1 / 17 visualization of gtd and multimedia remco chang charlotte visualization center unc charlotte
Post on 03-Jan-2016
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Visual GTD Flow Chart
Entity Relationships(Geo-temporal Vis)
Dimensional Relationships(ParallelSets)
Entity Analysis(Search By Example)
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Parallel Sets View
• Parallel Sets– Displays
relationships among categorical dimensions
– Shows intersections and distributions of categories
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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
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Entity Comparison
• Uses the algorithm “Longest Common Subsequence” (LCS) to identify similar patterns
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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
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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
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Integrating Terrorism Data Analysisand News Analysis
Terrorism Databases
Terrorism Visual
Analysis
News Story Databases
News Visual
Analysis
Jigsaw
TerrorismVA
BroadcastVA
Stab/TIBORReasoningEnvironment
Framing,Affective Analysis
NVAC
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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
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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
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