实证大数据研究：一个系统的文献映射
Empirical Big Data Research: A Systematic Literature Mapping
摘要：
背景：大数据是一个相对较新的研究和技术领域，文献报道了各种各样的大数据概念。一个研究领域的成熟程度可以用包含实证结果的出版物的数量来衡量。本文介绍了大数据实证研究的现状。方法：采用系统制图法，对所收集的文献按品种、数量和速度进行制图。此外，我们还讨论了大数据的应用领域。结果如下：我们发现，151的评估1778贡献包含一种形式的经验结果，可以映射到一个或多个的3V的和59地址的应用领域。结论：与整个计算机科学研究相比，包含实证结果的出版物的份额远低于平均水平。为了使大数据研究更加成熟，我们建议采用实证方法来增强对报告结果的信心。根据我们的趋势分析，我们认为数量和多样性是大数据中最有前途的未知领域。
英文摘要：
Background: Big Data is a relatively new field of research and technology, and literature reports a wide variety of concepts labeled with Big Data. The maturity of a research field can be measured in the number of publications containing empirical results. In this paper we present the current status of empirical research in Big Data. Method: We employed a systematic mapping method with which we mapped the collected research according to the labels Variety, Volume and Velocity. In addition, we addressed the application areas of Big Data. Results: We found that 151 of the assessed 1778 contributions contain a form of empirical result and can be mapped to one or more of the 3 V's and 59 address an application area. Conclusions: The share of publications containing empirical results is well below the average compared to computer science research as a whole. In order to mature the research on Big Data, we recommend applying empirical methods to strengthen the confidence in the reported results. Based on our trend analysis we consider Volume and Variety to be the most promising uncharted area in Big Data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
