Name
Abstract | ||
|---|---|---|
The most commonly used interaction techniques in space-filling visualization are drilling-down + semantic-zooming and focus + context methods. However, under these schemes, users often have insufficient knowledge about contextual information to guide them exploring through very large and deep hierarchical structures. This paper proposes an efficient interaction method called "chain-context view" (CCV) for the navigation in space-filling visualizations. Instead of displaying a no or one context views, we provide users with a progressive sequence of context views, which maximize the display area of contextual information. The rich contextual information provided in the exploration path could greatly increase the accuracy of user's decisions and reduce the "unsuccessful trips" and "unnecessary views" while locating the target object by browsing in deep levels of hierarchical structures with CCVs. The new method allows the users to trace each step of their interactions and make it easy to jump or return to any level of the hierarchy that they have previously visited. A usability study was conducted to evaluate the effectiveness of the CCV, by measuring the user performance and satisfaction on the navigation of deep levelled relational structures. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/s12650-015-0329-3 | J. Visualization |
Keywords | Field | DocType |
Information visualization,Interaction,Focus + context,Chain-context | Graph,Information visualization,Information retrieval,Computer science,Context model | Journal |
Volume | Issue | ISSN |
19 | 3 | 1343-8875 |
Citations | PageRank | References |
1 | 0.35 | 26 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jie Liang | 1 | 86 | 10.85 |
| Mao Lin Huang | 2 | 736 | 80.10 |
| Quang Vinh Nguyen | 3 | 232 | 32.97 |