Scalable Visual Analytics

Summary

Visual analytics is the science of analytical reasoning facilitated by interactive visual interface. This project aims to design and evaluate new visual representations and interaction techniques for huge complex data sets such as social networks and biological networks.

Supervisor(s)

Professor Seok-Hee Hong

Research Location

Computer Science

Program Type

Masters/PHD

Synopsis

 Synopsis: Technological advances such as sensors have increased data volumes in the last few years, and now we are experiencing a “data deluge” in which data is produced much faster than it can be used by humans. Further, these huge and complex data sets have grown in importance due to factors such as international terrorism, the success of genomics, increasingly complex software systems, and widespread fraud on stock markets.

We aim to develop new visual representation, visualization and interaction methods for humans to find patterns in huge abstract data sets, especially network data sets. These data sets include social networks, telephone call networks, biological networks, physical computer networks, stock buy-sell networks, and transport networks. These new visualization and interaction methods are in high demand by industry.

Keywords

algorithm, graph theory, graph drawing, Information visualization, Data Mining, network analysis, large and complex networks, social networks, biological networks, interaction

Opportunity ID

The opportunity ID for this research opportunity is: 1032

Other opportunities with Professor Seok-Hee Hong

• Drawing Algorithms for Almost Planar Graphs
• Algorithms for Drawing Non-Planar Graphs in Three Dimensions
• MultiPlane Graph Embedding (2.5D Graph Embeddability)
• Visualization and Analysis of Large and Complex Biological Networks and Social Networks
• 2.5D Graph Navigation and Interaction Techniques
• Drawing Algorithms for Almost Planar Graphs