Visual Informatics最新文献

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.

A novel approach to visually represent meteorological data has emerged with the maturation of Immersive Analytics (IA). We have proposed an immersive meteorological virtual sandbox as a solution to the limitations of 2D analysis in expressing and perceiving data. This innovative visual method enables users to interact directly with data through non-contact aerial gestures (NCAG). Referring to the “What you see is what you get” concept in scientific visualization, we proposed a novel approach for the visual exploration of meteorological data that aims to immerse users in the analysis process. We hope this approach can inspire immersive visualization techniques for other types of geographic data as well. Finally, we conducted a user questionnaire to evaluate our system and work. The evaluation results demonstrate that our system effectively reduces cognitive burden, alleviates mental workload, and enhances users’ retention of analysis findings.

Immersive visualization utilizes virtual reality, mixed reality devices, and other interactive devices to create a novel visual environment that integrates multimodal perception and interaction. This technology has been maturing in recent years and has found broad applications in various fields. Based on the latest research advancements in visualization, this paper summarizes the state-of-the-art work in immersive visualization from the perspectives of multimodal perception and interaction in immersive environments, additionally discusses the current hardware foundations of immersive setups. By examining the design patterns and research approaches of previous immersive methods, the paper reveals the design factors for multimodal perception and interaction in current immersive environments. Furthermore, the challenges and development trends of immersive multimodal perception and interaction techniques are discussed, and potential areas of growth in immersive visualization design directions are explored.

How to explore fine-grained but meaningful information from the massive amount of social media data is critical but challenging. To address this challenge, we propose the TopicBubbler, a visual analytics system that supports the cross-level fine-grained exploration of social media data. To achieve the goal of cross-level fine-grained exploration, we propose a new workflow. Under the procedure of the workflow, we construct the fine-grained exploration view through the design of bubble-based word clouds. Each bubble contains two rings that can display information through different levels, and recommends six keywords computed by different algorithms. The view supports users collecting information at different levels and to perform fine-grained selection and exploration across different levels based on keyword recommendations. To enable the users to explore the temporal information and the hierarchical structure, we also construct the Temporal View and Hierarchical View, which satisfy users to view the cross-level dynamic trends and the overview hierarchical structure. In addition, we use the storyline metaphor to enable users to consolidate the fragmented information extracted across levels and topics and ultimately present it as a complete story. Case studies from real-world data confirm the capability of the TopicBubbler from different perspectives, including event mining across levels and topics, and fine-grained mining of specific topics to capture events hidden beneath the surface.

With the support of edge computing, the synergy and collaboration among central cloud, edge cloud, and terminal devices form an integrated computing ecosystem known as the cloud-edge-client architecture. This integration unlocks the value of data and computational power, presenting significant opportunities for large-scale 3D scene modeling and XR presentation. In this paper, we explore the perspectives and highlight new challenges in 3D scene modeling and XR presentation based on point cloud within the cloud-edge-client integrated architecture. We also propose a novel cloud-edge-client integrated technology framework and a demonstration of municipal governance application to address these challenges.

We present a visual analysis environment based on a multi-scale partitioning of a 2d domain into regions bounded by cycles in weighted planar embedded graphs. The work has been inspired by an application in granular materials research, where the question of scale plays a fundamental role in the analysis of material properties. We propose an efficient algorithm to extract the hierarchical cycle structure using persistent homology. The core of the algorithm is a filtration on a dual graph exploiting Alexander’s duality. The resulting partitioning is the basis for the derivation of statistical properties that can be explored in a visual environment. We demonstrate the proposed pipeline on a few synthetic and one real-world dataset.

Node-link visual representation is a widely used tool that allows decision-makers to see details about a network through the appropriate choice of visual metaphor. However, existing visualization methods are not always effective and efficient in representing bivariate graph-based data. This study proposes a novel node-link visual model – visual entropy (Vizent) graph – to effectively represent both primary and secondary values, such as uncertainty, on the edges simultaneously. We performed two user studies to demonstrate the efficiency and effectiveness of our approach in the context of static node-link diagrams. In the first experiment, we evaluated the performance of the Vizent design to determine if it performed equally well or better than existing alternatives in terms of response time and accuracy. Three static visual encodings that use two visual cues were selected from the literature for comparison: Width-Lightness, Saturation-Transparency, and Numerical values. We compared the Vizent design to the selected visual encodings on various graphs ranging in complexity from 5 to 25 edges for three different tasks. The participants achieved higher accuracy of their responses using Vizent and Numerical values; however, both Width-Lightness and Saturation-Transparency did not show equal performance for all tasks. Our results suggest that increasing graph size has no impact on Vizent in terms of response time and accuracy. The performance of the Vizent graph was then compared to the Numerical values visualization. The Wilcoxon signed-rank test revealed that mean response time in seconds was significantly less when the Vizent graphs were presented, while no significant difference in accuracy was found. The results from the experiments are encouraging and we believe justify using the Vizent graph as a good alternative to traditional methods for representing bivariate data in the context of node-link diagrams.

With the intersection and convergence of multiple disciplines and technologies, more and more researchers are actively exploring interdisciplinary cooperation outside their main research fields. Facing a new research field, researchers often hope to quickly learn what is being studied in this field, which research points are receiving high attention, which researchers are studying these research points, and then consider the possibility of collaborating with core researchers on these research points. In addition, students who are preparing for academic further education usually conduct research on mentors and mentors’ research platforms, including academic connections, employment opportunities, etc. In order to satisfy these requirements, we (1) design a research point state map based on a science map to help researchers and students understand the development state of a new research field; (2) design a bar-link author-affiliation information graph to help researchers and students clarify academic networks of scholars and find suitable collaborators or mentors; (3) designs citation pattern histogram to quickly discover research achievements with high research value, such as the Sleeping Beauty papers, recently hot papers, classic papers and so on. Finally, an interactive analytical system named PubExplorer was implemented with IEEE VIS publication data, and its effectiveness is verified through case studies.