Business Intelligence

Journal Visualization by a Dual Hierarchical Data Visualization Technique

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Executive Summary

Researchers often search for specific papers from journals, or look over the trends of the journals. This task is not always easy: for example, various kinds of papers may be extracted if the general terms are use for query operations, and it may prevent to discover the interesting papers. This poster presents a journal visualization technique, applying the dual hierarchical data visualization technique. The technique firstly extracts meaningful keywords from a journal, and calculates importance of the keywords for each paper or author. It finally represents the distribution of keywords, papers, and authors by the own dual hierarchical data visualization technique.

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