Social-Aware Collaborative Visualization for Large Scientific Projects Kwan-Liu Ma and Chaoli Wang CTS’085/21/2008

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

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

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 Users Data Tools Annotation s Logs s Tool/data centric User 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, , 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

 Online collaboration system of International Linear Collider (ILC) project  Researchers from the US, Japan, and Germany  Collaborative annotation feature ModeVis Interface Simulation run Image Animation

VICA Simulation run Color: authorship Thickness: size # 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  OCI  CNS  Collaborators  Zeqian Shen, Yue Wang, James UC Davis  Greg SLAC  Tina LLNL