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Document (Text) Visualization

Document (Text) Visualization. Mao Lin Huang. Paper Outline. Introduction Visualizing text Visualization transformations: from text to pictures Examples from the MVAB Project Conclusions and directions for future research and development. Introduction. Current Visualization approaches

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Document (Text) Visualization

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  1. Document (Text) Visualization Mao Lin Huang

  2. Paper Outline • Introduction • Visualizing text • Visualization transformations: from text to pictures • Examples from the MVAB Project • Conclusions and directions for future research and development

  3. Introduction • Current Visualization approaches • For visualizing mostly structured and/or hierarchical information • Some research in information retrieval • Utilized graph theory or figural display • Information returned is documents in text form • Users still have to read • Causes a severe upper limit • Open Source digital information • Available text overwhelms the traditional reading methods of inspection, sift and synthesis

  4. Visualizing text • True text visualizations • Must represent textual content and meaning without the user having to read it • Result from content abstraction and spatialization of the text document • Use primarily preattentive, parallel processing powers of visual perception • Goal is to spatially transform text information into a new visual representation

  5. Visualization transformations: from text to pictures • Four important technical considerations • Clear definition of text • what comprises text • how it can be distinguished from other symbolic representations • Way to transform raw text into a different visual form • Foundation for meaningful visualization • Suitable mathematical procedures and analytical measures • A database management system

  6. Processing Text • Requirements of text processing engine • Identification and extraction of text features • Frequency-based measures on words • Higher order statistics taken on the words • Semantic in nature • Efficient and flexible representation of documents in terms of these text features • Support for information retrieval and visualization • Pre-process, indexing

  7. Visualizing output from text processing • Representing the document • a vector in high dimensional feature space • Comparisons, filters, and transformations can be applied • Clustering using the normalized document vectors • Projection • Principal Components Analysis • Multi-Dimensional Scaling • Exponential order of complexity • Clustering in the high-dimensional feature space • Visualize the cluster centroids

  8. Managing the representation • Two basic classes of data • Raw text files • Static in nature, Simple in structure • Easy to manage • Visual forms of the text • Extensive and dynamic • Object-Oriented Database • Flexibility of data representation • Power of inheritance • Ease of data access

  9. Interface design for text visualization • Backdrop • Central display resource • Workshop • Grid having resizable windows to hold multiple views • Chronicle • Area where views are placed and linked to form a visual story

  10. Examplesfrom the MVAB Project • MVAB • Multidimensional Visualization and Advanced Browsing Project • Visualization and analysis of textual information • Showcased in SPIRE • SPIRE • Spatial Paradigm for Information Retrieval and Exploration • Starfields and Topographical maps metaphors • Galaxies and Themescapes

  11. Galaxies • Displays cluster and document interrelatedness • 2D scatterplot of ‘docupoints’ • Simple point and click exploration • Sophisticated tools • Facilitate more in-depth analysis • Ex) temporal slicer

  12. Galaxies Screen Shot

  13. ThemeScapes • Abstract, 3D landscapes of information • Convey relevant information about topic or themes without the cognitive load • Spatial relationships reveal the intricate interconnection of thems

  14. ThemScapes - Advantages • Displays much of the complex content of the document database • Utilizes innate human abilities for pattern recognition and spatial reasoning • Communicative invariance across levels of textual scale • Promote analysis

  15. ThemeScapes Screen Shot

  16. Conclusions • Text visualizations can overcome much of the user limitations • Enhanced insight and time savings (35 mins vs 2 weeks) • Creative with the tool • Querying and analytical manipulation come together in a single visualization • Permits a different kinds of querying • Text visualizations will have to access and utilize the cognitive and visual processes

  17. Directions for Future R & D • Visual Data Analysis • Elaborate the visual metaphors • Addition of sensory modalities • Virtual interaction

  18. My Favorite Sentence The bottleneck in the human processing and understanding of information in large amounts of text can be overcome if the text is specialized in a manner that takes advantage of common powers of perception.

  19. Contributions • Explorations of new visualizations • Discussion of the process for mapping Raw Data Document collections into visualizations

  20. Notes on the Reference • Designing Interaction: Psychology at the Human Computer Interaction • Interfaces Issues and Interaction Strategies for Information Retrieval Systems • Clustering and Dimensionality Reduction in SPIRE

  21. Critique – Strengths and Weaknesses • Strengths • Provide natural visual metaphors • Enable the users to see the relationships between documents with minimal required reading • Weaknesses • No validation of some conclusions

  22. What has happened to this topic? • 1996 R&D 100 Award • OCSB • On-line Citation Searching and Browsing in UMD • "ThemeScape" is now a trademarked term of Cartia, Inc. • WebThemeTM • an interactive tool that provides a visual display of the common themes in collections of web-based documents

  23. WebTheme Screen Shot

  24. Document Lens • Why: -Text too small to read but yet needed to • perceive patterns. • - Perspective wall wastes corner areas of screen • What: General visualization technique based on a • common strategy for understanding paper • documents when their structure is not • known. • How:3D Visualization Tool For Large • Rectangular Presentations

  25. Document Lens Features • Lens – rectangular – interested in text that is mostly rectangular • Sides are elastic and pull the surrounding parts towards the lens creating a pyramid

  26. Document Lens

  27. Document Lens

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