A Systematic Survey of Business Intelligence Literature Using Machine Learning Techniques

E. Houstis, G. Fakas, M. Vavalis
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Abstract

Business intelligence (BI) comprises the strategies and technologies used by enterprises for the data analysis of business information for assisting its management and decision process. The principal aims of this study are: a) to complement the existing literature surveys in the BI area by identifying publications for the period 2007 to 2020, b) to classify these publications according to nine research strategies, c) to classify them according to various well-defined research topic categories, and d) apply machine learning techniques to assess their relevance with the BI field. We have collect 332 papers using ‘Google Scholar’ and a set of related keywords to the field of BI. The results show that most of the papers appeared during the years 2015 and 2017 [1]. The classifications of the literature based on research strategies and research topic indicate that most papers address formal theory and/or reviews and belong to the “benefits” research topic category. Finally, we attempt to increase the accuracy of the classification of aggregated information by utilizing machine learning techniques and estimating the “relevance” with respect to BI discipline. It appears that the overall best individual classifier is the SVM polynomial with ROSE sampling on 80% of the original data using the ‘TDM’ feature space, following by the Neural Networks with ROSE sampling on 80% of the original data using the ‘Topic’ feature space. We furthermore apply various ensemble methods, and we estimate and analyze their performance
使用机器学习技术的商业智能文献系统调查
商业智能(BI)包括企业用于业务信息数据分析的策略和技术,以协助其管理和决策过程。本研究的主要目的是:a)通过确定2007年至2020年期间的出版物来补充商业智能领域的现有文献调查,b)根据九种研究策略对这些出版物进行分类,c)根据各种定义明确的研究主题类别对它们进行分类,d)应用机器学习技术来评估它们与商业智能领域的相关性。我们使用“Google Scholar”和一组与BI领域相关的关键字收集了332篇论文。结果显示,大部分论文出现在2015年和2017年[1]。基于研究策略和研究主题的文献分类表明,大多数论文涉及形式理论和/或综述,属于“利益”研究主题类别。最后,我们试图通过利用机器学习技术和估计与BI学科相关的“相关性”来提高汇总信息分类的准确性。总的来说,最好的个体分类器是使用“TDM”特征空间对80%的原始数据进行ROSE采样的SVM多项式,其次是使用“Topic”特征空间对80%的原始数据进行ROSE采样的神经网络。我们进一步应用了各种集成方法,并对它们的性能进行了估计和分析
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