Visualization of big data scientific productions in the citation database Web of Science

Document Type : Original Article

Authors

university of Qom

Abstract

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Objective: This research was conducted with the aim of visualizing scientific productions related to big data in the Web of Science citation database.

Method: This study is an applied research conducted with a scientometric approach. Data extraction was performed by searching the term "Ts=Big Data" in the advanced search section of Web of Science, resulting in a total of 1,053 records. The statistical population of this research includes all data related to Iran's scientific productions in this citation database concerning big data. After extraction, the data were imported into scientometric software. Tools such as VOSviewer, BibExcel, and UCINET were used. VOSviewer was utilized for mapping, BibExcel for identifying co-occurrence of terms, and UCINET for analyzing the relationships between keywords.

Content and Findings: The findings revealed that a total of 1,053 records related to big data from Iran have been indexed in this citation database. The first article dates back to 2013. Iran's most frequent collaborations in writing articles were with the United States, Australia, and the United Kingdom. All scientific productions were in English, and Islamic Azad University, with 240 records, had the highest number of scientific productions. Among the authors, Amir Mousavi from Obdu University, with 48 records, 1,851 citations, and an H-Index of 26, ranked first.
In this study, the term "big data" from Cluster 2, with 374 occurrences, was the most frequent keyword. Findings from this cluster indicated that, in addition to this term, words such as "machine learning," "deep learning," "artificial intelligence," and "internet of things" were also high-frequency and received significant attention. A total of 1,053 documents were retrieved for this research.
Overall, the data consisted of 4 clusters, with Cluster 2, containing 20 keywords, being the largest.

Conclusion: Analyzing and visualizing scientific productions related to big data in the Web of Science citation database can help better understand research trends, identify key concepts, and explore relationships between them. By using scientometric methods and visualization tools, more precise analysis and visual representation of information become possible. This approach can significantly enhance the quality of research and facilitate strategic scientific decision-making.

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References

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