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Augmenting Visualizations with Statistical and User-Defined Data Facts
Augmenting Visualizations with Statistical and User-Defined Data Facts
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202105551
- ISBN
- 9798265405630
- DDC
- 741
- 저자명
- Guo, Grace.
- 서명/저자
- Augmenting Visualizations with Statistical and User-Defined Data Facts
- 발행사항
- [Sl] : Georgia Institute of Technology, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 145 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-05, Section: A.
- 주기사항
- Advisor: Endert, Alex.
- 학위논문주기
- Thesis (Ph.D.)--Georgia Institute of Technology, 2024.
- 초록/해제
- 요약When designing visualizations and visualization systems, we often augment charts and graphs with visual elements in order to convey richer and more nuanced information about relationships in the data. However, we do not fully understand user considerations when creating these augmentations, nor do we have toolkits to support augmentation authoring. This thesis first outlines a design space of user-created augmentations, then introduces Auteur, a front-end JavaScript toolkit designed to help developers add augmentations to web-based D3 visualizations and systems to convey statistical and custom data relationships. The library is then customized and extended for the domains of online learning and causal inference, where users may be interested in domain-specific data relationships or work with unique chart types and data sets. Collectively, these contributions aim to help us better incorporate user-defined augmentations into visualizations for analysis and storytelling, thus conveying human context, user preferences, and domain knowledge through our charts and graphs.
- 일반주제명
- Design
- 일반주제명
- Graphs
- 일반주제명
- Visualization
- 기본자료저록
- Dissertations Abstracts International. 87-05A.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798265405630
■035 ▼a(MiAaPQ)AAI32315697
■035 ▼a(MiAaPQ)GeorgiaTech75712
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a741
■1001 ▼aGuo, Grace.
■24510▼aAugmenting Visualizations with Statistical and User-Defined Data Facts
■260 ▼a[Sl]▼bGeorgia Institute of Technology▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a145 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-05, Section: A.
■500 ▼aAdvisor: Endert, Alex.
■5021 ▼aThesis (Ph.D.)--Georgia Institute of Technology, 2024.
■520 ▼aWhen designing visualizations and visualization systems, we often augment charts and graphs with visual elements in order to convey richer and more nuanced information about relationships in the data. However, we do not fully understand user considerations when creating these augmentations, nor do we have toolkits to support augmentation authoring. This thesis first outlines a design space of user-created augmentations, then introduces Auteur, a front-end JavaScript toolkit designed to help developers add augmentations to web-based D3 visualizations and systems to convey statistical and custom data relationships. The library is then customized and extended for the domains of online learning and causal inference, where users may be interested in domain-specific data relationships or work with unique chart types and data sets. Collectively, these contributions aim to help us better incorporate user-defined augmentations into visualizations for analysis and storytelling, thus conveying human context, user preferences, and domain knowledge through our charts and graphs.
■590 ▼aSchool code: 0078.
■650 4▼aDesign
■650 4▼aGraphs
■650 4▼aVisualization
■690 ▼a0389
■71020▼aGeorgia Institute of Technology.
■7730 ▼tDissertations Abstracts International▼g87-05A.
■790 ▼a0078
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17360586▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
