At present, one of the greatest threats to companies is not being able to cope with the constant changes that occur in the market because they do not predict them well in advance. Therefore, the development of new processes that facilitate the detection of significant phenomena and future changes is a key component for correct decision making that sets a correct course in the company. For this reason, a business intelligence architecture system is hereby proposed to allow the detection of discrete changes or weak signals in the present, indicative of more significant phenomena and transcendental changes in the future. In contrast to work currently available focusing on structured information sources, or at most with a single type of data source, the detection of these signals is here quantitatively based on heterogeneous and unstructured documents of various kinds (scientific journals, newspaper articles and social networks), to which text mining and natural language processing techniques (a multi-word expression analysis) are applied. The system has been tested to study the future of the artificial intelligence sector, obtaining promising results to help business experts in the recognition of new driving factors of their markets and the development of new opportunities.

中文翻译：

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通过文本挖掘技术检测异类文档中的弱信号，改善战略决策

当前，对公司最大的威胁之一是无法应对市场中不断发生的变化，因为它们无法提前预测到它们。因此，开发有助于发现重大现象和未来变化的新流程是正确决策的关键组成部分，可为公司树立正确的道路。为此，在此提出了一种商业智能体系结构系统，以允许检测当前的离散变化或微弱信号，这表明未来将出现更重要的现象和先验变化。与目前专注于结构化信息源或最多只使用一种类型的数据源的工作相反，这些信号的检测在此定量地基于各种异类和非结构化文档（科学期刊，报纸文章和社交网络），并应用了文本挖掘和自然语言处理技术（多词表达分析）。该系统已经过测试，可以研究人工智能领域的未来，并获得可喜的结果，以帮助业务专家认识到其市场的新驱动因素和新机遇的发展。