Deciphering Big Data Research Themes
Abstract
Big Data is a relatively novel research field that has attracted high interest from the industry and academia for its wide applicability. Numerous definitions of Big Data have been given by scholars from different perspectives. We think the Big Data research field could be better appreciated by analyzing the relevant scientific articles published over the years. However, the sheer volume of the Big Data related literature needs a more efficient way to analyze them. As such, we utilize the knowledge domain analysis techniques developed by information scientists to build the intellectual structure and uncover the main research themes, which afford us a holistic view of the overall Big Data research field. Based on our analysis, the research themes of Big Data may be classified into four main categories: the first one deals with the technologies and architectures aspect of Big Data; the second one relates to the prospective applications of the Big Data analytics; the third one covers levels of parallelism in the Big Data processing stacks; the rest encompasses mostly machine learning related studies and some miscellaneous topics that may benefit from the Big Data processing capabilities.
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