{"title":"Qutaber: task-based exploratory data analysis with enriched context awareness","authors":"Qi Jiang, Guodao Sun, Tong Li, Jingwei Tang, Wang Xia, Sujia Zhu, Ronghua Liang","doi":"10.1007/s12650-024-00975-1","DOIUrl":null,"url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>Exploratory data analysis (EDA) has emerged as a critical tool for users to gain deep insights into data and unearth hidden patterns. The integration of recommendation algorithms has enhanced its capabilities and further popularized its utilization. Most recommendation-based EDA methods concentrate on the extraction of pivotal insights from datasets, and the taxonomy of these insights is well-established. However, the support for further analytical endeavors to expand these initial findings remains constrained, as evidenced by the restricted scope of analytical intents that are tailored to specific scenarios. Moreover, these systems often lack sufficient context-awareness capabilities, failing to equip users with the necessary tools for a thorough exploration of extensive recommendations. To address these limitations, we introduce Qutaber, a task-based EDA system with enriched context-awareness. We first summarize six core analytical tasks tailored for EDA scenarios through literature reviews and expert interviews. Then, Qutaber integrates the use of small multiples, enhanced with a multi-metric re-ranking function, to enable a thorough and efficient examination of expanded charts pertaining to various analytical tasks. Furthermore, a machine learning method is leveraged to characterize the semantic features of these charts for a holistic landscape of recommended charts. Finally, a case study using a real-world dataset demonstrates Qutaber’s practical application, followed by a user study to further evaluate the usability of the proposed techniques. Our findings illustrate that Qutaber facilitates an effective and context-rich EDA experience for users.</p><h3 data-test=\"abstract-sub-heading\">Graphic abstract</h3>","PeriodicalId":54756,"journal":{"name":"Journal of Visualization","volume":"60 1","pages":""},"PeriodicalIF":1.7000,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Visualization","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s12650-024-00975-1","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
Exploratory data analysis (EDA) has emerged as a critical tool for users to gain deep insights into data and unearth hidden patterns. The integration of recommendation algorithms has enhanced its capabilities and further popularized its utilization. Most recommendation-based EDA methods concentrate on the extraction of pivotal insights from datasets, and the taxonomy of these insights is well-established. However, the support for further analytical endeavors to expand these initial findings remains constrained, as evidenced by the restricted scope of analytical intents that are tailored to specific scenarios. Moreover, these systems often lack sufficient context-awareness capabilities, failing to equip users with the necessary tools for a thorough exploration of extensive recommendations. To address these limitations, we introduce Qutaber, a task-based EDA system with enriched context-awareness. We first summarize six core analytical tasks tailored for EDA scenarios through literature reviews and expert interviews. Then, Qutaber integrates the use of small multiples, enhanced with a multi-metric re-ranking function, to enable a thorough and efficient examination of expanded charts pertaining to various analytical tasks. Furthermore, a machine learning method is leveraged to characterize the semantic features of these charts for a holistic landscape of recommended charts. Finally, a case study using a real-world dataset demonstrates Qutaber’s practical application, followed by a user study to further evaluate the usability of the proposed techniques. Our findings illustrate that Qutaber facilitates an effective and context-rich EDA experience for users.
Journal of VisualizationCOMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY
CiteScore
3.40
自引率
5.90%
发文量
79
审稿时长
>12 weeks
期刊介绍：
Visualization is an interdisciplinary imaging science devoted to making the invisible visible through the techniques of experimental visualization and computer-aided visualization.
The scope of the Journal is to provide a place to exchange information on the latest visualization technology and its application by the presentation of latest papers of both researchers and technicians.
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