Visual Informatics最新文献

This paper presents a survey on automatic or semi-automatic recommendation systems that help users create dashboards. It starts by showing the important role that dashboards play in data science, and give an informal definition of dashboards, i.e., a set of visualizations possibly with linkage, a screen layout and user feedback. We are mainly interested in systems that use a fully or partially automatic mechanism to recommend dashboards to users. This automation includes the suggestion of data and visualizations, the optimization of the layout and the use of user feedback. We position our work with respect to existing surveys. Starting from a set of over 1000 papers, we have selected and analyzed 19 papers/systems along several dimensions. The main dimensions were the set of considered visualizations, the suggestion method, the utility/objective functions, the layout, and the user interface. We conclude by highlighting the main achievements in this domain and by proposing perspectives.

Data have become valuable assets for enterprises. Data governance aims to manage and reuse data assets, facilitating enterprise management and enabling product innovations. A data lineage graph (DLG) is an abstracted collection of data assets and their data lineages in data governance. Analyzing DLGs can provide rich data insights for data governance. However, the progress of data governance technologies is hindered by the shortage of available open datasets for DLGs. This paper introduces an open dataset of DLGs, including the DLG model, the dataset construction process, and applied areas. This real-world dataset is sourced from Huawei Cloud Computing Technology Company Limited, which contains 18 DLGs with three types of data assets and two types of relations. To the best of our knowledge, this dataset is the first open dataset of DLGs for data governance. This dataset can also support the development of other application areas, such as graph analytics and visualization.

As computer graphics technology supports pursuing a photorealistic style, replicated artworks with a photorealistic style overwhelmingly predominate in the computer-generated art circle. Along with the progression of generative technology, this trend may make generative art a virtual world of photorealistic fake, in which the single criterion of expressive style imperils art into the context of a single boring stereotype. This article focuses on the issue of style diversity and its technical feasibility by artistic experiments of generating flower images in StyleGAN. The author insisted that photo both technology and artistic style should not be confined merely for realistic purposes. This proposition was validated in the GAN generation experiment by changing the training materials.

Mixed reality offers a larger visualization space and more intuitive means of interaction for data exploration, and many works have been dedicated to combining 2D visualizations on screen with mixe reality. However, for each combination, we need to customize the implementation of the corresponding mixed reality 3D visualization. It is a challenge to simplify this development process and enable agile building of mixed reality 3D visualizations for 2D visualizations. In addition, many existing 2D visualizations do not provide interfaces oriented to immersive analytics, so how to extend the mixed reality 3D space from existing 2D visualizations is another challenge. This work presents an agile and flexible approach to interactively transfer visualizations from 2D screens to mixed reality 3D spaces. We designed an interactive process for spatial generation of mixed-reality 3D visualizations, defined a unified data transfer framework, integrated data deconstruction techniques for 2D visualizations, implemented interfaces to immersive visualization building tool-kits, and encapsulated these techniques into a tool named X-Space. We validated that the approach is feasible and effective through 2D visualization cases including scatter plots, stacked bar charts, and adjacency matrix. Finally, we conducted expert interviews to discuss the usability and value of the method.

One main challenge for simplifying node-link diagrams of large-scale social networks lies in that simplified graphs generally contain dense subgroups or cohesive subgraphs. Graph triangles quantify the solid and stable relationships that maintain cohesive subgraphs. Understanding the mechanism of triangles within cohesive subgraphs contributes to illuminating patterns of connections within social networks. However, prior works can hardly handle and visualize triangles in cohesive subgraphs. In this paper, we propose a triangle-based graph simplification approach that can filter and visualize cohesive subgraphs by leveraging a triangle-connectivity called $k$-truss and a force-directed algorithm. We design and implement TriGraph, a web-based visual interface that provides detailed information for exploring and analyzing social networks. Quantitative comparisons with existing methods, two case studies on real-world datasets, and feedback from domain experts demonstrate the effectiveness of TriGraph.

