The overall purpose of intelligence analysis platforms is to extract key information from multi-source data. Ultimately, these systems are meant to save intelligence analysts time and effort by offering knowledge discovery capabilities. However, intelligence analysis platforms only assist analysts to the extent they are designed with human factors in mind. Poorly designed intelligence analysis platforms can hinder the knowledge discovery process, or worse, promote the misinterpretation of analysis results. Future intelligence systems must be critical enablers for improving speed, efficiency, and effectiveness of command-level decision making. Human-centered research is needed to address the challenge of visualizing large data collections to facilitate orientation and context, enable the discovery and selection of relevant information, and provide dynamic feedback for identifying changes in the state of a targeted region or topic. From the perspective of the ‘Human as a Data Explorer,’ this study investigates the visual presentation of intelligence information to support timely and accurate decision making. The investigation is a starting point in understanding the rich and varied set of information visualizations sponsored by the Army in recent years. A human-subjects experiment explores two visualization approaches against a control condition for displaying sentiment about a set of topics with an emphasis on the performance metrics of decision accuracy and response time. The resulting data analysis is the first in a series of experiments providing input for technology development informing future interface designs and system prototypes.

Date Published: 15 May 2015
PDF: 7 pages
Proc. SPIE 9499, Next-Generation Analyst III, 94990H (15 May 2015); doi: 10.1117/12.2176218

Published in SPIE Proceedings Vol. 9499:
Next-Generation Analyst III
Barbara D. Broome; Timothy P. Hanratty; David L. Hall; James Llinas, Editor(s)