Text Mining Techniques to Capture Facts for Cloud Computing Adoption and Big Data Processing

Digital libraries, journals and conference proceedings repositories are a great source of information. These sources are very useful for the purpose of research and development. This paper presents an overview of text mining and its application towards information extraction from literature. In this study, we used word cloud, term frequency analysis, similarity analysis, cluster analysis, and topic modeling to extract information from multi-domain research articles. Cloud computing and big data are new emerging trends. So it is important to extract useful patterns and knowledge from published articles in these domains and discover the relationship between them. Therefore, a total of two hundred research articles published from 2010 to 2018 in these domains, were selected. The source of these articles is high impact factor journals from reputed publishers namely IEEE, Springer, Wiley, Elsevier, and ACM. It is a cross-domain analysis in cloud computing and big data domains to find the latest trends, related topics, tools, terms, and author affiliation from extracted data. This study identifies the ten major areas of big data using cloud computing, fourteen factors towards cloud adoption, and hurdles in adoption. Moreover finding shows that IEEE has more sources for subject cloud computing application towards big data, then comes Springer, Wiley, and Elsevier. Furthermore, it has been observed in the analysis that the number of articles in these domains increased from 2013 onward.