V4RIN:利用领域知识对区域产业网络进行可视化分析。

IF 3.2 4区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Visual Computing for Industry Biomedicine and Art Pub Date : 2024-05-15 DOI:10.1186/s42492-024-00164-9
Wenli Xiong, Chenjie Yu, Chen Shi, Yaxuan Zheng, Xiping Wang, Yanpeng Hu, Hong Yin, Chenhui Li, Changbo Wang
{"title":"V4RIN:利用领域知识对区域产业网络进行可视化分析。","authors":"Wenli Xiong, Chenjie Yu, Chen Shi, Yaxuan Zheng, Xiping Wang, Yanpeng Hu, Hong Yin, Chenhui Li, Changbo Wang","doi":"10.1186/s42492-024-00164-9","DOIUrl":null,"url":null,"abstract":"<p><p>The regional industry network (RIN) is a type of financial network derived from industry networks that possess the capability to describe the connections between specific industries within a particular region. For most investors and financial analysts lacking extensive experience, the decision-support information provided by industry networks may be too vague. Conversely, RINs express more detailed and specific industry connections both within and outside the region. As RIN analysis is domain-specific and current financial network analysis tools are designed for generalized analytical tasks and cannot be directly applied to RINs, new visual analysis approaches are needed to enhance information exploration efficiency. In this study, we collaborated with domain experts and proposed V4RIN, an interactive visualization analysis system that integrates predefined domain knowledge and data processing methods to support users in uploading custom data. Through multiple views in the system panel, users can comprehensively explore the structure, geographical distribution, and spatiotemporal variations of the RIN. Two case studies were conducted and a set of expert interviews with five domain experts to validate the usability and reliability of our system.</p>","PeriodicalId":29931,"journal":{"name":"Visual Computing for Industry Biomedicine and Art","volume":null,"pages":null},"PeriodicalIF":3.2000,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11096142/pdf/","citationCount":"0","resultStr":"{\"title\":\"V4RIN: visual analysis of regional industry network with domain knowledge.\",\"authors\":\"Wenli Xiong, Chenjie Yu, Chen Shi, Yaxuan Zheng, Xiping Wang, Yanpeng Hu, Hong Yin, Chenhui Li, Changbo Wang\",\"doi\":\"10.1186/s42492-024-00164-9\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The regional industry network (RIN) is a type of financial network derived from industry networks that possess the capability to describe the connections between specific industries within a particular region. For most investors and financial analysts lacking extensive experience, the decision-support information provided by industry networks may be too vague. Conversely, RINs express more detailed and specific industry connections both within and outside the region. As RIN analysis is domain-specific and current financial network analysis tools are designed for generalized analytical tasks and cannot be directly applied to RINs, new visual analysis approaches are needed to enhance information exploration efficiency. In this study, we collaborated with domain experts and proposed V4RIN, an interactive visualization analysis system that integrates predefined domain knowledge and data processing methods to support users in uploading custom data. Through multiple views in the system panel, users can comprehensively explore the structure, geographical distribution, and spatiotemporal variations of the RIN. Two case studies were conducted and a set of expert interviews with five domain experts to validate the usability and reliability of our system.</p>\",\"PeriodicalId\":29931,\"journal\":{\"name\":\"Visual Computing for Industry Biomedicine and Art\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2024-05-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11096142/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Visual Computing for Industry Biomedicine and Art\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1186/s42492-024-00164-9\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Visual Computing for Industry Biomedicine and Art","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1186/s42492-024-00164-9","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0

摘要

区域产业网络(RIN)是从产业网络中衍生出来的一种金融网络,具有描述特定区域内特定产业之间联系的能力。对于大多数缺乏丰富经验的投资者和金融分析师来说,产业网络提供的决策支持信息可能过于模糊。相反,RIN 则能更详细、更具体地表达区域内外的产业联系。由于 RIN 分析是针对特定领域的,而当前的金融网络分析工具是为通用分析任务而设计的,无法直接应用于 RIN,因此需要新的可视化分析方法来提高信息探索效率。在本研究中,我们与领域专家合作,提出了 V4RIN 交互式可视化分析系统,该系统整合了预定义的领域知识和数据处理方法,支持用户上传自定义数据。通过系统面板上的多个视图,用户可以全面探索 RIN 的结构、地理分布和时空变化。为了验证我们系统的可用性和可靠性,我们进行了两项案例研究,并与五位领域专家进行了一组专家访谈。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
V4RIN: visual analysis of regional industry network with domain knowledge.

The regional industry network (RIN) is a type of financial network derived from industry networks that possess the capability to describe the connections between specific industries within a particular region. For most investors and financial analysts lacking extensive experience, the decision-support information provided by industry networks may be too vague. Conversely, RINs express more detailed and specific industry connections both within and outside the region. As RIN analysis is domain-specific and current financial network analysis tools are designed for generalized analytical tasks and cannot be directly applied to RINs, new visual analysis approaches are needed to enhance information exploration efficiency. In this study, we collaborated with domain experts and proposed V4RIN, an interactive visualization analysis system that integrates predefined domain knowledge and data processing methods to support users in uploading custom data. Through multiple views in the system panel, users can comprehensively explore the structure, geographical distribution, and spatiotemporal variations of the RIN. Two case studies were conducted and a set of expert interviews with five domain experts to validate the usability and reliability of our system.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
5.60
自引率
0.00%
发文量
0
期刊最新文献
A study on the influence of situations on personal avatar characteristics. Noise suppression in photon-counting computed tomography using unsupervised Poisson flow generative models. Machine learning approach for the prediction of macrosomia. Medical image registration and its application in retinal images: a review. IQAGPT: computed tomography image quality assessment with vision-language and ChatGPT models.
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1