1 / 23

Table Lens

Table Lens. Introduction to the Table Lens concept Table Lens Implementation Projected Usage Scenarios Usage Comparison with Splus Critical Analysis. Interfacing with Tables. Tables as a common representation Regularized content Instance vs. Value Layout

konane
Télécharger la présentation

Table Lens

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Table Lens • Introduction to the Table Lens concept • Table Lens Implementation • Projected Usage Scenarios • Usage Comparison with Splus • Critical Analysis

  2. Interfacing with Tables • Tables as a common representation • Regularized content • Instance vs. Value Layout • Large raw tables are uninterpretable

  3. Table Views • Two common modes of interface • Focus: User examines as many fields as will fit on her screen (660 cells) • Sacrifices relationships for detail • Context: A generated representation of broad dataset it examined • High-level interpretation with no specifics

  4. Table Lens • Lenses are devices which focus attention on part of a large context • Table Lens allows both Focus and Context views simultaneously • Tables are regular • Deformations are also regular • Lensing creates categories of detail

  5. Categories of detail • Central areas of lens have highest levels of detail • Row and Column focal have less detail • Non-focal areas have sharply reduced detail but are still present

  6. Degree of Interest • Detail categorization and Visualization based on Degree of Interest calculation • DOI Translates to cell size along 2 independent axes • Binary correspondence in each dimension

  7. Table Lens Benefits • Table Lens can display both focus and context • Much more data can be displayed at once • 30 – 100 times basic spreadsheet • Allows simultaneous view of: • Variable value distribution shape • Inter-variable correlation • Specific instance values • Outlier identification

  8. Table Lens Implementation • Interactive manipulation of focus – Atomic operations • Zoom: Enlargement of focal area • Ajust: Expansion of focal contents • Slide: Positioning of focal area • Composite manipulation • Adjust-zoom: Adds items to focus while expanding focal area

  9. Multiple Foci • Multiple focal areas are supported • Important use modifications • Adjust corrupts display • Zoom required to be global

  10. Graphical Cell Representation • Presentation factors • Value • Value Type • DOI (Region) Type • Cell Size • User Choices • Spotlighting

  11. Other Features • Ascending/Descending Sorting • Spotlighting • Formula compilation • Median, Quarter, Extents Selection

  12. Table Lens facilitates • Correlation of variable value curves • Outlier identification/interrogation • Variable nesting identification • Ease of use (Simple!)

  13. Usage Comparison • Exploratory Data Analysis (EDA) • Sensemaking • “Activities in which external representations… are interpreted into semantic content and represented in some other manner”

  14. EDA Tasks • Batch Assessment • Determining structure of information and its irregularities • Variable modeling • Finding formulaic expression for variable values

  15. Learning Loop • Steps: • Search for representation of regularities • Encoding information into representation • Altering representation to accommodate outliers • Use of representation for discovery

  16. Table Lens vs Splus • Estimating utility of application approach • Required time to perform tasks • Benchmark times • Empirical times • Qualitative Considerations • Ease of use • Complexity vs Return

  17. Correlation : Table Lens

  18. Correlation : Splus

  19. Time-cost for important properties of all variables • Table Lens superior for iterative analysis • Splus faster for random access

  20. Time-cost for related variables • Table Lens superior when several clusters can be grouped and eliminated early • Splus more effective when broad dataset must be analyzed

  21. Learning costs • Table Lens performs within significant margins as well as Splus • Table Lens is much simpler than Splus

More Related