Interfaces for Intense Information Analysis

1. Interfaces for Intense Information Analysis Marti Hearst UC Berkeley This research funded by ARDA

2. Outline • A contrast • Search vs. Analysis • Goals for three user groups • Intelligence Analysts • Biomedical Researchers • Investigative Reporters • Our current interface design

3. Search vs. Analysis Search: Finding hay in a haystack Analysis: Creating new hay

4. UIs for Search vs. Analysis • Search: • A necessary but undesirable step in a larger task • UI should not draw attention to itself • UI should be very easy to use for everyone • Analysis: • The larger task • UI can be more of a “science project” • But UI should have “flow”

5. General Goals • Support hypothesis formation / refutation • Flow • Easy creation, destruction, and cataloging of connections and coverage • Easy movement between multiple views • Represent: • Multiple supporting clues • Conflicting evidence • Uncertainty • Timeliness • Non-monotonicity

6. Intelligence Analysts

7. Intelligence Analysts • I have recently interviewed several active counter-terrorist analysts • Great diversity in • Goals • Computing environments • Biggest problems are social/systemic • Many mundane IT problems as well

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

9. 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

10. 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.

11. 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

12. 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

13. BioInformatics Researchers

14. BioInformatics Example 1 • How to discover new information … • … As opposed to discovering which statistical patterns characterize occurrence of known information. • Method: • Use large text collections to gather evidence to support (or refute) hypotheses • Make Connections • Gather Evidence

15. Etiology Example • Don Swanson example, 1991 • Goal: find cause of disease • Magnesium-migraine connection • Given • medical titles and abstracts • a problem (incurable rare disease) • some medical expertise • find causal links among titles • symptoms • drugs • results

16. Gathering Evidence stress CCB migraine magnesium magnesium PA SCD magnesium magnesium

17. CCB PA SCD stress Gathering Evidence migraine magnesium

18. Swanson’s Linking Approach • Two of his hypotheses have received some experimental verification. • His technique • Only partially automated • Required medical expertise

19. BioInformatics Example 2: • How to find functions of genes? • Have the genetic sequence • Don’t know what it does • But … • Know which genes it coexpresses with • Some of these have known function • So …infer function based on function of co-expressed genes • This is problem suggested by Michael Walker and others at Incyte Pharmaceuticals

20. Gene Co-expression:Role in the genetic pathway Kall. Kall. g? h? PSA PSA PAP PAP g? Other possibilities as well

21. Make use of the literature • Look up what is known about the other genes. • Different articles in different collections • Look for commonalities • Similar topics indicated by Subject Descriptors • Similar words in titles and abstracts adenocarcinoma, neoplasm, prostate, prostatic neoplasms, tumor markers, antibodies ...

23. Formulate a Hypothesis • Hypothesis: mystery gene has to do with regulation of expression of genes leading to prostate cancer • New tack: do some lab tests • See if mystery gene is similar in molecular structure to the others • If so, it might do some of the same things they do

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

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

26. Investigative Reporter Example Named-entity recognition Creating a list of terms Apply the list to a Subcollection Create regex rules with POS information • 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?

27. Summary Query Analysis Term Set Document Set All terms: * a c u y m z x x Diseases: emphysema cancer hypertension … New Merge All documents: * WHO: organization = world health organization LINDI File Help

28. Thank you! bailando.sims.berkeley.edu/lindi.html For more information: