SimulSort: Multivariate Data Exploration through an Enhanced Sorting Technique

Abstract

Sorting is one of the well-understood and widely-used interaction techniques. Sorting has been adopted in many software applications and supports various cognitive tasks. However, when used in analyzing multi-attribute data in a table, sorting appears to be limited. When a table is sorted by a column, it rearranges the whole table, so the insights gained through the previous sorting arrangements of another column are often difficult to retain. Thus, this study proposed an alternative interaction technique, called “SimulSort.” By sorting all of the columns simultaneously, SimulSort helps users see an overview of the data at a glance. Additional interaction techniques, such as highlighting and zooming, were also employed to alleviate the drawbacks of SimulSort. A within-subject controlled study with 15 participants was conducted to compare SimulSort and the typical sorting feature. The results showed typical sorting and SimulSort work with comparable efficiency and effectiveness for most of the tasks. Sorting more effectively supports understanding correlation and reading corresponding values, and SimulSort shows the potential to more effectively support tasks that need multi-attribute analyses. The implications of the results and planned future work are discussed as well.