J. A. Wise, J.J. Thomas, K. Pennock, D. Lantrip, M. Pottier, A. Schur, and V. Crow

1. 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. 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) 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. 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: Document Visualization

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 Document Visualization

6. SeeSoft (Eick, 1992) Document Visualization

7. 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 Document Visualization

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. Document Visualization

9. 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. 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. Document Visualization

11. Galaxies Document Visualization

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. Document Visualization

13. Themescapes Document Visualization

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. Document Visualization

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. Document Visualization

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) Document Visualization

17. 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. Document Visualization

18. Screenshot Document Visualization

19. 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). Document Visualization

20. 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. Document Visualization

21. 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. Document Visualization

22. 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” 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 Document Visualization

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. Document Visualization