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Dive deep into literature exploration with visualization techniques and tools for better understanding, analyzing, and presenting research insights. Learn about information visualization, knowledge transfer, and interactive visual representations that amplify cognition. Explore various methods such as graphs, tables, maps, and more for comprehensive data comprehension. Discover top visualization applications and techniques to streamline your research process.
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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 * Reading * Evaluating * Writing
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
Literature Exploration – Con’t • Product of literature exploration – literature review A literature review is a summary of previous research on a topic.
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?
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?
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
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
Visual Representations • Graphs (quantitative) • Tables (words, numbers) • Maps (spatial) • Time charts (temporal) • Network charts (node & link) • Diagrams (structure & process) • Icons • Photos
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
Rearrangement Examples • Table Lens
Visualization Techniques – Con’t • Presentation Focus + Context (Fisheye): researchers’ concentration on a problem can probably be enhanced if irrelevant detail are removed
Presentation Examples • Perspective Wall • Hyperbolic Tree (http://nsdl.org/browse/index.php)
Visualization Techniques – Con’t • Interaction * Overview * Zoom * Filter * Details-on-demand * Relate * History * Extract
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/)
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?)
Vivisimo • Carnegie Mellon University 2000 • Award-winning search technology – “clustering” • Pre-retrieval Tagging vs. post-retrieval Clustering
Clusty • Vivisimo 2004 Pittsburgh • Metasearch engine: Ask.com, MSN, Wikipedia, etc. • Clusters • Discover unexpected relationships between items • Tree – expand, contract
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)
Mooter • Mooter Media 2003 • Clusters • Node-link diagrams; all the clusters separated on multiple pages
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
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
KwMap • KwMap.Net • Keyword search engine • Refine search keywords – related keywords and keywords variantions • Two axes • Results - websites
TouchGraph • TouchGraph LLC • Visualizations of associative networks • Amazon browser, Google browser, and LiveJournal browser • Interactive node-link diagrams • Clusters
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
RefViz - Galaxy • Groups and references
RefViz - Matrix • Groups and concepts
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
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
Term Project Pre-visualization processing: • Classification schemes subjected to change • Literature resources • Collaborative human reading • Filtering, tagging, and submitting to semi-hierarchy
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