table lens from papers 1 and 2 by tichomir tenev, ramana rao, and stuart k. card

Table Lens

From papers 1 and 2

By Tichomir Tenev, Ramana Rao, and Stuart K. Card

Overview

Uses focus+Context apporach Context elements are represented graphically Focus elements have text and graphic

display

Advantages

Increases viewable portion of table by 100 times

Ease of Navigation Ease of Exploration

Table Lens Focal Technique

Mutates layout of table Does not bend any rows or columns

Distortion Function Framework DOI function: item -> value. Value indicates

level of interest DOI function controls how available space is

allocated among items

DOI in Table lens

DOI maps cell address to interest level 2 of them, one for each dimension

Manipulation of Focus Operations Zoom- changes amount of space to focal

area Adjust- changes amount of contents viewed

within focus area Slide- changes location of focus area within

the context

User manipulation

Clicking at upper left corner- zooms all cells Touching any region in context will slide

current focus to that location Grasping focus slides focus to that location

Results

Apply data to baseball stats of 323 rows by 23 columns (7429 cells)

Display whole table on screen at one time

Paper #2 Design 1 Nesting Focal Levels Space allocated to each element is

dependent on the focal level of element 2 foci, Primary focus always inside region of

secondary focus 2ndary focus used for coarse navigation Primary used for finer navigation

Design II Controlling focal spans Space allocated per data element dependent

on focus level and parameter specified by user

Primary focus elements may vary in size Spatial map at any time depends on History

of user interaction

Conclusion

Felt design 2 was the better design.

Disadvantage

Works only for data tables which have have <= number of entries as pixel rows and each column has enough pixels wide to accommodate variables.

Paper #2 discusses how to improve it

Polaris: A System for Query, Analysis and Visualization of Multi-dimensional Relational DatabasesChris Stolte and Pat Hanrahan

Standford University

Polaris

Interactive exploration of large multi-dimensional databases

Expressive set of graphical displays Uses tables to organize multiple graphs on a

display

Relational databases

Each row in table = basic entity (tuple) Each column represents a field Fields can be ordinal, or quantitative

Visual Specification

Is the configuration of the fields of the tables on shelves

User does this by dragging and dropping fields onto shelves

Visual Specification

Mapping of data sources to layers # of rows, columns, and layers, and relative

order Selection of tuples from the database Grouping of data within a pane Type of graphic displayed in each pane Mapping of data fields with retinal properties

Table Algebra

Used to specify table configurations. Dragging and dropping implicitly does it

Operands are the names of the ordinal and quantitative fields of database

Operators (concatenation, cross, nest)

Types of Graphics (Ordinal- Ordinal) Axis variables are independent of each other

R represents the fields encoded in the retinal properties of the marks

Following slide shows sales and margin as a function of product type, month and state for items sold by coffee chain

Ordinal-Quantitative Graphics Bar charts, dot plots, Gantt chart Quantitative variable is dependent of ordinal

variable

Figure 6c shows a case where a matrix of bar charts is used to study several functions of the independent variables product and month

Quantitative-Quantitative Graphics Discover causal relationships between the

two quantitative variables.

Figure 3e shows how flight scheduling varies with the region of the country the flight originated.

Visual mappings

Encoding different fields of the data to retinal properties

Shape, Size, Orientation, Color Used in the ordinal to ordinal example

Generating Database Queries

1. Selecting the Records

Generating Database Queries

2. Partitioning the records into pains Putting retrieved records in their corresponding

pane

Generating Database Queries

3. Transforming Records within the Panes If aggregation, it is done here

Results

Cut expenses for a national coffee store Create table of scatterplots showing

relationship between marketing costs and profit (Figure 6a)

Notice trend; certain products have high marketing costs with no or little profit

Results

Used linked displays to determine that in New York several products are offering very little return despite high costs

Creates bar chart for products in New York

Future Work

Exploring interaction techniques for navigating hierarchical structures of mulit-dim databases

Use selected mark in one display as the data input to another