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 –Large raw tables are uninterpretable

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

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

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

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

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

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

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

Graphical Cell Representation Presentation factors –Value –Value Type –DOI (Region) Type –Cell Size –User Choices –Spotlighting

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

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

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

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

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

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

Correlation : Table Lens

Correlation : Splus

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

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

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