國立虎尾科技大學 |

Creating Overview Visualizations for Data Understanding.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Creating Overview Visualizations for Data Understanding./
作者:	Liu, Li.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
面頁冊數:	158 p.
附註:	Source: Dissertations Abstracts International, Volume: 80-10, Section: B.
Contained By:	Dissertations Abstracts International80-10B.
標題:	Computer Engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10982776
ISBN:	9781392075869

Creating Overview Visualizations for Data Understanding.
Liu, Li.

Creating Overview Visualizations for Data Understanding. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 158 p.

Source: Dissertations Abstracts International, Volume: 80-10, Section: B.

Thesis (Ph.D.)--Rutgers The State University of New Jersey, School of Graduate Studies, 2019.

This item must not be sold to any third party vendors.

One of the major challenges in data visualization is the presentation problem: having too much information to display at a time in one screen. The traditional presentation techniques (e.g., panning, scrolling, and flipping) that are widely used in standard user interfaces always introduce a discontinuity between the information displayed at different times and places. Viewers find a compact visual summarization of the information space (i.e., an overview) is helpful in data understanding. When utilized properly, an overview can provide users with an immediate appreciation and an overall sense of data. Although creating an overview is often a design goal and an overview is widely noted in data visualization as a qualitative awareness of one aspect of the data (e.g., gaining an overview of the information space) or a technical and user interface component (e.g., overview + detail visualization), the properties and categories of overviews, the relations between overviews and viewers’ awareness, and the process to create overviews are barely discussed in the literature. In data visualization, giving an overview of a dataset is part of a broad topic of providing a combination of contextual and detailed views. Although discussions on contextual and detailed visualizations are mostly made in the scope of information visualization, approaches are also used in scientific visualization. These visualizations are studied and classified by interface mechanisms (e.g., overview+detail, focus+context, contextual cue) used to separate and blend views without considering the characteristics of information space. More importantly, not all of the contextual and detailed visualizations give an overview of data. In this thesis, I focus on “overview” visualizations as a means to covey the context of a large dataset. An overview visualization is a visual representation that provides viewers with an overall awareness of the content, structure, or changes of the data (the dataset can contain time-varying information) while allowing the viewer to further drill down into the details. The applicability of overview visualizations is extended to both scientific visualization and information visualization domains. Overview visualizations are characterized into five important aspects: (1) the nature of overviews; (2) the roles of overviews; (3) design strategies for the overview display; (4) approaches for shrinking the information space of data; (5) techniques for the interactions and the detail representations. Based on the characterization of overview visualizations and inspired by other visualization design models, a pipeline model is created to analyze existing systems or papers and to guide the development process of an overview visualization. Two case studies are presented with evaluations to illustrate the general usage of the proposed characterization and pipeline model. The first involves a time-series 3D dataset of ocean simulations (scientific visualization) and the second involves a university career job portal (information visualization). The results demonstrate how overview visualizations can facilitate data understanding and analysis.

ISBN: 9781392075869Subjects--Topical Terms:

796158
Computer Engineering.
Subjects--Index Terms:

Data understanding

Creating Overview Visualizations for Data Understanding.
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520 $a One of the major challenges in data visualization is the presentation problem: having too much information to display at a time in one screen. The traditional presentation techniques (e.g., panning, scrolling, and flipping) that are widely used in standard user interfaces always introduce a discontinuity between the information displayed at different times and places. Viewers find a compact visual summarization of the information space (i.e., an overview) is helpful in data understanding. When utilized properly, an overview can provide users with an immediate appreciation and an overall sense of data. Although creating an overview is often a design goal and an overview is widely noted in data visualization as a qualitative awareness of one aspect of the data (e.g., gaining an overview of the information space) or a technical and user interface component (e.g., overview + detail visualization), the properties and categories of overviews, the relations between overviews and viewers’ awareness, and the process to create overviews are barely discussed in the literature. In data visualization, giving an overview of a dataset is part of a broad topic of providing a combination of contextual and detailed views. Although discussions on contextual and detailed visualizations are mostly made in the scope of information visualization, approaches are also used in scientific visualization. These visualizations are studied and classified by interface mechanisms (e.g., overview+detail, focus+context, contextual cue) used to separate and blend views without considering the characteristics of information space. More importantly, not all of the contextual and detailed visualizations give an overview of data. In this thesis, I focus on “overview” visualizations as a means to covey the context of a large dataset. An overview visualization is a visual representation that provides viewers with an overall awareness of the content, structure, or changes of the data (the dataset can contain time-varying information) while allowing the viewer to further drill down into the details. The applicability of overview visualizations is extended to both scientific visualization and information visualization domains. Overview visualizations are characterized into five important aspects: (1) the nature of overviews; (2) the roles of overviews; (3) design strategies for the overview display; (4) approaches for shrinking the information space of data; (5) techniques for the interactions and the detail representations. Based on the characterization of overview visualizations and inspired by other visualization design models, a pipeline model is created to analyze existing systems or papers and to guide the development process of an overview visualization. Two case studies are presented with evaluations to illustrate the general usage of the proposed characterization and pipeline model. The first involves a time-series 3D dataset of ocean simulations (scientific visualization) and the second involves a university career job portal (information visualization). The results demonstrate how overview visualizations can facilitate data understanding and analysis.
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