Health Analytics and Cyber Security Software - Asymmetric Threat Analysis Tool ("Atrap")Technology #ua13-024
Questions about this technology? Ask a Technology Manager
- Jacob Gulotta Graduate Assistant
- Michael Valenzuela Graduate Assistant, Teaching, Electrical & Computer Engineering
- Jonathan Sprinkle Assistant Professor, Electrical & Computer Engineering
- Brian Ten Eyck Director, Engineering Research Development, Engineering Administration
- Jerzy Rozenblit University Distinguished Professor, Electrical & Computer Engineering
- Managed By
- Lewis Humphreys Licensing Manager (520) 626-2574
ATRAP was developed using University of Arizona research and built by Ephibian, a custom software development firm. From its inception, ATRAP was designed to aid analysts with information analysis of large data sets. It has applications in health analytics and cyber security.
ATRAP tools help analysts fuse, search, organize, analyze and collaborate on pertinent information amidst a sea of data; as well as provide analysts tools to guide future collections of data. ATRAP has a suite of tools to help analysts visualize and query data to find known patterns or unearth new patterns in the data, and share the resulting knowledge through collaboration tools.
The data can be viewed a variety of ways, depending on user preferences and objectives. It is useful in many environments to be able to visualize data in three dimensions — geographically, and in time sequence. ATRAP was designed from the outset to allow analysts to visualize patterns in data geographically, at a level of granularity the analyst specifies. It also allows manipulation of time, so that events can be explored not only where they happened but when they happened. In addition, ATRAP delivers several traditional 2-dimensional data visualization tools, such as link charts (to display the relationship between entities), and time wheels (to help visualize patterns of when activities occur).
It also has tools to help categorize data in easily understood data sets, called Dossiers, where related data elements (e.g., summary data, images, detailed documents and analyst notes) can be grouped together in an intuitive manner.
For all these tools, ATRAP provides mechanisms so that researchers and analysts can share the associated data and analyses as a collaborative team.