IBM System G Social Media Solution: Analyze Multimedia Content, People, and Network Dynamics in Context
Ching-Yung Lin, Danny Yeh, Nan Cao, Jui-Hsin Lai, Chun-Fu (Richard) Chen, Conglei Shi, Jie Lu, Jason Crawford, Keith Houck, Yinglong Xia, Sabrina Lin, Richard B. Hull, Fenno F. Heath III, Piyawadee Sukaviriya, SweeFen Goh
We introduce time curves as a general approach for visualizing patterns of evolution in temporal data. Examples of such patterns include slow and regular progressions, large sudden changes, and reversals to previous states. These patterns can be of interest in a range of domains, such as collaborative document editing, dynamic network analysis, and video analysis. Time curves employ the metaphor of folding a timeline visualization into itself so as to bring similar time points close to each other. This metaphor can be applied to any dataset where a similarity metric between temporal snapshots can be defined, thus it is largely datatype-agnostic. We illustrate how time curves can visually reveal informative patterns in a range of different datasets.