Quang Vinh Nguyen , David Arness , Carrissa J. Sanderson , Simeon Simoff , Mao Lin Huang
{"title":"Enabling effective tree exploration using visual cues","authors":"Quang Vinh Nguyen , David Arness , Carrissa J. Sanderson , Simeon Simoff , Mao Lin Huang","doi":"10.1016/j.jvlc.2018.06.001","DOIUrl":null,"url":null,"abstract":"<div><p>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 <em>simple cues, tree cues</em> and <em>treemap cues</em><span>. 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 </span><em>simple cues</em> had the fastest completion time across three of the node identification tasks. The <em>treemap cues</em> 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 <em>tree cues</em> followed by the <em>simple cue</em>, despite this not consistently reflected in performance results.</p></div>","PeriodicalId":54754,"journal":{"name":"Journal of Visual Languages and Computing","volume":"47 ","pages":"Pages 44-61"},"PeriodicalIF":0.0000,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.jvlc.2018.06.001","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Visual Languages and Computing","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1045926X1630009X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Computer Science","Score":null,"Total":0}
引用次数: 1
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.
期刊介绍:
The Journal of Visual Languages and Computing is a forum for researchers, practitioners, and developers to exchange ideas and results for the advancement of visual languages and its implication to the art of computing. The journal publishes research papers, state-of-the-art surveys, and review articles in all aspects of visual languages.