Authors:
Florian Windhager
1
;
Albert Amor-Amorós
2
;
Michael Smuc
1
;
Paolo Federico
2
;
Lukas Zenk
1
and
Silvia Miksch
2
Affiliations:
1
Danube University Krems, Austria
;
2
Vienna University of Technology, Austria
Keyword(s):
Information Visualization, Visual Analytics, Patent Data, Emergent Technologies, Dynamic Networks.
Related
Ontology
Subjects/Areas/Topics:
Abstract Data Visualization
;
Computer Vision, Visualization and Computer Graphics
;
Databases and Visualization, Visual Data Mining
;
General Data Visualization
;
Graph Visualization
;
Information and Scientific Visualization
;
Time-Dependent Visualization
;
Visual Data Analysis and Knowledge Discovery
;
Visualization Applications
Abstract:
Patents, archived as large collections of semi-structured text documents, contain valuable information about historical trends and current states of R&D fields, as well as performances of single inventors and companies. Specific methods are needed to unlock this information and enable its insightful analysis by investors, executives, funding agencies, and policy makers. In this position paper, we propose an approach based on modelling patent repositories as multivariate temporal networks, and examining them by the means of specific visual analytics methods. We illustrate the potential of our approach by discussing two use-cases: the determination of emerging research fields in general and within companies, as well as the identification of inventors characterized by different temporal paths of productivity.