M.S. Computer Science
College of Science & Mathematics
The Information Visualization field focuses on the visualization of abstract data, and with the growing interest in big data analysis, the need for analyzing complex datasets is nowadays highly relevant. With the growing amount and diversity of these datasets new and exciting ways to visualize them are being developed. However, being able to thoroughly test and evaluate the effectiveness of these new visualization techniques is an arduous manual process. Moreover, different researchers hold different opinions on how to thoroughly evaluate a new visualization method. A step towards automating the process of evaluation of visualizations, called the Framework for the Evaluation of VizTools (FEV), was developed and is presented in this thesis. The FEV Framework combines, guidelines, scenarios, and tasks, generated by an extensive literature review, into an easy to use open-source and expandable software package. With FEV, researchers are able to generate evaluation task lists based on their own data, and using evaluation methods that have already been vetted by the visualization community. By using the FEV tool with a variety of visualizations, it was possible to generate full evaluation task lists for each of them. By making the framework open-source and with an adaptable architecture, new functionality can easily be added, enabling it to be used by researchers to evaluate an almost limitless number of visualization methods.
Taggart, Douglas, "Towards an automated system for evaluation of visualizations" (2014). Theses and Dissertations. 318.