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

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Presentation transcript:

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

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

3 / 17 Five Flexible Entry Components

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

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

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 / 17 ParallelSets - Framing

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

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 / 17 Auto Video Extraction

11 / 17 Multimedia Visual Analysis

12 / 17 Concept Graph

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 / 17 News Lens

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 / 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 Questions? Thank you!

18 / 17 Backup

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 / 17 ParallelSets - Framing