Using visual representations for the searching and browsing of large, complex, multimedia data sets

Industry as a whole has become increasingly global and digitized in recent years, resulting in a huge increase in data generated by companies, projects and even individuals. This has led to great challenges in visualizing and searching for information. The speed and accuracy at which these large datasets can be effectively mined for information that is relevant and valuable can have a significant effect on company performance. Therefore, this research investigates the feasibility of using visual representations for the searching and browsing of large, complex, multimedia data sets. This paper introduces the SIZL (Searching for Information in a Zoom Landscape) system, which was developed to enable the authors to effectively test whether 2.5D environments can benefit effective data management. The usability of this visualization system was analyzed using experiments and a combination of quantitative and qualitative data collection methods. The paper presents these results and discusses potential industrial applications as well as future work that will improve the SIZL data visualization method

Final Report for Data Information Systems (DaISy) (Big Data) Programme – EPSRC Grant EP/J020338/1

This project investigates the feasibility of using visual representations for the searching and browsing of large, complex, multimedia data sets. It draws upon prior research that shows the human visual system has a powerful ability to recognise and classify objects in 2D and 3D environments

Technology and Motivation to Exercise: Data Display Formats, Progress Feedback, and Strength of Commitment for Personal Fitness

The purpose of this study, inspired by observations of the increased use of data-driven fitness activity trackers, is to measure how using different methods to display the same set of data influences perceptions of its value: understanding of the data, informative value of the display, and motivation to take action or change behavior. Previous research predominantly focuses on the effectiveness of wearables, not their displays. For this study, data was collected from 273 respondents: an approximately equal number of males and females ranging in age from 18 to 72 (average age of 31) from OSU undergraduates and Amazon’s Mechanical Turk. Participants evaluated different charts, tables, and graphs created from the same data set: 2 line graphs, 2 bar graphs, 1 pie chart, 1 table, 1 radar graph, and 3 visual displays. Questions assessed interpretation and understanding of the material as well as personal perception of the informational and motivational value of the displays. Respondents ranked motivational power of the displays in the following order (most to least): visual display, table, pie chart, line graph. Need for Cognition was included, and both those in the top 25% and the bottom 25% answered a context question more accurately using a table than a line graph despite looking at the table for less time. Respondents rated display characteristics related to data and information more important than appearance-related characteristics. Display format does influence the severity of inferences people deduce from data, and how meaningful they find the information to be. These results can be applied to the health and medical fields in general by providing insight into data display formats that are more likely to promote healthy diets, exercise, and other regimes such as medical prescription adherence.Max M. Fisher College of BusinessUndergraduate Student GovernmentNo embargoAcademic Major: Information System