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Patent Big Data Analysis using Graph Theory

Authors: Sunghae Jun;

Patent Big Data Analysis using Graph Theory

Abstract

Patent big data analysis is to analyze patent document data by big data technologies. A patent document contains diverse and complete information of researched and developed technology, such as patent title and abstract, patent number, issued date, claim, drawing, etc. The velocity of increasing the number of applied patents is so fast. So, patent data have characteristic of big data, 3V (volume, variety, and velocity). In this paper, we propose a graph model for patent analysis because visualization by graph theory is representative method for big data. We can apply the results of patent analysis to R&D planning and technological innovation for company and nation.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
2
Average
Average
Average
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