table lens introduction to the table lens concept table lens implementation projected usage...

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