Data Intensive Computing
Interactive Visual Content Analysis of Real-Time Data Streams
Principal Investigator: George Chin
Challenge
As real-time problem solvers and decision-makers, humans need to be able to quickly comprehend and act upon the meaning and context of dynamic information.
Visual Content Analysis of Real-Time Data Streams
Approach
- Research and develop rapid and intuitive interactive visualizations that allow users to visually view, navigate, and explore high-volume, real-time data streams.
- Extend visual techniques and paradigms for static data to exploit the dynamic and data intensive nature of real-time data
- Support multiple visual contexts (e.g., spatial, temporal, categorical, hierarchical, etc.) and multimedia types
- Focus on cognition and comprehension
Impact
This research provides plug-and-play visualizations, adaptive visual content analysis and automatic temporal analysis capabilities, allowing users to make rapid first-cut analyses of real-time data and visually monitor real-time situations.
Collaborations
- Other LDRD projects (for data filtering and feature extraction)
- PNNL cyber security and counterintelligence analysts (for requirements and data sources)
Accomplishments
- Requirements gathering with scientists and analysts
- Evaluated open-source visualization frameworks and selected Prefuse visualization toolkit for project
- Developed use cases based on real-world problems (e.g., computer network traffic, disease outbreak data, WMD classifications)