Developing a database to help analysts store and process requests for intelligence

As the largest navy in the world, the United States Navy must quickly retrieve and communicate information across the globe in order to protect the country and its allies.

As the amount of information that analysts must sort through grows exponentially, this creates a more difficult environment for extracting useful and relevant data.

This problem of information complexity in research is present in other disciplines, creating a demand for a system to increase both effectiveness and efficiency of research.

This research, conducted by MSDS students Nicholas Kim, Matthew DaVolio and Joel Stein, proposes the Research Assistant Management Platform to assist analysts in their duties in retrieving relevant maritime information. 

The platform is a realization of a proof of concept utilizing a database backend for storage of requests for intelligence, real-time updating topic models for document similarity and thematic analysis, and an intuitive front-end user interface with built-in work-flow operations, such as saving and model visualizations.

The system provides effective information retrieval and an integration of a previously fragmented multi-step process into one research platform. The system was evaluated based on user testing and the determined relevance of the displayed results from a query. The final topic model consists of 50 topics that allow analysts to improve their responses and learn from the existing corpus of requests for intelligence.