Abstract
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.
Highlights
In the current era, most of the information is stored in the form of e-documents
Similarity analysis in terms of category shows that these articles are not exactly similar but interlinked in meaning in the context of their domains
1st category, as per the study [29], we analyzed keywords that appeared in topics and cluster centroid and identified the big data areas discussed in these articles, in which cloud computing technology is used for data processing. These are Enterprise big data, health care,business intelligence, bioinformatics, medical images, remote sensing image processing, telehealth, CCTV application data, and education learning system, mobile cloud processing, IOT big data, traffic hotline GPS data, big data image processing and internet of vehicles (IOV) big data processing. (Appendix Table 14 (Topic 1-10 and 18) and Table 12). (Topic 11-17 and Topic19 -24) (Appendix A (Table 14) and Table 11) we identified tools, techniques, terms, and algorithms discussed in the context of the application of the cloud computing model towards big data processing
Summary
Most of the information is stored in the form of e-documents This practice is adopted by several business organizations, institutes, media, and others. The scientific literature is stored mostly in the form of e-documents in digital libraries, conference proceedings repositories, and journal databases This is a great source of information and it provides the basis for future developments. U. Haq et al.: Text Mining Techniques to Capture Facts for Cloud Computing Adoption and Big Data Processing several steps. The analysis stage includes the semantic analysis and other techniques to get data processed according to the requirements The result of this phase can be stored in the database management system for further processing. This research focuses on IE of cloud computing application towards big data and cloud adoption trends using text mining techniques on the published research papers in these domains. We explain in detail the research methodology, in the IV section we discuss the experiments and results, and in V section we have a conclusion
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
