1. Proceedings of Infoviz’95
Document Visualization “Visualizing the Non-Visual: Spatial Analysis and Interaction with Information from Text Documents”
J. A. Wise, J.J. Thomas, K. Pennock, D. Lantrip, M. Pottier, A. Schur, and V. Crow
Proceedings of Infoviz’95
Reviewed by
Nada Golmie
for CMSC 838S
Fall 1999

2. Document Visualization
Outline
Document visualization:
What? Why? How?
Examples for 1D, 2D visualizations:
vector space analysis (Salton 1995)
reduced text + interaction (Eick, 1992)
2D maps of document collections (Lin, 1992)
3D Visualization: SPIRE
(Wise et. al. 1995)
3D + Time: Interactive Landscapes
(Rennison, 1994)
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Document Visualization

3. Document Visualization
Document visualization is an important IV application due to emerging technology trends:
World Wide Web
Digital Libraries
Communication Advances
Mapping a text document:
Understand the content of a document.
Mapping a collection of documents:
Discover relationships among documents.
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Document Visualization

4. Vector Space Analysis (Salton et. al.)
Support of free-form text queries in IR.
Text passages are mapped into a vector of terms in high dimensional space:
where is the weighted assigned to term k in document .
Given document and query a similarity computation is computed as:
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5. Reduced Text + Interaction: SeeSoft (Eick, 1992)
Reduced representation
display of lines as rows, files as columns (max 900 rows per column)
Colors are used to display statistics
statistics include: age, programmer, feature, type of line, number of times the line was executed
Direct manipulation techniques
find interesting patterns
capability to read actual code using magnification
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6. Document Visualization
SeeSoft (Eick, 1992)
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7. Document Visualization
2D Maps (Lin, 1992)
Framework for information retrieval:
mapping of high dimensional document space into 2D map.
document relationships are explored using visual cues such as: dots, links, clusters, and areas.
Neural network self-organizing learning algorithm based on Kohonen’s feature map:
preserves distance relationships between input data.
allocates different numbers of nodes to inputs based on their occurrence frequencies.
Sitemap
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8. Visual Text Analysis: SPIRE
SPIRE (Spatial Paradigm for Information Retrieval and Exploration) is a software that allows users:
to explore complex relationships between text documents.
to rapidly discover known and hidden information relationships by reading only the pertinent documents rather than wading through large volumes of text.
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Document Visualization

9. Document Visualization
Applications
SPIRE was originally developed for the U.S. intelligence community.
Other potential applications include:
environmental assessment
market analysis
corporations researching competitive products,
health care providers searching patient records,
or attorneys reading through previous cases.
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Document Visualization

10. 2D Scatterplot: Galaxies
Galaxies computes word similarities and patterns in documents and then displays the documents on a computer screen to look like a universe of "docustars”:
closely related documents will cluster together in a tight group.
unrelated documents will be separated by large spaces.
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11. Document Visualization
Galaxies
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12. 3D Landscapes: Themescapes
Themes within the document spaces appear on the computer screen as a relief map of natural terrain:
mountains in Themescapes indicate where themes are dominant;
valleys indicate weak themes.
shapes reflect how the thematic information is distributed and relate across documents.
Themes close in content will be close visually based on the many relationships within the text spaces.
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13. Document Visualization
Themescapes
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14. Visualization Transformations
Definition of text: written form of natural language.
Text conversion to spatial form: algorithms & processes.
Meaningful visualizations: mathematical procedures and analytical measures.
Database management:store and manage text and its derivative forms.
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15. Processing Text Requirements
Identification and extraction of text features:
frequency-based measures of words
higher order statistics taken on words: occurrence, frequency, context of individual words are used to characterize defined word classes.
Semantic approaches using natural language understanding.
Efficient and flexible representation of documents in terms of text features.
Support of information retrieval and visualization.
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16. Visual Output of Text Processing
Vector representation of document in high dimensional feature space.
Comparisons, filters, transformations can be applied
Projection onto 2-3D visualization
dimensionality reduction
scaling
clustering in high dimension feature space and centroids of clusters are fed into layout algorithms (principal component analysis or multidimensional scaling)
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Document Visualization

17. Document Visualization
Interface Design
Three display types:
Backdrop: central display resource.
Workshop: grid with resizable windows to hold multiple views.
Chronicle: space where views are placed and linked to form a visual story.
Tools provided to allow more in-depth analysis: point and click, grouping, annotation, query, subset, temporal slicing.
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Document Visualization

18. Document Visualization
Screenshot
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19. Document Visualization
Favorite Sentences
“The bottleneck in the human processing and understanding of information in large amounts of text can be overcome if the text is spatialized in a manner that takes advantage of common powers of perception.”
“So much has already been written about everything that you can’t find out anything about it”. James Thurber (1961).
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Document Visualization

20. Document Visualization
Contributions
Effective use of physical metaphors such as night sky and landscape to provide overview visualization on the collection of documents:
helps answer simple questions about the database
Discussion on processing text for visualization.
Platform includes integrated tools and techniques for text manipulation and analysis.
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21. Document Visualization
Critique
How to measure the effectiveness of the visualization in discovering relationships and answering detailed questions about the documents:
may depend on the ease of interaction
need to verify claim: “discovering in 35 minutes what would have taken two weeks otherwise”.
There could be cluttering and occlusion resulting from layout algorithms (complex for large collections of documents)
Clustering may reduce feature sensitivity from individual documents.
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22. Document Visualization
Other Comments
Agree with the need to create visual tools to aid cognitive skills, however skeptical about statement:
“And the limitations of Information Age will not be set by the speed with which human mind can read”:
Paper contains too many sound biting sentences and buzz words which could be distracting:
“fluid environment for reflective cognition and higher-order thought”
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Document Visualization

23. Galaxy of News: Interactive Landscapes
Parse content to extract key information
Build an associative relation network
Classify elements into hierarchies
Sort peer elements spatially and temporally
Construct visual information space based on classified elements
Dynamic response to visual interaction
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24. Galaxy of News: Summary
Use of motion to visualize relationships among documents.
Documents have no fixed position in space
associative relation network built dynamically
fixed positioning of categories
Space constructed is based on conceptual abstract metaphors (galaxies) and could have any dimensions.
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Document Visualization