1. Transfer Viva Empathic Visualisation Algorithm (EVA)

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

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

4. Types of Variables n Nominal (equal or not equal) n Ordinal (obeys a < relation) n Quantitative (can do arithmetic)

5. Taxonomy n Mapping (1) – Arbitrary (a) – Automatic (b) n Visual Structure (2) – Abstract (c) – Naturalistic (d) ? (1) (2) (a)(b) (c) (d)

6. Arbitrary Mapping - Abstract Visual Structure n Advantages – Actual Values – Quantitative analysis – Variables treated uniformly – Objective visual structure n Disadvantages – Hard to get overview – User learning time – Time to make decisions – Not generic – Hard to find relationships – Complexity increases with dimensionality

7. Automatic Mapping - Abstract Visual Structure n Advantages – Generic system – Actual Values – All variables treated equally – Less user learning time n Disadvantages – Time to make decisions – hard to get overview – hard to find relationships – Interactivity – Complex processing, and more complicated visual structure

8. Arbitrary Mapping - Naturalistic Visual Structure n Advantages – Holistic view – Simple, easy to use visual structure – Less time to make decisions – Relationships of the variables n Disadvantages – Variables not treated uniformly – Subjectiveness of visual structure – User learning time – No actual quantities, values – No extreme cases

9. Automatic Mapping - Naturalistic Visual Structure: EVA n interrelationships of data variables, encapsulated in one visual structure n Gross information about the data (overall view) n Simple, easy to understand visual structure n Learning time minimised n Background of users irrelevant n May enable decisions on the fly

10. ... n Generic system n Complexity of visual structure doesn’t increase with dimensionality

11. From Data to Naturalistic Visual Structure an Automatic Mapping

12. Fundamentals of method n n*k data matrix X (row individual, k observations) n Objective: – salient features – overall view – Naturalistic, Automatic n “Value System” n Goal of method: Visual Homomorphism

13. Assumptions Be p functions over the data - “value system” Be p characteristics of visual structure. Measuring the totality of the visual structure The r features of the visual structure

14. Assumptions 2 Be feature functions over the data matrix determining the visual structure Ifand Then we have to choose Such thatis minimised

15. Using Genetic Programming n Minimisation problem can be tackled with a GP n Large collection of random functions - Population n Fitness: The distance measurement

17. Summary n User identifies the “value system” n System decides on the visual structure and its features n System identifies the characteristics of the visual structure n Fitness function is defined n GP parameters, and run

18. Achievements so far n Literature survey on Information Visualisation n GP survey n System implemented n Successfully tested using circles – users needed no learning time – users made decisions immediately – users noticed small changes

19. Conclusions n Can subjects extract information from the visual representation of the data set? Yes n Can this visualisation method act as an aid to the decision making process? Yes

20. Under process n Experiment using faces n Face: the epitome of a naturalistic visual structure n At final stage

21. Future Work n For current experiment, test method for: – generalisability – convergence (experimentally) n 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

22. Time Plan n Finish the first test by end of January n Finish the main, final experiment by beginning of July n Write up