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Interactive Querying over Large Network Data: Scalability, Visualization, and Interaction Design
Robert Pienta1, Acar Tamersoy1, Hanghang Tong2
1Georgia Tech.
IUI. International Conference on Intelligent User Interfaces
|April 11, 2015
Summary
Interactive graph querying systems are essential for exploring large network data. Our research develops scalable algorithms and intuitive interfaces for efficient graph data exploration, moving towards a complete interactive querying solution.
Area of Science:
- Computer Science
- Data Mining
- Human-Computer Interaction
Background:
- Modern graph data is growing exponentially, necessitating advanced querying methods.
- Current querying languages for complex graph structures can be cumbersome.
- Interactive graph querying offers a more accessible approach to network data analysis.
Purpose of the Study:
- To design and develop an interactive querying system for large network data.
- To address the need for efficient querying of complex graph structures.
- To bridge the gap between complex data mining and user accessibility.
Main Methods:
- Developing scalable data mining algorithms for large-scale graph analysis.
- Implementing advanced graph visualization techniques.
- Focusing on robust interaction design for user-friendly querying.
Main Results:
- An approximate subgraph matching system (MAGE) has been developed, capable of querying graphs with hundreds of millions of edges.
- Preliminary work on visual graph querying (Graphite) has been completed.
- Ongoing development of graph visualization and interaction design for a complete system.
Conclusions:
- Scalable algorithms and effective visualization/interaction are key to interactive graph querying.
- The developed system aims to simplify the exploration of large network datasets.
- Further research is focused on integrating components for a fully interactive querying experience.
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