This document summarizes two interfaces presented as part of a VAST challenge solution: a News Analyzer and Email Analyzer. The News Analyzer allows exploration of news articles to identify events using search, entity recognition, and visualization tools. The Email Analyzer visualizes email networks through a co-occurrence matrix, radial graph, and word cloud to examine relationships. Both interfaces were designed specifically for the challenge and utilize techniques like search engines, named entity recognition, spectral co-clustering, and D3 visualization.

1. Safeguarding Abila through Multiple Data Perspectives
VAST 2014 Grand Challenge Award: Effective Analysis and Presentation
VAST 2014 Mini Challenge 2 Award: Honorable Mention for Effective Presentation
Parang Saraf; Patrick Butler; Naren Ramakrishnan
Discovery Analytics Center, Department of Computer Science, Virginia Tech
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Presented By: Parang Saraf

2. System Hosted At:
http://embers.cs.vt.edu:60051
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3. VAST Challenge Solution From DAC
• The DAC solution offers three key advantages:
1. Provides an efficient front-end interface for user-centered
exploration of data
2. Very little analysis or cleaning of data is performed in the
backend, thereby helping an analyst to understand the
data better
! Example: Faulty news sources or GPS coordinates are displayed
3. Offers an intuitive interface to present data in several
different ways
• Each Interface was designed from scratch
specifically for the VAST Challenge
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4. Mini Challenge - I
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5. Mini Challenge - I
• Two Interfaces:
– News Analyzer
• Helps an Analyst
explore news articles
and Identify Events
– Email Analyzer
• Helps an Analyst
visualize and examine
Email network
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6. News Analyzer
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7. News Analyzer
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8. News Analyzer
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9. News Analyzer
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10. News Analyzer
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11. News Analyzer
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12. News Analyzer
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13. News Analyzer
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14. News Analyzer
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15. News Analyzer
Example: How POK Leadership has changed over Time
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1998 – 1999

16. News Analyzer
Example: How POK Leadership has changed over Time
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1998 – 1999
2000 – 2009

17. News Analyzer
Example: How POK Leadership has changed over Time
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1998 – 1999
2000 – 2009
2000 – 2014

18. News Analyzer
Packages Used
• Search Engine
– Whoosh (Python search engine library)
• Provides Logical Query of Articles
• Returns Similar articles
• Named Entity Recognition
– Stanford NER Parser
• Provides Person, Organization and Locations in the input document set
• Visualization
– NVD3
• Line Chart
– D3
• Word Clouds
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19. Email Analyzer
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20. Email Analyzer
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21. Email Analyzer
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22. Email Analyzer
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23. Email Analyzer
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24. Email Analyzer
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25. Email Analyzer
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26. Email Analyzer
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Spectral Co-Clustering
• Given an n x m matrix of n
documents and m words, the
algorithm performs co-clustering of
documents and words.
• The clustering problem is posed in
terms of finding minimum cut vertex
partitions in a bipartite graph
between document and words
• We provide an m x m matrix where
rows and columns denote
employees and a cell denotes the
number of emails exchanged
• Implemented using the scikit-learn
package

27. Email Analyzer
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28. Email Analyzer
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29. Email Analyzer
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30. Email Analyzer - Example
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31. Email Analyzer - Example
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32. Email Analyzer - Example
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33. Email Analyzer
Packages Used
• Co-Occurrence Matrix
– Spectral Co-Clustering Algorithm
– Visualization
• D3
– Email Radial, Co-Occurrence matrix and Word Cloud
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34. Thank You
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