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This paper explores the concept of collaboratories, which are "centers without walls" that allow researchers to perform research regardless of location, interact with colleagues, share data and resources, and access information in digital libraries. The paper discusses the functions of current collaboratories, the missing social context and activities, and the challenges in creating a successful collaboratory. It also presents social-aware collaboration techniques, such as annotizers and visualization tools, for collecting and analyzing social data in collaboratories.
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Social-Aware Collaborative Visualization for Large Scientific Projects Kwan-Liu Ma and Chaoli WangCTS’08 5/21/2008
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 User centric Emails Logs Annotations Users Data Tools 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
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