1. Professor John Canny Spring 2003
CS 160: Lecture 24
Professor John Canny
Spring 2003
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2. Preamble
Quiz on last lecture.
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3. Information Architecture
The OAI model (lecture 20) was our starting point for information organization.
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4. Information Tasks Specific Fact-finding: Extended Fact-finding:
Find the phone number of Bill Clinton
Extended Fact-finding:
What kinds of music is Sony publishing?
Open-ended browsing:
Is there new work on voice recognition in Japan?
Exploration of availability:
What genealogy information is at the National Archives?
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5. Database queries
Query languages like SQL are widely used, but are hard to learn and easy to make mistakes with.
SELECT DOCUMENT# FROM JOURNAL-DB
WHERE (DATE >= 1994 AND DATE <= 1997)
AND (LANGUAGE = ENGLISH OR FRENCH)
AND (PUBLISHER = ASIS OR HFES OR ACM)
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6. Visual Query Builders
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7. QBE: Query By Example
User chooses a record (Database) or document (search engine) and specifies “more like this”.
User can also pick a segment of text, even a paragraph, from a good document and use it as a search query (search engines only).
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8. Visualizing Search Results
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9. Multidimensional Scaling
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10. Multidimensional Scaling
Multi-Dimensional Scaling (MDS) is a general technique for displaying n-dimensional data in 2D.
It preserves the notion of “nearness”, and therefore clusters of items in n-dimensions still look like clusters on a plot.
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11. Multidimensional Scaling
MDS applied to hand-classified discussion topics.
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12. Multidimensional Scaling
Clustering of the MDS datapoints (discussion topics)
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13. Discussion
Try to assign labels to (positive and negative) X and Y axes in the previous plot.
Note that X and –X may not be opposites in the usual sense –
this is an artifact of linear projection methods.
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14. Multidimensional Scaling
MDS can be applied to search engine results easily because they automatically have a high-dimensional representation (used internally by the search engine).
The MDS plot helps organize the data into meaningful clusters. You can search either near your desired result, or scan for an overview.
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15. Tasks for a visualization system
Overview: Get an overview of the collection
Zoom: Zoom in on items of interest
Filter: Remove uninteresting items
Details on demand: Select items and get details
Relate: View relationships between items
History: Keep a history of actions for undo, replay, refinement
Extract: Make subcollections
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16. Visualization principles
To support tasks 1 & 2, a general design pattern called “focus+context” is often used.
Idea is to have a focal area at high resolution, but keep all of the collection at low resolution.
Mimics the human retina.
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17. Distortion
Several visualization systems use distortion to allow a focus+context view.
“Fisheye lenses” are an example of strongly enlarging the focus while keeping a lot of context (sometimes the entire dataset).
Many of these were developed at Xerox PARC.
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18. Focus+Context: Document lens
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19. Focus+Context: Webbook lens
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20. Focus+Context: Table lens
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21. Focus+Context: Datelens
Datelens is a PDA calendar program developed at U. Maryland (Bedersen et al.)
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22. Navigation: Hyperbolic trees
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23. Navigation: Hyperbolic trees
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24. Navigation: Hyperbolic trees
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25. Navigation: Hyperbolic trees
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26. Navigation: Hyperbolic trees
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27. Navigation: Animation
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28. Network visualization
Often use mass-spring dynamic models.
Can be animated and interacted with.
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29. Discussion
Animation is used in viz schemes to smooth transitions, or increase “aliveness” of the display.
Discuss advantages/disadvantages of animation.
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30. Using 3D
People perceive a 3D world from 2D views, so it seems like we could use 3D structure to advantage.
Several systems (also Xerox PARC) have tried this.
Use 3D spatial memory and organization to speed up navigation.
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31. WebBook
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32. Web Forager
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33. Representing Hierarchies
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34. Summary
High-dimensional data can be visualized by 2D via MultiDimensional Scaling.
Focus+Context is a design pattern for showing the area of interest and its relationship to the entire dataset.
3D techniques can leverage the spatial capabilities of the human visual system.
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