CHI '94 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
FOCUS: the interactive table for product comparison and selection
Proceedings of the 9th annual ACM symposium on User interface software and technology
Multidimensional information visualization through sliding rods
AVI '00 Proceedings of the working conference on Advanced visual interfaces
Parallel bargrams for consumer-based information exploration and choice
Proceedings of the 14th annual ACM symposium on User interface software and technology
Angular Brushing of Extended Parallel Coordinates
INFOVIS '02 Proceedings of the IEEE Symposium on Information Visualization (InfoVis'02)
Semiology of graphics
Low-Level Components of Analytic Activity in Information Visualization
INFOVIS '05 Proceedings of the Proceedings of the 2005 IEEE Symposium on Information Visualization
How to filter out random clickers in a crowdsourcing-based study?
Proceedings of the 2012 BELIV Workshop: Beyond Time and Errors - Novel Evaluation Methods for Visualization
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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.