Defragmenting Research Areas with Knowledge Visualization and Visual Text Analytics

Abstract

The increasing specialization of science is motivating the fragmentation of traditional and well-established research areas into interdisciplinary communities of practice that focus on cooperation between experts to solve problems in a wide range of domains. This is the case of problem-driven visualization research (PDVR), in which groups of scholars use visualization techniques in different application domains such as the digital humanities, bioinformatics, sports science, or computer security. In this paper, we employ the findings obtained during the development of a novel visual text analytics tool we built in previous studies, GlassViz, to automatically detect interesting knowledge associations and groups of common interests between these communities of practice. Our proposed method relies on the statistical modeling of author-assigned keywords to make its findings, which are demonstrated in two use cases. The results show that it is possible to propose interactive, semisupervised visual approaches that aim at defragmenting a body of research using text-based, automatic literature analysis methods.

Visual Analytics for Decision Support: A Supply Chain Perspective

“…The focus of the previous studies has not been mainly on SC VA. In general, majority of the previous studies are either focusing on the application of VA in other topics [10]- [12] or even if it is considered from a SC perspective, it is mainly focused on VA itself [13] and the SC related activity has been mostly used only as a case study for the proposed VA, making it difficult to identify all SC specific VA studies from the literature.…”