Name
Abstract | ||
|---|---|---|
Growth and trends in recent data visualization research show that fact that after maturity of the tools allowing the end-user to explore through data, the next logical step is to focus on data analysis and presentation. The main criterion of setting up a presentation is to balance the relevancy level of data exposure and interaction within the story arc. Commonly, this is the author's role to write a single story and make it memorable and effective for the targeted audience. Considering the existing level of personal information that can be extracted from social networks, a unique opportunity is to get to know the audience before developing the story. Applying this theory in a system creates the possibility of crafting separate personalized visualization for each targeted individual. In this paper, we propose a foundation to a framework to generate the ultimate recommender story based on the given objectives and balanced level of detail in the visualization using the extracted user's information from social networks. This will be the next rational step towards enhancing the tools to assist the critical role of aiding decision making and education processes using visualization. |
Year | Venue | Keywords |
|---|---|---|
2013 | CASCON | social network,visualization research show,single story,separate personalized visualization,data exposure,data analysis,recent data,ultimate recommender story,balanced level,story arc,recommender narrative visualization |
Field | DocType | Citations |
Data visualization,Social network,Information visualization,Computer science,Visualization,Level of detail,Personally identifiable information,Narrative visualization,Distributed computing | Conference | 1 |
PageRank | References | Authors |
0.40 | 9 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amirsam Khataei | 1 | 1 | 1.41 |
| Diana Lau | 2 | 1 | 0.40 |