MobileVisFixer: Tailoring Web Visualizations for Mobile Phones Leveraging an Explainable Reinforcement Learning Framework

Wu, Aoyu; Tong, Wai; Dwyer, Tim; Lee, Bongshin; Isenberg, Petra; Qu, Huamin

doi:10.1109/tvcg.2020.3030423

Cited by 41 publications

(51 citation statements)

References 51 publications

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“…Due to the interdisciplinary nature of our corpus, we find tasks are usually described in inconsistent vocabularies. For instance, the task of extracting encoding choices from visualization images or specifications is described as deconstruction [1], [2] or chart mining [23]. Thus, we wish to establish a common vocabulary that enables consistent discussions for researchers from different areas to communicate the relevance and subtleties.…”

Section: Coding and Classificationmentioning

confidence: 99%

AI4VIS: Survey on Artificial Intelligence Approaches for Data Visualization

Wang

Shu

et al. 2021

Preprint

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Visualizations themselves have become a data format. Akin to other data formats such as text and images, visualizations are increasingly created, stored, shared, and (re-)used with artificial intelligence (AI) techniques. In this survey, we probe the underlying vision of formalizing visualizations as an emerging data format and review the recent advance in applying AI techniques to visualization data. We define visualization data as the digital representations of visualizations in computers and focus on visualizations in information visualization and visual analytic. We build our survey upon a corpus spanning ten different fields in computer science with an eye toward identifying important common interests. Our resulting taxonomy is organized around WHAT is visualization data and its representation, WHY and HOW to apply AI to visualization data. We highlight a set of common tasks that researchers apply to the visualization data and present a detailed discussion of AI approaches developed to accomplish those tasks. Drawing upon our literature review, we discuss several important research questions surrounding the management and exploitation of visualization data, as well as the role of AI in support of those processes. We make the list of surveyed papers and related material available online at ai4vis.github.io.

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Section: Coding and Classificationmentioning

confidence: 99%

AI4VIS: Survey on Artificial Intelligence Approaches for Data Visualization

Wang

Shu

et al. 2021

Preprint

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“…In its simplest form, as envisioned by Hinckley et al (2000), device context such as screen size and orientation can be used to create responsive visualizations that automatically reconfigure themselves to adapt to the active viewport (e.g., (Hoffswell et al, 2020;Wu et al, 2021)). In addition, explicit movements like tilting can also be used to interact with visualizations (e.g., sideways tilting can be used to navigate a timeline view).…”

Section: Context-aware Visualization Interfacesmentioning

confidence: 99%

Post-WIMP Interaction for Information Visualization

Lee¹,

Srinivasan²,

Isenberg³

et al. 2021

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Well-designed interactions for visualization systems hold great promise both for empowering people to effectively complete their tasks and for providing more natural and engaging user experiences. Leveraging advancements in hardware and software technology, the visualization research community has made considerable progress on providing novel input and interaction experiences. These technologies continue to advance at a fast pace, and thus it seems timely to look back at what has been achieved so far and contemplate what might be possible in the future. In this monograph, we first present a condensed summary of research efforts investigating post-WIMP interaction techniques in visualization systems. We also include research from the broader HCI community and several product releases from industry that we believe to be relevant and have influenced visualization interfaces. Furthermore, we reflect on our own projects that investigated post-WIMP InfoVis interaction and systems. We discuss the main challenges we faced and lessons we learned, and we reflect on how our perspectives and viewpoints on post-WIMP for InfoVis have evolved over the

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“…ViSizer [46] presents a perception-based framework for scaling important regions uniformly and deforming homogeneous context. MobileVisFixer [45] adapts a reinforcement learning-based approach that automatically learns and applies decision rules for generating mobile-friendly visualizations. Hoffswell et al [21] analyzed a corpus of responsive news visualizations that informed a prototype tool for designing responsive visualizations for different device contexts.…”

Section: Responsive Visualizationsmentioning

confidence: 99%