1. 1 / 17 Visualization of GTD and Multimedia Remco Chang Charlotte Visualization Center UNC Charlotte

2. 2 / 17 Visual GTD Flow Chart Entity Relationships (Geo-temporal Vis) Dimensional Relationships (ParallelSets) Entity Analysis (Search By Example)

3. 3 / 17 Five Flexible Entry Components

4. 4 / 17 Seeing Patterns… FARC showing an outlier Unusual temporal pattern of NPA

5. 5 / 17 Parallel Sets View Parallel Sets – Displays relationships among categorical dimensions – Shows intersections and distributions of categories

6. 6 / 17 Parallel Sets View Dynamic filtering on continuous dimensions can show more information Here we see the large proportion of facility attacks and bombings in Latin America during the early 1980s

7. 7 / 17 ParallelSets - Framing

8. 8 / 17 Entity Comparison Uses the algorithm “Longest Common Subsequence” (LCS) to identify similar patterns

9. 9 / 17 Grouping using MDS in 2D Each o represents a terrorist group Groups form cluster according to naturally occurring trend sizes Clusters are easily visible MDS Analysis by Country

10. 10 / 17 Auto Video Extraction

11. 11 / 17 Multimedia Visual Analysis

12. 12 / 17 Concept Graph

13. 13 / 17 Video Analysis Example CNN Fox News MSNBC News contains view points and opinions Find local, regional, national, and international reports of the same event to get a complete picture

14. 14 / 17 News Lens

15. 15 / 17 Integrating Terrorism Data Analysis and News Analysis Terrorism Databases Terrorism Visual Analysis News Story Databases News Visual Analysis Jigsaw Terrorism VA Broadcast VA Stab/ TIBOR Reasoning Environment Framing, Affective Analysis NVAC

16. 16 / 17 Future Work Event-based video analysis Smart Visual GTD – Collaboration with Daniel Kiem (Univ Konstanz, Germany) – Multimedia Analysis Collaboration with PNNL (A. Sanfilipo, W. Pike) Analyzes (layout of) webpages, videos, images, and unstructured texts. Tracking temporal changes

17. 17 / 17 Questions? Thank you! rchang@uncc.edu http://viscenter.uncc.edu

18. 18 / 17 Backup

19. 19 / 17 Entity Comparison Two strings of data (each representing a series of events) – GATCCAGT – GTACACTGAG Basic algorithm returns length of longest common subsequence: 6 Can return trace of subsequence if desired: – GTCCAG GATCCAGT GTACACTGAG Additional variations can take into account event gap penalties, time gap penalties, and exploration of shorter, or alternate, common subsequences

20. 20 / 17 ParallelSets - Framing