THESIS
2022
1 online resource (xx, 214 pages) : illustrations (chiefly color)
Abstract
The influential rise of the Internet and social media has accompanied ever-increasing public
needs for accessing information and data. In light of this trend, data visualizations have
emerged as one of the primary mediums for public data communication. Thus, a large
number of visualizations have been produced and shared on the web, raising new challenges
and problems in both society and academia. In this thesis, I investigate this phenomenon
and accompanying problems through a careful combination of research methods
including literature survey, empirical studies, and machine learning. Specifically, this
thesis focuses on:
(1) Building novel recommenders for authoring high-quality visualizations. Many
web visualizations are made by non-experts and suffer from quality problems such as
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The influential rise of the Internet and social media has accompanied ever-increasing public
needs for accessing information and data. In light of this trend, data visualizations have
emerged as one of the primary mediums for public data communication. Thus, a large
number of visualizations have been produced and shared on the web, raising new challenges
and problems in both society and academia. In this thesis, I investigate this phenomenon
and accompanying problems through a careful combination of research methods
including literature survey, empirical studies, and machine learning. Specifically, this
thesis focuses on:
(1) Building novel recommenders for authoring high-quality visualizations. Many
web visualizations are made by non-experts and suffer from quality problems such as
poor readability and insight. I present three automated approaches to assist the general
public in creating visualizations, including MobileVisFixer for generating mobile-friendly
designs, LQ2 for authoring aesthetic layouts, and MultiVision for designing analytical
dashboards.
(2) Formalizing visualizations as a new first-class object for efficient analysis. Through a comprehensive literature survey into ten research fields in computer science, I argue that
visualizations are becoming a new data object like images and text. I further formulate the
emerging research field as visualization processing and analysis that concerns processing
digitized visualizations and extracting meaningful information. My work Computable-Viz presents a formalism for operating on multiple visualizations, thereby creating novel
applications such as interactively combining visualizations in AR environments.
By integrating those two perspectives, this dissertation contributes to a new online
knowledge ecosystem – both analyzing web visualizations to distill knowledge and assisting
the public in producing new visualizations to communicate data. I hope that this
ecosystem will continue stimulating new theories, problems, techniques, and applications
to further bridge the public with data.
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