Art therapy as an intervention has been shown to alleviate social impairment in people with AD. Meanwhile, digital technology (DTS) has been shown to perform well in different degenerative dementias through mobile devices and apps. However, it is unclear whether digital art creation therapy has an impact on the speech function of people with early AD. Therefore, the aim of this study was to confirm whether digital art creation therapy has an ameliorating effect on language decline in AD patients through the KnowU social teleprompter. This study was a controlled trial in which 16 patients with early AD worked with us and were divided into a paper-based art creation therapy group (control group) and a KnowU social teleprompter therapy group for a 6-week intervention. In the digital art creation intervention group we introduced the KnowU digital kit, consisting of a creation plug-in for the Procreate app on a tablet and a wearable device and its app. The entire treatment process is recorded and combined with a quantitative analysis of the McNemar $\chi$2 test to analyze the differences in outcomes of verbal communication function in early AD patients after different therapies. Ultimately, it is shown that early AD patients utilizing​ the KnowU social teleprompter are more effective in the intervention treatment of language decline in the real social domain compared to the paper-based art creation therapy group. The discussion further demonstrates that DTs and art therapy can provide a better social experience, creative approach and emotional recall of language loss in early AD patients, as well as increase the collaborative relationship between early AD patients and their caregivers.

Music and colour, as human hearing and visual art, are closely related to human psychological feelings and symbolic associations. There is an isomorphic relationship between music and colour. The article uses the concept of “synesthesia” in psychology and the “co-construction” relationship in mathematics as a bridge, based on Kandinsky’s “inner sound” theory and Mallion’s “tone-colour system”, an interdisciplinary theoretical model of “timbre isomorphism synesthesia” (ISCM) is constructed. At the practical level, based on the ISCM theory, a set of timbre synesthesia visualization tools ASAH and visualization processes are designed, through music data input, graphics mapping visualization, colour mapping visualization, real-time interactive visualization, and finally output timbre synesthesia visualization works. In order to avoid the visual homogenization caused by algorithm design, ASHA has set up a custom editor, which emphasizes the individual differences and multi-sensory experience of tonal synesthesia visualization.

This paper introduces an approach to analyzing multivariate time series (MVTS) data through progressive temporal abstraction of the data into patterns characterizing the behavior of the studied dynamic phenomenon. The paper focuses on two core challenges: identifying basic behavior patterns of individual attributes and examining the temporal relations between these patterns across the range of attributes to derive higher-level abstractions of multi-attribute behavior. The proposed approach combines existing methods for univariate pattern extraction, computation of temporal relations according to the Allen’s time interval algebra, visual displays of the temporal relations, and interactive query operations into a cohesive visual analytics workflow. The paper describes the application of the approach to real-world examples of population mobility data during the COVID-19 pandemic and characteristics of episodes in a football match, illustrating its versatility and effectiveness in understanding composite patterns of interrelated attribute behaviors in MVTS data.

Drug molecule design is a classic research topic. Drug experts traditionally design molecules relying on their experience. Manual drug design is time-consuming and may produce low-efficacy and off-target molecules. With the popularity of deep learning, drug experts are beginning to use generative models to design drug molecules. A well-trained generative model can learn the distribution of training samples and infinitely generate drug-like molecules similar to the training samples. The automatic process improves design efficiency. However, most existing methods focus on proposing and optimizing generative models. How to discover ideal molecules from massive candidates is still an unresolved challenge. We propose a visualization system to discover ideal drug molecules generated by generative models. In this paper, we investigated the requirements and issues of drug design experts when using generative models, i.e., generating molecular structures with specific constraints and finding other molecular structures similar to potential drug molecular structures. We formalized the first problem as an optimization problem and proposed using a genetic algorithm to solve it. For the second problem, we proposed using a neighborhood sampling algorithm based on the continuity of the latent space to find solutions. We integrated the proposed algorithms into a visualization tool, and a case study for discovering potential drug molecules to make KOR agonists and experiments demonstrated the utility of our approach.