AI4VIS: Survey on Artificial Intelligence Approaches for Data Visualization

Wu, Aoyu; Wang, Yun; Shu, Xinhuan; Moritz, Dominik; Cui, Wei; Zhang, Haidong; Zhang, Dongmei; Qu, Huamin

doi:10.1109/tvcg.2021.3099002

Cited by 90 publications

(50 citation statements)

References 124 publications

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“…Depending on the ultimate purposes, visualizations can be stored in various formats including graphics, programs and hybrid approaches [51].…”

Section: Visualization Formatmentioning

confidence: 99%

“…Raster graphics (i.e., bitmaps) are the most common approaches to store and share visualizations [34] due to their high compatibility. However, they are hardly editable and often lead to the loss of visualization-specific information such as the chart type and visual encodings [51]. Alternatively, vector graphics such as SVGs provide general users with the flexibility of modification and annotation and can preserve partial visualization-related information [50,51] such as the relationship between visual elements.…”

Section: Visualization Formatmentioning

confidence: 99%

“…However, they are hardly editable and often lead to the loss of visualization-specific information such as the chart type and visual encodings [51]. Alternatively, vector graphics such as SVGs provide general users with the flexibility of modification and annotation and can preserve partial visualization-related information [50,51] such as the relationship between visual elements. Previously, some studies have explored utilizing vector graphics to achieve visualization query by specification [17] and visualization type classification [1].…”

Section: Visualization Formatmentioning

confidence: 99%

“…They have been widely used in visualizations on the Internet [1]. Compared with bitmaps, SVGs can preserve more visualization-specific information such as the style of the visual elements [51]. The structural information in SVGs mainly includes the hierarchical and spatial relationship among elements and the properties of each element.…”

Section: Structural Information In Svgsmentioning

confidence: 99%

See 3 more Smart Citations

Structure-aware Visualization Retrieval

Li,

Wang,

et al. 2022

Preprint

Self Cite

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With the wide usage of data visualizations, a huge number of Scalable Vector Graphic (SVG)-based visualizations have been created and shared online. Accordingly, there has been an increasing interest in exploring how to retrieve perceptually similar visualizations from a large corpus, since it can benefit various downstream applications such as visualization recommendation. Existing methods mainly focus on the visual appearance of visualizations by regarding them as bitmap images. However, the structural information intrinsically existing in SVG-based visualizations is ignored. Such structural information can delineate the spatial and hierarchical relationship among visual elements, and characterize visualizations thoroughly from a new perspective. This paper presents a structureaware method to advance the performance of visualization retrieval by collectively considering both the visual and structural information. We extensively evaluated our approach through quantitative comparisons, a user study and case studies. The results demonstrate the effectiveness of our approach and its advantages over existing methods.

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“…Depending on the ultimate purposes, visualizations can be stored in various formats including graphics, programs and hybrid approaches [51].…”

Section: Visualization Formatmentioning

confidence: 99%

Section: Visualization Formatmentioning

confidence: 99%

Section: Visualization Formatmentioning

confidence: 99%

Section: Structural Information In Svgsmentioning

confidence: 99%

See 2 more Smart Citations

Structure-aware Visualization Retrieval

Li,

Wang,

et al. 2022

Preprint

Self Cite

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show abstract

“…Although we provide styles and guidelines, selecting and implementing them in data videos still requires extensive manual work from users. Therefore, future work should design and develop AI approaches [33] and (semi)-automatic authoring tools to recommend appropriate guidelines or provide templates for end-users to realize their designs.…”

Section: Limitations and Future Workmentioning

confidence: 99%

From `Wow' to `Why': Guidelines for Creating the Opening of a Data Video with Cinematic Styles

Xu,

Yang,

Yip

et al. 2022

Preprint

Self Cite

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Data videos are an increasingly popular storytelling form. The opening of a data video critically influences its success as the opening either attracts the audience to continue watching or bores them to abandon watching. However, little is known about how to create an attractive opening. We draw inspiration from the openings of famous films to facilitate designing data video openings. First, by analyzing over 200 films from several sources, we derived six primary cinematic opening styles adaptable to data videos. Then, we consulted eight experts from the film industry to formulate 28 guidelines. To validate the usability and effectiveness of the guidelines, we asked participants to create data video openings with and without the guidelines, which were then evaluated by experts and the general public. Results showed that the openings designed with the guidelines were perceived to be more attractive, and the guidelines were praised for clarity and inspiration.CCS Concepts: • Human-centered computing → Empirical studies in visualization.

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Overview of evolutionary algorithms and neural networks for modern mobile communication

Kouhalvandi

Shayea

Özoğuz

et al. 2022

Trans Emerging Tel Tech

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The sixth generation (6G) of mobile networks must support the huge growth of mobile connections and provides various intelligent services in future mobile networks. For that, designing high-performance network architecture and communication systems (CSs) as well as finding coherent solutions for the determined problems is critical demand that needs to be achieved in 6G networks.Optimal solutions are mainly sought by using modifiable, smart, and perceptive algorithms aimed at optimizing more specific tasks. Therefore, advanced optimization methods are highly required to accommodate the requirements of CSs efficiently. That will be in various parts of the future networks such as advanced mobility management, multi communication links, efficient power consumption, ultra-lower latency, ultra-security, high speed, and reliable connectivity.Accordingly, highly accurate and smart functions must be modeled to optimize the required communication parameters in the network. This study provides a comprehensive overview of optimization methods that may need further investigations and developments to be applied in 6G networks at various parts of networks. A detailed theoretical description for each method is presented and discussed to elucidate future research directions for optimizing specific characteristics with multi-objective optimizations. Moreover, this article illustrates the conceptual and structural viewpoints of reported optimization methods. Also, the capability of various optimization methods that can offer industrial solutions in 6G is discussed. The potential applications of each method are also analyzed. Finally, this article presented the research issues and future directions of optimization technology and research gaps that need to be addressed before the standardization of 6G networks. INTRODUCTIONRecently, innovative solutions of communication systems (CSs) have been developed impressively, as data traffic is increasing exponentially by more than 50% per year. 1 Several factors will contribute for increasing data demand massively, for example, as predicted by CISCO, the video will make up 82% of the all IP traffic by 2022. 2,3 This massive increase in data traffic will cause several new issues that will facing the implementation of future mobile networks. That will include a dramatic increase for wider spectrum need, 4 the necessity for big system capacity, 5 further mobility issues, 6,7 and higher energy consumption, 8 in which cannot be handled efficiently by the current cellular networks.

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AI4VIS: Survey on Artificial Intelligence Approaches for Data Visualization

Cited by 90 publications

References 124 publications

Structure-aware Visualization Retrieval

Structure-aware Visualization Retrieval

From `Wow' to `Why': Guidelines for Creating the Opening of a Data Video with Cinematic Styles

Overview of evolutionary algorithms and neural networks for modern mobile communication

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