Abstract
In the field of data mining and knowledge discovery, scientometric is an emerging research area for technical publications. Knowledge discovery from the publications and their profiles is of interest across academic and scientific world. Such knowledge is utilized for policy-making and various decisions about institutions and individuals both. Thus, various metrics as well as scientific databases have been designed for analyzing the publication data. In this paper, we examine such indices and data sources, and their benefits and limitations. We argue that ranking of a journal varies with respect to databases. Our study is limited for Computer Science (CS) sub-fields. Therefore, we explore ranking of journals of CS sub-field-wise with respect to such databases. Further, we carry-out a comparative study of ranking of journals from three CS sub-fields. We empirically found that the relation between the ranking of journals, e.g. Scopus versus Google Scholar, is valid between 36% to 81% in CS sub-domains.
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Kumari, P., Kumar, R. (2022). Scientometrics and Publications: A Comparative Study of Ranking of Multi-source Databases. In: Kumar, A., Senatore, S., Gunjan, V.K. (eds) ICDSMLA 2020. Lecture Notes in Electrical Engineering, vol 783. Springer, Singapore. https://doi.org/10.1007/978-981-16-3690-5_71
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DOI: https://doi.org/10.1007/978-981-16-3690-5_71
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