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A Research Review and Taxonomy Development for Decision Support and Business Analytics Using Semantic Text Mining

机译：使用语义文本挖掘决策支持和业务分析的研究综述与分类

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By 2018, business analytics (BA), believed by global CIOs to be of strategic importance, had for years been their top priority. It is also a focus of academic research, as shown by a large number of papers, books, and research reports. On the other hand, the BA domain suffers from several incorrect, imprecise, and incomplete notions. New areas and concepts emerge quickly; making it difficult to ascertain their structure. BA-related taxonomies play a crucial role in analyzing, classifying, and understanding related objects. However, according to the literature on taxonomy development in information systems (IS), in most cases the process is ad hoc. BA taxonomies and frameworks are available in the literature; however, some are excessively general frameworks with a high-level conceptual focus, while others are application or domain-specific. Our paper aims to present a novel semi-automatic method for taxonomy development and maintenance in the field of BA using content analysis and text mining. The contribution of our research is threefold: (1) the taxonomy development method, (2) the draft taxonomy for BA, and (3) identifying the latest research areas and trends in BA.
机译：到2018年，全球知识委员会认为的业务分析（BA）具有战略重要性，多年来一直是他们的首要任务。它也是学术研究的重点，如大量文件，书籍和研究报告所示。另一方面，BA域遭受了几种不正确，不精确和不完整的概念。新地区和概念很快出现;使其难以确定其结构。 BA相关分类在分析，分类和理解相关对象方面发挥着至关重要的作用。然而，根据信息系统（是）的分类发育的文献，在大多数情况下，该过程是临时。文献中提供BA分类和框架;但是，有些是过度普遍的框架，具有高级别的概念焦点，而其他人则是应用程序或特定于域的。我们的论文旨在使用内容分析和文本挖掘为BA领域的分类和维护提供一种新的半自动方法。我们的研究贡献是三倍：（1）分类学发展方法，（2）BA草案，（3）识别BA的最新研究领域和趋势。

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