SocialAware Collaborative Visualization for Large Scientific Projects

1
Social-Aware Collaborative Visualization for
Large Scientific Projects
Kwan-Liu Ma and Chaoli WangCTS08 5/21/2008
2
What is a collaboratory?

• A center without walls Wulf 93, in which
  researchers can
• Perform research regardless of physical locations
• Interact with colleagues
• Make use of instrumentation
• Share data and computational resources
• Access information in digital libraries

3
Examples of collaboratory

• Upper Atmospheric Research Collaboratory, 1993
• Multidisciplinary research collaboration for
  space scientists
• TeleMed, 1997
• International health care collaboratory
• DOE National Collaboratories Program, 1998
• Particle Physics Data Grid Collaboratory Pilot
• Earth System Grid II
• National Fusion Collaboratory
• Collaboratory for Multi-Scale Chemical Science
• Open scientific discovery infrastructure
• DOE Science Grid, 2001
• NSF TeraGrid, 2001

4
Functions of current collaboratories

• Data repository
• Tool warehouse
• Computing resource
• Web-interface for information retrieval
• What are missing?
• Social context and activities
• Collective analysis

5
Social-aware collaboration
User centric
Logs
Emails
Annotations
Users
Data
Tools
Tool/data centric
6
Social context of collaboration

• Key challenges in creating a collaboratory
• Social rather than technical Henline 98
• A collaboratory is an organizational form
• Also includes social process Cogburn 03
• Users of collaboratory
• 17 to 215 users per collaboratory, 1992 to 2000
  Sonnenwald 03
• Communication could be large and complex

7
Next-generation collaboratory

• Support social aspect of collaboration
• Associations between data and users
• Interactions and communications among users
• Visualization and analysis
• Social context and activities
• Heterogeneous information (text, table, graph,
  image, and animation etc.)
• Knowledge discovery
• Extraction, consolidation, and utilization
• Share knowledge about the data

8
Where and how to collect social data

• Source of social data
• Log, annotation, email, instance messenger, wiki
  website
• How to collect them
• Automatic recording user activities
• Data mining for information retrieval
• Related issues
• Context vs. content
• Security and privacy

9
Social context activities

• Annotizer Jung et al. 06
• An online annotation system for creating,
  sharing, and searching annotations on existing
  HTML contents
• OntoVis Shen et al. 06
• A visual analytics tool for understanding large,
  heterogeneous social networks
• VICA Wang et al. 07
• A Vornoni interface for visualizing collaborative
  annotations

10
OntoVis

• Large, heterogeneous social network
• Techniques
• Semantic abstraction
• Structural abstraction
• Importance filtering
• Example the movie network
• Eight node types
• Person, movie, role, studio, distributor, genre,
  award, and country
• 35,312 nodes, 108,212 links

11
Ontology graph

• Node size disparity of connected types for each
  node type
• on edge frequencies of links between two types

12
OntoVis semantic abstraction

• Visualization of all the people have played any
  of the five roles hero, scientist, love
  interest, sidekick, and wimp
• Red nodes are roles and blue nodes are actors

13
OntoVis structural abstraction

• Abstraction of the visualization of five roles
  and related actors

14
OntoVis importance filtering

• The three major genres (in green) of Woody
  Allens movies are comedy, romantic, and drama

15
ModeVis Interface
Image
Simulation run
Animation

• Online collaboration system of International
  Linear Collider (ILC) project
• Researchers from the US, Japan, and Germany
• Collaborative annotation feature

16
VICA
Thickness size
Simulation run
Color authorship
layers annotations
17
VICA hit count saturation
18
VICA author focus
19
Collective analysis

• Design gallery Marks et al. 97
• Automatic generation of rendering results by
  varying input parameters and arranging them into
  2D layout
• Image graph Ma 99
• A dynamic graph for representing the process of
  visual data exploration
• Visualization by analogy Scheidegger et al. 07
• Query-by-example in the context of an ensemble of
  visualizations

20
Visualizing visualizations

• Visual data exploration
• Iterative and explorative process
• Contains a wealth of information parameters,
  results, history, relationships among them
• The process itself can be stored, tracked, and
  analyzed
• Learn lessons and share experiences
• The process can be incorporated into a
  visualization system

21
Image graphs

• A visual representation of data exploration
  process
• Represent the results as well as the process of
  data visualization

22
Image graphs

• Edge editing replace the color transfer function
  of node 3 with the color map of node 7

23
Image graphs

• A forward propagation of the color transfer
  function

24
Concluding remarks

• Scientific collaboration
• Intrinsically social interaction among
  collaborators
• From data/tool centric to user centric
• Enhance existing collaborative spaces with
• Social context
• Collective analysis
• Visualization plays a key role in
• Collaborative space management
• Knowledge discovery

25
Acknowledgements

• DOE SciDAC program
• DEFC02-06ER25777
• NSF
• CCF-0634913
• OCI-0325934
• CNS-0551727
• Collaborators
• Zeqian Shen, Yue Wang, James Shearer _at_ UC Davis
• Greg Schussman _at_ SLAC
• Tina Eliassi-Rad _at_ LLNL