Text and Documents 1
CS 7450 - Information Visualization March 16, 2004
John Stasko
Text is Everywhere
• We use documents as primary information artifact in our lives
• Our access to documents has grown tremendously in recent years due to networking infrastructure
− WWW
− Digital libraries
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Big Question
• What can information visualization provide to help users in gathering information from text and document collections?
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InfoVis Tasks
• Two main tasks that Information
Visualization can assist with in this area
− Enhance a person’s ability to read, understand and gain knowledge from a document
− Understand the contents of a document or collection of documents without reading them
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More Specific Tasks
• Which documents contain text on topic XYZ?
• Which documents are of interest to me?
• Are there other documents that might be close enough to be worthwhile?
• What are the main themes of a document?
• How are certain words or themes distributed through a document?
We’re Not Doing...
• Information Retrieval
− Active search process that brings back particular entities
• InfoVis (on the other hand)
− Perhaps not sure precisely what you’re looking for
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Challenge
• Text is nominal data
− Does not seem to map to geometric presentation as easily as ordinal and quantitative data
• The “Raw data --> Data Table” mapping now becomes more important
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Simple Taxonomy
Single document
Collection of documents
Enhanced presentation (syntax)
Concepts and relationships (semantics)
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Today
• Focus more on syntactic (enhanced presentation of a document) issue
− Excentric labeling
− Fluid text
− Document lens
− Tilebars
• Next time: focus more on presentation of concepts and themes
What’s wrong with this picture ?
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Problem
• Where are the labels?
− Labeling is difficult to do when so many entities exist
− Can add to ball of string problem
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Objectives
• Each label for a data point should:
− Be readable
− Non-ambiguously relate to its graphical object
− Not hide other pertinent information
• Completeness (labeling of all objects) is desired but not always possible
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Two types of techniques
• Static
− Road maps
− Physical presentations
− Used in cartography
• Dynamic
− Interactive data points
Cartography
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Dynamic Techniques
• Tool tip or “cursor sensitive balloon label”
− SeeIT
• “All or Nothing” technique, zooming (show all then small enough)
− FilmFinder
• Magic lens, shows labels inside
• Dynamic Sampling, only 1->3 labels shown at any one time, context
− Chalmers
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Excentric Labeling
Area of focus
Line and box color match the color of the data point
Description boxes containing the name of the data point
Fekete and Plaisant CHI ‘99
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Being Excentric
• “Invisible” – Does not appear until user hovers over data points
• Describes data points using the name field
• Visually connects labels with data points
• Can order labels to indicate graph position
Different Techniques
• Radial Labeling
− No intersections
− Position does not indicate value
• Vertical Labeling
− Relative vertical position indicates Y value
• Horizontal Labeling
− Relative horizontal position indicates X value
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Demos
• Different examples at
www.cs.umd.edu/hcil/excentric
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Other Issues
• Dealing with long labels
− Just do truncation
• Limiting discontinuities when scanning
− Users “figure it out”
• Facilitating selection of objects
− Right click to lock current label display and select one
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Problems/Questions
• Still dependent on mouse movement rather than showing all
• Can more variables per data point be shown?
• Disorientation is greater when there are no other cues besides position
Augmentation
• Previous work augmented visualizations with helpful text
• How about augmenting text with helpful text?
• Example: Tell me a little more about what I’ll get if I follow a hyperlink
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Fluid Text
• Objective: Annotations to text material that expand on some item of interest
− Example: For hyperlink of a few words, annotation explains more about it
• Important for deciding whether to follow
• Do not want to clutter up text with more noise
Zellweger, Chang and Mackinlay Hypertext ‘98
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Fluid Link
• When user moves mouse cursor over hyperlink, more explanatory material is presented
• Gloss - Expanded material presented
• Click on gloss follows link
• Moving away makes it disappear
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Framework
• Visual cue
− Underline of anchor
• Animated transition
− Gloss effects are smooth animations, allowing viewer to track changes more easily
• Accommodation
− Primary material needs to shift in some way to make room for gloss
Accommodation Techniques
• Interline expansion
− Main text takes on tighter line spacing to make more room for gloss, or main text moves into margins more
• Margin callout
− Gloss drawn out in margins, pointed to by line
• Textual overlay
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Show Technique
• Must really see it work to understand it fully
• Shows examples of different techniques
Video
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Key Points
• Trying to keep main document free of clutter
• Animation is crucial
• One way where vision of electronic book seems a positive
• Comments?
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Problem
• Want to view a page of a document but see context of surrounding text and pages
Example
Text is too small to read
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Potential Solutions
Magnifying lens
Fisheye view
Bifocal
display Perspective
wall
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The Document Lens
• Focus + Context display
• Use 3D to make more effective use of available screen space
• Certain region of text in focus and readable, rest is projected onto 3D pyramid
Robertson and Mackinlay UIST ‘93
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The Document Lens
Video
(Saw little last time on WebBook)
Features
• Movable, rectangular magnifying lens
− Mouse - x,y
− Space,alt - z
• Truncated viewing pyramid
• Readable highlighted regions
• Effective use of most of the screen space
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Problem
Lens pyramid can
leave viewing frustrum So, couple movement of lens with viewpoint movement
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Application
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Application
Improving Text Searches
• What’s wrong with the common search?
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What Hearst Thinks is Wrong
• Query responses do not include include:
− How strong the match is
− How frequent each term is
− How each term is distributed in the document
− Overlap between terms
− Length of document
• Document ranking is opaque
• Inability to compare between results
• Input limits term relationships
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TileBars
• Goal
− Minimize time and effort for deciding which documents to view in detail
• Idea
− Show the role of the query terms in the retrieved documents, making use of document structure
Hearst CHI ‘95
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TileBars
• Graphical representation of term distribution and overlap
• Simultaneously indicate:
− Relative document length
− Frequency of term sets in document
− Distribution of term sets with respect to the document and each other
Interface
Search terms
Presentation
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Technique
Relative length of document
Two search terms
Blocks indicate “chunks”
of text, such as paragraphs
Blocks are darkened according to the frequency of the term in the
document
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See It Work
• Video
• TileBar web page with demo
http://elib.cs.berkeley.edu/tilebars/about.html
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Issues
• Horizontal alignment doesn’t match mental model
• May not be the best solution for web searches
− Non-linear material
− Images? Java apps?
• Anything else?
Still to Come
• Visualizing themes and semantics of document collections
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Reminder
• Project mid-way progress report due on 30th
− 2 hardcopies
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HWs
• HW4 (hierarchy vizs) not yet graded
• HW5 (graph drawing) returned at end
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HW6
• Commercial tools 2
• Due in 2 weeks
• 10 pages or less
• Stress cognitive tasks again
• InfoZoom & EZChooser
− Demos
Upcoming
• Documents & Text 2
− Reading Chapter 10 Salton et al Lin
• Software visualization
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References
• Spence and CMS texts
• All referred to papers
• Fall ‘99 slides
− Lewis
− Kim and Ho
