Name
Abstract | ||
|---|---|---|
Patents, archived as large collections of semi-structured text documents, contain valuable information about historical trends and current states of Ru0026D 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. |
Year | Venue | Field |
|---|---|---|
2015 | IVAPP | Data science,Data mining,Network dynamics,Multivariate statistics,Computer science,Visualization,Position paper,Visual analytics |
DocType | Citations | PageRank |
Conference | 1 | 0.34 |
References | Authors | |
6 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Florian Windhager | 1 | 41 | 9.56 |
| Albert Amor-Amoros | 2 | 12 | 2.58 |
| Michael Smuc | 3 | 79 | 7.58 |
| Paolo Federico | 4 | 52 | 5.20 |
| Lukas Zenk | 5 | 29 | 2.03 |
| Silvia Miksch | 6 | 2212 | 174.85 |