 Problem:  How to discover the latent structure in unstructured data (e.g. Wikipedia articles).  Objective:  Improve the ways people explore and analyze.

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Presentation transcript:

 Problem:  How to discover the latent structure in unstructured data (e.g. Wikipedia articles).  Objective:  Improve the ways people explore and analyze hundreds of thousands Wikipedia articles.  Methods (cont.) :  Extract topics that summarize 189,000 Wikipedia articles  Extract time and location from the articles  Visually present the results in a meaningful way.

Collect Data Extract Topic Summaries Extract Location and Date Visualize Results

Map View Views can include: Imagery Topographic Street layer Oceanic

Support View Tabs can show: Separate map Link to Wikipedia article List of searchable topics

Timeline Navigation slider shows user’s range Graph shows number of articles on topic at the time

 Havre et al. “ThemeRiver Visualizing Thematic Changes in large Document Collections”  Specialty: Large amounts of data over time.

 Incorporating annotations Birth of Christ (0 AD) Conquest of Dacia (106 AD)

 With annotations in the proper coordinates

 With annotations displayed

 Potential data applications include:  Military  Corporate  Government  Medical  Consumer  Try for yourself at: 