Transcript CTS08

Social-Aware Collaborative
Visualization for Large Scientific
Projects
Kwan-Liu Ma and Chaoli Wang
CTS’08 5/21/2008
What is a collaboratory?
 A “center without walls” [Wulf 93], in which
researchers can
 Perform research regardless of physical
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


locations
Interact with colleagues
Make use of instrumentation
Share data and computational resources
Access information in digital libraries
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
Functions of current collaboratories
 Data repository
 Tool warehouse
 Computing resource
 Web-interface for information retrieval
 What are missing?
 Social context and activities
 Collective analysis
Social-aware collaboration
User centric
Annotation
s
Logs
Emails
Users
Tools
Data
Tool/data centric
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
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
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
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
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
Ontology graph
 Node size: disparity of connected types for each node type
 # on edge: frequencies of links between two types
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
OntoVis – structural abstraction
 Abstraction of the visualization of five roles and related actors
OntoVis – importance filtering
 The three major genres (in green) of Woody Allen’s movies are comedy,
romantic, and drama
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
VICA
Thickness: size
Simulation run
Color: authorship
# layers: # annotations
VICA – hit count saturation
VICA – author focus
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
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
Image graphs
 A visual representation of data exploration process
 Represent the results as well as the process of data
visualization
Image graphs
 Edge editing: replace the color transfer function of
node 3 with the color map of node 7
Image graphs
 A forward propagation of the color transfer function
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
Acknowledgements
 DOE SciDAC program
 DEFC02-06ER25777
 NSF
 CCF-0634913
 OCI-0325934
 CNS-0551727
 Collaborators
 Zeqian Shen, Yue Wang, James Shearer @ UC Davis
 Greg Schussman @ SLAC
 Tina Eliassi-Rad @ LLNL