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Visual Tool for Literature Exploration

Visual Tool for Literature Exploration. Tingting Jiang November 14, 2006. Outline. Literature Exploration Visualization Overview Visualization Applications Term Project. Literature Exploration. Traditional activities in literature exploration: * Collecting * Categorizing

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Visual Tool for Literature Exploration

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  1. Visual Tool for Literature Exploration Tingting Jiang November 14, 2006

  2. Outline • Literature Exploration • Visualization Overview • Visualization Applications • Term Project

  3. Literature Exploration • Traditional activities in literature exploration: * Collecting * Categorizing * Reading * Evaluating * Writing

  4. Literature Exploration – Con’t • Collecting – Literature Search 1. Identifying resources * Databases, PittCat, E-journals * Internet 2. Developing search strategies * Keywords or phrases * Broaden, narrow, or modify

  5. Literature Exploration – Con’t • Product of literature exploration – literature review A literature review is a summary of previous research on a topic.

  6. Literature Exploration – Con’t • Questions to be answered in a literature review: 1. What is known about the subject? 2. Are there any gaps in the knowledge of the subject? 3. Have areas of further study been identified by other researchers that you may want to consider? 4. Who are the significant research personalities in this area? 5. Is there consensus about the topic? 6. What aspects have generated significant debate on the topic?

  7. Literature Exploration – Con’t • Questions to be answered in a literature review: 7. What methods or problems were identified by others studying in the field and how might they impact your research? 8. What is the most productive methodology for your research based on the literature you have reviewed? 9. What is the current status of research in this area? 10. What sources of information or data were identified that might be useful to you?

  8. Visualization Overview • Information visualization - The use of computer-supported, interactive, visual representations of abstract data to amplify cognition • Knowledge visualization - The use of visual representations to transfer knowledge between at least two persons

  9. Knowledge Visualization • Purposes * Reduce visual search time * Comprehend large amounts of data * Better understand complex data ** Identify key ideas, researchers, changes in a filed; Knowledge transfer/Scholarly communication

  10. Visual Representations • Graphs (quantitative) • Tables (words, numbers) • Maps (spatial) • Time charts (temporal) • Network charts (node & link) • Diagrams (structure & process) • Icons • Photos

  11. Visualization Techniques • Rearrangement A graphic is no longer ‘drawn’ once for all: it is ‘constructed’ and reconstructed (manipulated) until all the relationships which lie within it have been perceived

  12. Rearrangement Examples • Table Lens

  13. Visualization Techniques – Con’t • Presentation Focus + Context (Fisheye): researchers’ concentration on a problem can probably be enhanced if irrelevant detail are removed

  14. Presentation Examples • Perspective Wall • Hyperbolic Tree (http://nsdl.org/browse/index.php)

  15. Visualization Techniques – Con’t • Interaction * Overview * Zoom * Filter * Details-on-demand * Relate * History * Extract

  16. Visualization Applications • Dogpile (http://www.dogpile.com/) • Vivisimo (http://vivisimo.com/) • Clusty (http://clusty.com/) • Grokker (http://www.grokker.com/) • Mooter (http://www.mooter.com/) • KartOO (http://www.kartoo.com/) • ujiko (http://www.ujiko.com/) • KwMap (http://www.kwmap.net/) • TouchGraph (http://www.touchgraph.com/) • RefViz (http://www.refviz.com/)

  17. Dogpile • InfoSpace, Inc. • Metasearch engine: Google, Yahoo! Search, MSN, Ask.com, About, MIVA, LookSmart and more • Relevancy • Metasearch technology ensuring best results top the list • Missing Pieces visualization (disappear?)

  18. Dogpile

  19. Vivisimo • Carnegie Mellon University 2000 • Award-winning search technology – “clustering” • Pre-retrieval Tagging vs. post-retrieval Clustering

  20. Clusty • Vivisimo 2004 Pittsburgh • Metasearch engine: Ask.com, MSN, Wikipedia, etc. • Clusters • Discover unexpected relationships between items • Tree – expand, contract

  21. Grokker • Groxis Inc. • Metasearch engine: Yahoo!, Wikipedia, Amazon Books • Clusters • Results grouped in topics rather than presented in a linear list where some results might be missed • Outline View (tree) as well as Map View (interactive)

  22. Grokker

  23. Mooter • Mooter Media 2003 • Clusters • Node-link diagrams; all the clusters separated on multiple pages

  24. KartOO • KartOO S.A. • Metasearch engine • Related topics help refine search • Interactive cartographic maps; one search generates several maps • Lots of cool visual tricks; not as relevant as expected

  25. ujiko • KartOO S.A. • Sets of themes help improve search • Interactive visualization • Customizable search engine – users decide the relevance of results • The more you use it, the more functions it is able to offer

  26. KwMap • KwMap.Net • Keyword search engine • Refine search keywords – related keywords and keywords variantions • Two axes • Results - websites

  27. TouchGraph • TouchGraph LLC • Visualizations of associative networks • Amazon browser, Google browser, and LiveJournal browser • Interactive node-link diagrams • Clusters

  28. RefViz • OmniViz Inc. • A text analysis and visualization software application designed to retrieve, analyze, organize, and facilitate the comprehension of the huge amounts of literature • Galaxy & Matrix visualizations

  29. RefViz - Galaxy • Groups and references

  30. RefViz - Matrix • Groups and concepts

  31. Summary • Relevance vs. clustering • Clusters: classification vs. categorization • Results: content vs. sources • Types: textual vs. multimedia • Keywords: automatically vs. manually • Browsing vs. searching • Visualization features

  32. Term Project • Goal: developing a new scheme for literature visualization (prototype or Web based system) • Follow-up research: developing a Web based tool for the whole process of literature exploration, not just collecting

  33. Term Project Pre-visualization processing: • Classification schemes subjected to change • Literature resources • Collaborative human reading • Filtering, tagging, and submitting to semi-hierarchy

  34. Term Project Visualization highlights: • Complementary visualization views – semi-hierarchical, time, and region • Tag-oriented • Tag to knowledge fraction mapping • Browsing as well as searching • Rearrangement, Presentation, and Interaction

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