1. 1 Email Viz Future Directions Marti Hearst UC Berkeley

2. 2 Outline Important Infoviz Principle Tough Data Mining Problem –The infrequent important thing Interfaces tailored to user goals –Intelligence Analysts –Investigative Reporters Promising Future Directions –Integration of task, viz, and content analysis –Mixed-Initiative Interaction

3. 3 Important InfoViz Principle Distinguish between: PRESENTATIONANALYSIS

4. 4 Tough Data Mining Problem It’s easy to see the main trends But often we want the rare but unexpected and important event: –Russian oil company example –Schwarzenegger and Enron –Cigarettes and kids –Person on the periphery who is working stealthily to influence things Deep throat

5. 5 Intelligence Analysts

6. 6 Interviews wit active counter-terrorist analysts Great diversity in –Goals –Computing environments Biggest problems are social/systemic Many mundane IT problems as well

7. 7 Mundane IT Problems System incompatibilities Data reformatting Data cleaning Documenting sources Archiving materials

8. 8 Intelligence Analysts: Problem 1 Look at a series of reports, images, communication patterns; Try to build a model of what is going on –Follow leads –Compare to previous situations Recent problem: –Groups are changing their behavior patterns quickly Very little use of sophisticated software tools

9. 9 Intelligence Analysts: Problem 2 Given a large collection “Roll around” in the data –See what has been “touched” Tools should indicate which parts of the collection have been examined and which have yet to be looked at, and by whom –View data in several different ways Data reduction methods such as MDS, SVD, and clustering often hide important trends.

10. 10 Intelligence Analysts: Problem 2 –Don’t show the obvious e.g., Cheney is president –Don’t show what you’ve already shown –Only show the most recent version –Show which info is not present Changes in the usual pattern Something stops happening

11. 11 Intelligence Analysts: Problem 3 Prepare a very short executive summary for the purposes of policy making –Really the culmination of a cascade of summaries –Reps from different agencies meet and “pow-wow” to form a view of the situation –Rarely, but crucially, must be able to refer back to original sources and reasoning process for purposes of accountability

12. 12 Investigative Reporter Example Looking for trends in online literature Create, support, refute hypotheses

13. 13 Investigative Reporter Example What are the current main topics? What are the new popular terms? How do they track with the news? Clustering Corpus-level statistics, Co-occurrence statistics Contrasting collection statistics

14. 14 Investigative Reporter Example How long after a new Star Trek series comes on the air before characters from the series appear in stories? How often do Klingons initiate attacks against Vulcans, vs. the converse? Named-entity recognition Creating a list of terms Apply the list to a Subcollection Create regex rules with POS information

15. 15 Integration TAKMI, by Nasukawa and Nagano, IBM systems Journal 40(4), 2001 The system integrates: –Real tasks (CRM, patent analysis) –Content analysis –Information Visualization

16. 16 TAKMI, by Nasukawa and Nagano, 2001 Docs containing “windows 98”

17. 17 CRM TAKMI, by Nasukawa and Nagano, 2001

18. 18 TAKMI, by Nasukawa and Nagano, 2001

19. 19 TAKMI, by Nasukawa and Nagano, 2001

20. 20 TAKMI, by Nasukawa and Nagano, 2001

21. 21 TAKMI, by Nasukawa and Nagano, 2001

22. 22 TAKMI, by Nasukawa and Nagano, 2001

23. 23 Mixed-Initiative Interaction Balance control between user and agent –In Spotfire demo, system adjusts axes after “other” category hidden –EDA: User selects a subset of data based on interesting- looking grouping System then does stats on this subset in the background while user continues to work Then system notifies user of interesting trends See the AIDE system: –St. Amant, R., Dinardo, M. D., and Buckner, N. (2003). Balancing Efficiency and Interpretability in an Interactive Statistical Assistant. Proceedings of IUI.