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. Examples from 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