PUNGA: Provenance-supported Undirected Node Graph Analytics

Faculty: 
Alex Endert
Students: 
John Thompson, Arjun Srinivasan, and Brad Thwaites

PUNGA (Provenance-supported Undirected Node Graph Analytics) is a tool for intelligence analysts. PUNGA assists analysts in making sense of a large textual-based dataset by supporting data processing (Named Entity Recognition), data cleaning, data analysis, and analytic provenance.

PUNGA provides users the ability to combine, format, clean the data as per their convenience before and during analysis with the Entity View. PUNGA also facilitates user interaction with the data sets in a number of linked views. These visualizations include the Document Viewer, the Node Graph View, and the Calendar View. Finally, PUNGA provides a Provenance View that displays quantitative values that summarize the analysis session and more importantly help in analytic provenance.

Lab: 
Director: 
Alex Endert
Faculty: 
Alex Endert
Our goal is to help people make sense of data. We research and develop interactive visualizations that couple machine learning with visual interfaces of data for exploration and sensemaking.