Layered Multi-Modal Network Analysis of Textual Data for Improved Situation Awareness

摘要

A team of researchers at the Air Force Research Laboratory's Information Directorate investigated combining multiple textual databases that consist of relational data to determine from different data networks if there is an improvement in a user's situation awareness. Currently, users manually process heterogeneous data types and are required to make mental correlations to merge databases and derive patterns in space and time across the different network types. This is because the vast majority of this data does not exist independently, as much of it is related and connected across networks. The researchers experimented with combining heterogeneous relational data types to determine the impact on situation awareness. The findings of this effort support future research to establish a layered multi-modal network analysis approach that would connect numerous data types for analysis purposes.