This document discusses information visualization (IV), which involves visually encoding abstract data to allow for visual pattern detection. IV takes advantage of the human visual system and can be used alongside statistical analysis. Common IV techniques include filtering, linking, and focusing data in interactive visualizations. IV has applications in domains like software visualization, where it can help analyze workflows, dependencies, and performance. Libraries exist to help developers integrate IV into their own work and provide end-users with pre-built visualization tools.
2. W HAT
What is Information Visualisation (IV)?
Visual encoding of abstract information to allow
visual exploration /detection of patterns
Can be used in tandem with statistical approaches
3. W HY
Humans have a well-developed visual system, so
take advantage of its pattern-detecting facilities
Also some people just don’t trust data until “they
see it with their own eyes”, or are uncomfortable
with statistical measures
4. WHY
MPG and Weight
Finding patterns negatively correlated
Horsepower and Weight
positively correlated
6. D ATA S TRUCTURES
Information is abstract i.e. non-physically rooted
Examples include
Family trees
Share prices
Social networks
Tuple data
7. I NTERACTION T ECHNIQUES
IV applications allow users to interact with the
data, as opposed to being static screenshots (cf
GraphViz)
Common techniques beyond the basics include
Filtering – removing, reordering and re-rendering
according to selected subsets of information
Linking – viewing the same data (and same filters)
in different views
Focusing – visual effects such as non-linear
focus+context and zoom to accentuate areas of
the visualisation
Speed of response is vital, recommend < 50ms
8. I NTERACTION T ECHNIQUES
Filtering works on a data set by interactively
reducing the number of items that fit in the
selected set.
Here a house sale set of 30,000+ records is cut down to
under 2,000 using the sliders on the columns.
9. I NTERACTION T ECHNIQUES
Focusing works by giving more space to items of
interest, but still retaining the ‘context’ of the
unselected objects.
Here the selected items in blue have increased in size.
10. I NTERACTION T ECHNIQUES
Linking works by having data viewed
simultaneously in different visualisations
The linking may also apply to selections and filters
Linking is closely associated with MVC
architectures for separating UI and Model
data. Use the same model data in multiple
UI components.
11. W HERE
Games Developers have two opportunities for
using IV
In the course of their work
Workflow analyses
Software dependencies
In the game
Attractive effects
User attention
12. S OFTWARE V ISUALISATION
Software visualisation – one of the first topics
explored by visualisation researchers – fixing
their own problems first
Eick et al – SeeSoft –
Developer tracker - 1992
Telea & Auber – CodeFlows
SVN Visualisation - 2009
Van Ham – Call Matrices –
Method Call Graphs - 2003
13. S OFTWARE V ISUALISATION
Stand alone tools are very well, but integrating
them into IDEs such as Eclipse makes them more
useful (and more likely to be used)
Malnati – XRay – Package CHISEL group – Creole –
dependencies - 2008 Call & Method graph - 2007
14. L IBRARIES
Developing visualisations can be time-consuming
Developer Libraries
Integrate common vis techniques into existing
programs / websites (Prefuse, InfoVis
Cyberinfrastructure)
End User Libraries
Drop data into visualisation (ManyEyes. Mondrian)
15. T HE E ND
Some demos at the CISS Napier website
http://www.ciss.soc.napier.ac.uk/
Q’s
Hinweis der Redaktion
This should be a half hour intro talk on why people should use visualisation techniques
