III: Visual Analytics for Steering Large-Scale Distributed Data Mining Applications

Rutgers University New Brunswick, New Brunswick NJ

Investigators

Abstract

The primary goal of this project is to provide a novel framework and software that will empower users to more effectively understand and apply information discovered in large distributed data environments. The approach is to provide tested and novel data analytics techniques, supported by a framework that integrates expertise and insight of human users. The novel combination of the skills, abilities and experience of human users with the sheer processing power of distributed computation provides an extraordinary synergistic information-processing potential -- termed Interactive Automation. This framework places the user explicitly in the center of the Design, Execution, and Analysis processing modes, allowing them to switch between these contexts fluidly as well as react and guide the runtime processing of complex, distributed and dynamic datasets. A secondary goal is to provide an authoritative, accurate, anonymized and openly available set of ground truth data in the law enforcement domain. At present, no such ground truth dataset currently exists, and currently analytics tools are evaluated using proprietary, confidential or otherwise closed datasets. Stable releases of the system modules, documentation, related publications and results from usability studies are available at the project website (www.dimacs.rutgers.edu). The broader impacts of this work lie regionally with our partners (in law enforcement, medicine and education), and broadly through dissemination of the dataset and software in academia and industry. In addition, the availability of the dataset will support related research efforts by providing a foundation for objective, comparative and scientific analysis of law enforcement data analytics tools.