Enabling effective tree exploration using visual cues. (August 2018)

• Record Type: Journal Article
• Authors: Nguyen, Quang Vinh; Arness, David; Sanderson, Carrissa J.; Simoff, Simeon; Huang, Mao Lin
• Is Part Of: Journal of visual languages & computing. Volume 47(2018)
• Page Start: 44
• Page End: 61
• Publication Date: 2018-08
• Subjects: Interactive visualization -- Tree visualization -- Visual cue -- DOITree -- Topological preview; Visual programming languages (Computer science) -- Periodicals; Visual programming (Computer science) -- Periodicals; Programming languages (Electronic computers) -- Semantics -- Periodicals; Langages de programmation visuelle -- Périodiques; Programmation visuelle -- Périodiques; Langages de programmation -- Sémantique -- Périodiques; Programming languages (Electronic computers) -- Semantics; Visual programming (Computer science); Visual programming languages (Computer science); Periodicals; Electronic journals; 005
• DOI: 10.1016/j.jvlc.2018.06.001
• Languages: English
• ISSNs: 1045-926X

Abstract

Abstract: This article presents a new interactive visualization for exploring large hierarchical structures by providing visual cues on a node link tree visualization. Our technique provides topological previews of hidden substructures with three types of visual cues including simple cues, tree cues and treemap cues . We demonstrate the visual cues on Degree-of-Interest Tree (DOITree) due to its familiar mapping, its capability of providing multiple focused nodes, and its dynamic rescaling of substructures to fit the available space. We conducted a usability study with 28 participants that measured completion time and accuracy across five different topology search tasks. The simple cues had the fastest completion time across three of the node identification tasks. The treemap cues had the highest rate of correct answers on four of the five tasks, although only reaching statistical significance for two of these. As predicted, user ratings demonstrated a preference for the easy to understand tree cues followed by the simple cue, despite this not consistently reflected in performance results.