Transfer Viva

Empathic Visualisation Algorithm (EVA)

Outline

Multi-dimensional Visualisation Overview of EVA Achievements so far What’s happening now Future Work

Data

Large Multiple dimensions (>3) Non-physical nature Hidden information Quantitative (or transformed to

quantitative)

Types of Variables

Nominal (equal or not equal) Ordinal (obeys a < relation) Quantitative (can do arithmetic)

Taxonomy

Mapping (1)– Arbitrary (a)– Automatic (b)

Visual Structure (2)– Abstract (c)– Naturalistic (d)

?

(1)

(2) (a) (b)

(c)

(d)

Arbitrary Mapping - Abstract Visual Structure Advantages

– Actual Values– Quantitative analysis– Variables treated

uniformly– Objective visual

structure

Disadvantages– Hard to get overview– User learning time– Time to make

decisions– Not generic– Hard to find

relationships– Complexity increases

with dimensionality

Automatic Mapping - Abstract Visual Structure Advantages

– Generic system– Actual Values– All variables treated

equally– Less user learning time

Disadvantages – Time to make

decisions– hard to get overview– hard to find

relationships– Interactivity– Complex processing,

and more complicated visual structure

Arbitrary Mapping - Naturalistic Visual Structure Advantages

– Holistic view– Simple, easy to use

visual structure– Less time to make

decisions– Relationships of the

variables

Disadvantages– Variables not treated

uniformly– Subjectiveness of

visual structure– User learning time– No actual quantities,

values– No extreme cases

Automatic Mapping - Naturalistic Visual Structure: EVA interrelationships of data variables,

encapsulated in one visual structure Gross information about the data (overall

view) Simple, easy to understand visual structure Learning time minimised Background of users irrelevant May enable decisions on the fly

...

Generic system Complexity of visual structure doesn’t

increase with dimensionality

From Data to Naturalistic Visual Structure an Automatic Mapping

Fundamentals of method

n*k data matrix X (row individual, k observations)

Objective:– salient features– overall view– Naturalistic, Automatic

“Value System” Goal of method: Visual Homomorphism

Assumptions

psXs ,...,2,1),( Be p functions over the data - “value system”

pses ,...2,1),( Be p characteristics of visual structure. Measuring the totality of the visual structure

rtt ,...,2,1),( The r features of the visual structure

Assumptions 2

rtXf tt ,...,2,1),()( Be feature functions over the data matrix determining the visual structure

If ),...,,( 21 p and ),...,,( 21 peeee

Then we have to choose )(Xft

Such that e is minimised

Using Genetic Programming

Minimisation problem can be tackled with a GP

Large collection of random functions - Population

Fitness: The distance measurement

e

Summary

User identifies the “value system” System decides on the visual structure and

its features System identifies the characteristics of the

visual structure Fitness function is defined GP parameters, and run

Achievements so far

Literature survey on Information Visualisation

GP survey System implemented Successfully tested using circles

– users needed no learning time – users made decisions immediately– users noticed small changes

Conclusions

Can subjects extract information from the visual representation of the data set? Yes

Can this visualisation method act as an aid to the decision making process? Yes

Under process

Experiment using faces Face: the epitome of a naturalistic visual

structure At final stage

Future Work

For current experiment, test method for:– generalisability– convergence (experimentally)

Final experiment (also using faces)– different application (data set)– divide users to experts/non_experts– test it with different number of characteristics– Measure time to make decisions– Test ‘readability’ of visual structure

Time Plan

Finish the first test by end of January Finish the main, final experiment by

beginning of July Write up