Current state of the art and future directions: Augmented reality data visualization to support decision-making

IF 3.8 3区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Mengya Zheng, David Lillis, Abraham G. Campbell
{"title":"Current state of the art and future directions: Augmented reality data visualization to support decision-making","authors":"Mengya Zheng,&nbsp;David Lillis,&nbsp;Abraham G. Campbell","doi":"10.1016/j.visinf.2024.05.001","DOIUrl":null,"url":null,"abstract":"<div><p>Augmented Reality (AR), as a novel data visualization tool, is advantageous in revealing spatial data patterns and data-context associations. Accordingly, recent research has identified AR data visualization as a promising approach to increasing decision-making efficiency and effectiveness. As a result, AR has been applied in various decision support systems to enhance knowledge conveying and comprehension, in which the different data-reality associations have been constructed to aid decision-making.</p><p>However, how these AR visualization strategies can enhance different decision support datasets has not been reviewed thoroughly. Especially given the rise of big data in the modern world, this support is critical to decision-making in the coming years. Using AR to embed the decision support data and explanation data into the end user’s physical surroundings and focal contexts avoids isolating the human decision-maker from the relevant data. Integrating the decision-maker’s contexts and the DSS support in AR is a difficult challenge. This paper outlines the current state of the art through a literature review in allowing AR data visualization to support decision-making.</p><p>To facilitate the publication classification and analysis, the paper proposes one taxonomy to classify different AR data visualization based on the semantic associations between the AR data and physical context. Based on this taxonomy and a decision support system taxonomy, 37 publications have been classified and analyzed from multiple aspects. One of the contributions of this literature review is a resulting AR visualization taxonomy that can be applied to decision support systems. Along with this novel tool, the paper discusses the current state of the art in this field and indicates possible future challenges and directions that AR data visualization will bring to support decision-making.</p></div>","PeriodicalId":36903,"journal":{"name":"Visual Informatics","volume":"8 2","pages":"Pages 80-105"},"PeriodicalIF":3.8000,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468502X24000202/pdfft?md5=f80d87851c5113d4a9dd7255cbbe2978&pid=1-s2.0-S2468502X24000202-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Visual Informatics","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2468502X24000202","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0

Abstract

Augmented Reality (AR), as a novel data visualization tool, is advantageous in revealing spatial data patterns and data-context associations. Accordingly, recent research has identified AR data visualization as a promising approach to increasing decision-making efficiency and effectiveness. As a result, AR has been applied in various decision support systems to enhance knowledge conveying and comprehension, in which the different data-reality associations have been constructed to aid decision-making.

However, how these AR visualization strategies can enhance different decision support datasets has not been reviewed thoroughly. Especially given the rise of big data in the modern world, this support is critical to decision-making in the coming years. Using AR to embed the decision support data and explanation data into the end user’s physical surroundings and focal contexts avoids isolating the human decision-maker from the relevant data. Integrating the decision-maker’s contexts and the DSS support in AR is a difficult challenge. This paper outlines the current state of the art through a literature review in allowing AR data visualization to support decision-making.

To facilitate the publication classification and analysis, the paper proposes one taxonomy to classify different AR data visualization based on the semantic associations between the AR data and physical context. Based on this taxonomy and a decision support system taxonomy, 37 publications have been classified and analyzed from multiple aspects. One of the contributions of this literature review is a resulting AR visualization taxonomy that can be applied to decision support systems. Along with this novel tool, the paper discusses the current state of the art in this field and indicates possible future challenges and directions that AR data visualization will bring to support decision-making.

技术现状和未来方向:支持决策的增强现实数据可视化
增强现实(AR)作为一种新颖的数据可视化工具,在揭示空间数据模式和数据与上下文的关联方面具有优势。因此,最近的研究发现,AR 数据可视化是提高决策效率和效果的一种有前途的方法。因此,AR 已被应用于各种决策支持系统,以加强知识的传达和理解,其中不同的数据-现实关联已被构建以帮助决策。尤其是在大数据兴起的现代社会,这种支持对未来几年的决策至关重要。使用 AR 将决策支持数据和解释数据嵌入最终用户的物理环境和焦点情境中,可避免将人类决策者与相关数据隔离开来。在 AR 中整合决策者的情境和 DSS 支持是一项艰巨的挑战。为了便于对出版物进行分类和分析,本文根据 AR 数据与物理情境之间的语义关联,提出了一种分类法来对不同的 AR 数据可视化进行分类。根据该分类法和决策支持系统分类法,从多个方面对 37 篇出版物进行了分类和分析。本文献综述的贡献之一是提出了可应用于决策支持系统的 AR 可视化分类法。除了这个新颖的工具之外,本文还讨论了该领域的技术现状,并指出了 AR 数据可视化在支持决策方面未来可能面临的挑战和发展方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Visual Informatics
Visual Informatics Computer Science-Computer Graphics and Computer-Aided Design
CiteScore
6.70
自引率
3.30%
发文量
33
审稿时长
79 days
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信