行有效,系统和客观的信息分析和预测技术趋势。此外,由于Gartner的模式,我们在建议的模型和Gartner的预测模型之间执行了比较评估,以验证建议的模型!广泛且普遍用于信息分析和预测。

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