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

Users

DataTools

Annotations

LogsEmailsEmails

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, 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

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-0634913 OCI-0325934 CNS-0551727

Collaborators Zeqian Shen, Yue Wang, James Shearer @ UC Davis Greg Schussman @ SLAC Tina Eliassi-Rad @ LLNL