A Visual Analytics System for Rapid Sensemaking of Scientific Documents

Amirreza Haghverdiloo Barzegar, The University of Western Ontario

Abstract

With the rapid growth of scientific documents over the years, researchers must examine large collections of documents to keep up with their research fields. Over the past years, numerous tools have been developed to support researchers in making sense of the documents collection; however, due to the high load and complexity of scientific information, many of these tools have only covered basic tasks or restricted information items. This thesis describes a visual analytics system (i.e., a tool that integrates data visualization, human-data interaction, and machine learning) that helps researchers explore and examine scientific documents thoroughly and rapidly with an especial focus on the textual content of scientific documents. Through a usage and comparative scenario, we illustrated the efficiency and advantages of our system over similar tools. Finally, we discussed possible future extensions and upgrades thanks to the modular architecture of the system.