Health Analytics and Cyber Security Software - Asymmetric Threat Analysis Tool ("Atrap")

Technology #ua13-024

Questions about this technology? Ask a Technology Manager

Download Printable PDF

Categories
Researchers
Jerzy Rozenblit
University Distinguished Professor, Electrical & Computer Engineering
Brian Ten Eyck
Director, Engineering Research Development, Engineering Administration
Jonathan Sprinkle
Assistant Professor, Electrical & Computer Engineering
Michael Valenzuela
Graduate Assistant, Teaching, Electrical & Computer Engineering
Jacob Gulotta
Graduate Assistant
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.