Visualisation of massive data from scholarly Article and Journal Database: A Novel Scheme

Abstract: Scholarly articles publishing and getting cited has become a way of life for academicians. These scholarly publications shape up the career growth of not only the authors but also of the country, continent and the technological domains. Author affiliations, country and other information of an author coupled with data analytics can provide useful and insightful results. However, massive and complete data is required to perform this research. Google scholar which is a comprehensive and free repository of scholarly articles has been used as a data source for this purpose. Data scraped from Google scholar when stored as a graph and visualized in the form of nodes and relationships, can offer discerning and concealed information. Such as, evident domain shift of an author, various research domains spread for an author, prediction of emerging domain and sub domains, detection of journal and author level citation cartel behaviours etc. The data from graph database is also used in computation of scholastic indicators for the journals. Eventually, econometric model, named Cobb Douglas model is used to compute the journal�s Modeling Internationality Index based on these scholastic indicators. Keywords: Data acquisition methods, Web scraping, Graph database, Neo4j, Data visualization, Cobb Douglas model.