Crowdster is a framework that enables social navigation in web-based visualizations using crowdsourced evaluation. It collects previous users' interaction data from a visualization and integrates it back to help predict trends and evaluate the visualization. Crowdster addresses performance issues like stability and scalability. While in-field evaluation provides immediate summarized results, it risks misinterpreting user behaviors or collecting incorrect data. The framework could potentially be extended to other applications like collecting data from one visualization to feed another or aggregating data across multiple visualizations for group evaluation.

1. Crowdster: Enabling Social
Navigation in Web-based
Visualization using
Crowdsourced Evaluation
Yuetling Wong Purdue University
wong64@purdue.edu
Niklas Elmqvist University of Maryland
elm@umd.edu

2. Google Map Example

3. Social navigation
• Social navigation: collect previous users’
interaction data and integrate the data
back to the visualization to predict the
trend or evaluate the visualization

4. Background
Web-based
Visualizationmany users
Interaction
Database
interaction
visualization
process
events
user A
social visualization
Web server
capture
events
present
data +
logic
Web browser

5. What is Crowdster?

6. Crowdster in Evaluation
• Performance:
– Stability
– Scalability
• In-Field Evaluation
– Advantage: immediate summarized results
– Disadvantages: misinterpret user behaviors
and collect incorrect data

7. What’s next?
• Can this framework be extended to other
applications?
– Collect data from one visualization to feed
another visualization?
– Collect data from multiple visualizations to do
group evaluation?

8. Thank you!
Contact us:
wong64@purdue.edu
elm@umd.edu