1. University of Amsterdam
Amir Masoud Abdol
Distributed and
Collaborative
Visualization
K.W. Brodlie, D.A, Duce, J.R. Gallop, J.P.R.B Walton and J.D. Wood

2. Definitions
▪ Scientific Visualization is computationally intensive.
▪ Many Machine: Grid computing allows us to do intensive job.
▪ Many human: Collaboration allows group of people to think about one or more
subject at the same time.
▪ Goal:
▪ To combine the collaboration workspaces or features like video conferencing or file sharing with the
power of Grid computing.
▪ To harness the processing power of many humans and many machines.

3. Different Combinations of Using Many People
and Many Machine
▪ Distributed Visualization System
▪ Facilities to use many machine
▪ Collaborative Visualization System
▪ Facilities to use share and talk about visualization result
▪ Distributed and Collaborative Visualization System
▪ Facilities to use many machine and share result or maybe modifying model

4. What you should consider to build such a
system?
▪ Rules and Privilege of Users
▪ Users should have different rules in process
▪ Place and Time of Process
▪ Different situation according to the time and place of members
▪ Layers of the System
▪ Different layers of distributed collaborative system

5. Applegate Place-time
matrix
Same time, same place
Meeting in meeting room
Different time, different place
Sending data via mails or fax
Same time, different place
Video Conferencing
Different time, same place!

6. Layers of System: 3-Layer Model
▪ Conceptual Layer (Visualization Design)
▪ Logical Layer (Software Engineering)
▪ Physical Layer (Implementation)

7. Conceptual Layer (Visualization Design)
Tasks Possible Collaboration
▪ Designing the visualization process ▪ Using video conference to argue
different method
▪ Screen Sharing
▪ Document Sharing
▪ Editing,
▪ Annotation,
▪ and …

8. Logical Layer (Software engineering)
Tasks Possible Collaboration
▪ Expressing visualization as a ▪ Using packages on local or server
particular configuration of software
entities without considering ▪ Using data on local or server
hardware.
▪ Computing results on the cloud
▪ Sub-layer
▪ Logical visualization design ▪ See data on cloud, download it and
▪ Description of the composition of modules visualize it on local machine
into a network
▪ Core software
▪ Available modules in a system

9. Physical Layer (Implementation)
Tasks Possible Collaboration
▪ Binding logical visualization • Working on code
design and core software to
particular resources.

10. Different implementations of
Distributed and
Collaborative Visualization

11. Client Based
Visualization
Data on Server
•Private (Only one person can
access)
•Shared (Many users can
access it)
Data and Design
•Visualization design is also
available on Server

12. Server Based
Visualization
• Image display on client
• Model rendering on the client

13. Web Based
Visualization
Java for
communication and
Choosing Design
visualization
Full Service
• Visualization is created remotely
by the service provider
• Only Graphical data deliver to
local system
Java for Interactivity
• Using Java Applet for
interactivity
Software Delivery
Main difference between Web-based and Server-Based
• Visualization Software download
maybe is data representation, In Server-based user can use to local machine, and read data
any core software but in Web-based Java uses to from server and visualize it.
implement the result into HTML pages.

14. Different Factors of Distributed and
Collaborative Visualization System
▪ Base Visualization System Collaboration
▪ Ability to collaborate between different visualization system
▪ Multiple Platform
▪ Collaboration between different platform, for instance, operation systems
▪ Functionality
▪ Exchanging of data, Exchanging of parameters, Exchanging of modules, Exchanging of
networks

15. Different Factor of Collaborative Visualization
System
▪ Participation
▪ Setting up the system (Adminstratation)
▪ Joining/ Leaving (Registration)
▪ Automatic Launch/connection (Sharing Elements)
▪ Floor Control (Privileges)
▪ Data Privacy
▪ Global View

16. Different Factor of Collaborative Visualization
System
▪ System
▪ Performance
▪ Reliability
▪ Scalability
▪ Target Users
▪ Visualization Programmer
▪ Visualization End User

17. System and Framework
▪ Visualization Tools
▪ VTK
▪ Amira
▪ AVS5 and AVS/Express, Cactus, COVISE, Ensight, IDL and PV-Wave, IRIS
Explorer, MATLAB, OpenDX, pV3, SCIRun, VisAD
▪ Collaboration Tools
▪ Desktop Sharing Tools
▪ VNC
▪ NetMeeting
• Remote Rendering
• SGI VIzserver

18. Questions?