Your SlideShare is downloading. ×
0
InfoVis General
InfoVis General
InfoVis General
InfoVis General
InfoVis General
InfoVis General
InfoVis General
InfoVis General
InfoVis General
InfoVis General
InfoVis General
InfoVis General
InfoVis General
InfoVis General
InfoVis General
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

InfoVis General

1,156

Published on

General presentation on some InfoVis work, lots of pictures

General presentation on some InfoVis work, lots of pictures

Published in: Education, Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
1,156
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
13
Comments
0
Likes
0
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide
  • This should be a half hour intro talk on why people should use visualisation techniques
  • Transcript

    • 1. I NFORMATION V ISUALISATION
    • 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
    • 5. WHY Low weight but rubbish Finding outliers / errors fuel economy
    • 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 firstEick 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

    ×