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Enhancing Cognition Through Information Visualization: Key Taxonomies and Challenges

This course on Information Visualization covers the fundamental principles and challenges associated with visualizing abstract data. It highlights the importance of using interactive visual representations to enhance cognitive abilities, distinguish between information and scientific visualization, and understand categorical and continuous data types. By exploring various data types and tasks such as overview creation and filtering, participants learn to harness visual data mining techniques effectively. The session addresses challenges like data cleaning, representation integration, and achieving universal usability for enhanced information discovery and decision-making.

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Enhancing Cognition Through Information Visualization: Key Taxonomies and Challenges

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Presentation Transcript

  1. Information VisualizationSession 13 Course : T0593/ Human Computer Interaction Year : 2012

  2. Outline Data Type by Task Taxonomy Challenges for Information Visualization 3

  3. Introduction • “A Picture is worth a thousand words” • Information visualization can be defined as the use of interactive visual representations of abstract data to amplify cognition (Ware, 2008; Card et al., 1999). • The abstract characteristic of the data is what distinguishes information visualization from scientific visualization. • Information visualization: categorical variables and the discovery of patterns, trends, clusters, outliers, and gaps • Scientific visualization: continuous variables, volumes and surfaces

  4. Introduction (cont.) • Sometimes called visual data mining, it uses the enormous visual bandwidth and the remarkable human perceptual system to enable users to make discoveries, take decisions, or propose explanations about patterns, groups of items, or individual items. • Visual-information-seeking mantra: • Overview first, zoom and filter, then details on demand. • Overview first, zoom and filter, then details on demand. • Overview first, zoom and filter, then details on demand. • Overview first, zoom and filter, then details on demand. • Overview first, zoom and filter, then details on demand.

  5. Data Type by Task Taxonomy

  6. Data Type by Task Taxonomy: 1D Linear Data

  7. Data Type by Task Taxonomy: 2D Map Data

  8. Data Type by Task Taxonomy: 3D World Data

  9. Data Type by Task Taxonomy: Multidimensional Data

  10. Data Type by Task Taxonomy: Temporal Data

  11. Data Type by Task Taxonomy: Tree Data

  12. Data Type by Task Taxonomy: Network Data

  13. The seven basic tasks • Overview task- users can gain an overview of the entire collection • Zoom task - users can zoom in on items of interest • Filter task- users can filter out uninteresting items • Details-on-demand task- users can select an item or group to get details • Relate task- users can relate items or groups within the collection • History task- users can keep a history of actions to support undo, replay, and progressive refinement • Extract task- users can allow extraction of sub-collections and of the query parameters

  14. Challenges for Information Visualization • Importing and cleaning data • Combining visual representations with textual labels • Finding related information • Viewing large volumes of data • Integrating data mining • Integrating with analytical reasoning techniques • Collaborating with others • Achieving universal usability • Evaluation

  15. Supporting Materials • www.cs.umd.edu/projects/hcil/members/bshneiderman/ijhcs/main.html • http://web.cs.wpi.edu/~matt/courses/cs543/visualize/

  16. Q & A

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