1. Past, Present, and Future of Information
Visualization
Tiziana Catarci| Sapienza – Università di Roma
Giuseppe Santucci| Sapienza – Università di Roma

2. Information = “data which serve a purpose”
Where to find it? Is it the right one?
Supply Supplie r Part Project Quantity
Supply Supplier Part Project Quantity
1 2 5 17
1 2 5 17
1 3 5 23 1 3 5 23
2 3 7 9 2 3 7 9
4 12 23 9
4 12 23 9
4 6 12 6
Tabular representation of the "Supply" relation 4 6 12 6
Representation of the "Supply" relation with a hypergraph
with label node copies
9 7 17
3
23
5
2
6 12
1 4
Supply Supplier Part Project Quantity
Representation of the "Supply" relation with a
hypergraph without label node copies
How to manipulate it? How to make sense out of it?

3. Visual Representations
We call visual representation one based on the use of visual formalisms
for communicating relevant concepts.
Visual Representation is a language for the eye, which benefits from the
ubiquitous properties of the VISUAL PERCEPTION
"The intricate nature of a variety of computer-related systems and
situations can, and in our opinion should, be represented via visual
formalisms; visual because they are to be generated, comprehended, and
communicated by humans; and formal, because they are to be
manipulated, maintained, and analyzed by computers". (D. Harel)
Basic visual formalisms in the DB area: forms, diagrams, and icons.

4. Using the “Right” Representation
•Certain data visualizations may produce unsound pictures
(pictures that express relationships that are not true in the
information system)
•Some graphical primitives are not adequate for expressing
certain types of data (e.g. shape is not adequate for expressing
ordered domains)
•Interpretation cost (not all graphical primitives that are adequate
for encoding certain information are equally effective)
• The final goal is to provide general frameworks for automatic (or
semi-automatic) generation of correct, complete, and effective
visualizations (given any data, users, tasks)

5. Example
T ow n P eople # P osition Distance Naples
R ome 4, 000, 000 0
Milan 1, 800, 000 N orth 600
N aples 1, 500, 000 S outh- East 200
Pis a 1 50, 000 N orth- W est 350 Rome Milan
Pes cara 2 00, 000 E ast 220
Milan
Pisa Pescara
Pisa 600 Km Neither correct nor complete
350 Km People #
Milan
>2,000,000
Pescara
Rome From 1,000,000
220 Km
to 2,000,000
P isa 600 Km
200 Km
From 500,000
Naples to 1,000,000 350 Km P eople #
<500,000 > 2,000,000
P escara
Complete but not correct Rome
220 Km
F rom 1,000
to 2,000,00
200 Km F rom 500,0
OK! N aples
to 1,000,000
< 500,000

6. DARE
General theory for establishing the adequacy of a visual representation,
once specified the database characteristics
DARE system, which implements such a theory and works in two
modalities
•Representation Check
•completeness
•correcteness
•Representation Generation
•Different kinds of rules:
Visual rules: characterize the different kinds of visual symbols and visual attributes.
Data rules: specify the characteristics of the data model, the database schema, and
the database instances.
Mapping rules: specify the link between data and visual elements.
Perceptual rules: tell us how the user perceives a visual symbol, relationships
between symbols, and which is the perceptual effect of relevant visual attributes
such as color, texture, etc.

7. An old fashioned demo: DARE

8. Old fashioned?
• Local application (even if Java based)
• Only two visualization paradigms
• One visualization at time
• Not a clear separation among steps
DATA --- > Visualization
• But... It was about early 90s...

9. Canonical steps of "up to date" Infovis - Representation

10. Canonical steps of "up to date" Infovis - Presentation
REPRESENTASTIOM

11. Better comprehension of perceptive issues

12. One (very) simple question
• How many 3s here ?
• You have 4 seconds…
458757626808609928083982698028
Game over!
747976296262867897187743671947
746588786758967329667287682085

13. So ?
• Time was not enough?
• You can do that in less than 0.2
seconds !
• Let’s try a different visualization…

14. Pre-attentive data encoding

15. Interaction is a key issue

16. FUTURE: Web based , multiple, coordinated views

17. Interaction!
• Let’s rearrange the rows
Treatments
Treatments Treatments
Treatments
Treatments Treatments
A B ACBD CE DF EG F G A D ACDE CG EB G B F
F
1 1 A B C D E F G 1 1 A D C E G B F
1 1
2 2 Rearrange
Rearrange 3 3
3
2
3 Rearrange 8
3
8
3 8
2
4 4 2
4 2
Crops 5
Crops 5 5 Crops
Crops 6 6 6
Crops 6 Crops 10
6 10
6
7 10
10
4
7 4
7 4
7
8 8 7
8 7
9
9
9
9 (10! , VA can help…)
9
9
10 5 5
10 5
10

19. FUTURE: Tight integration with automated analysis
Visual Analytics

20. One example

21. Comparing J. London and M. Twain books

22. User interaction (a non uniform book?)

23. Interaction

24. What about the Bible?
VA & IR - Giuseppe Santucci 24

25. FUTURE: Integration with everyday devices
Demo !

26. Summarizing
Well understood issues (just apply
them)
Interaction
Visual analytics
Web based application
Deep integration with everyday
devices

27. Thank you!
Questions?