With advancements in hardware and communication technologies, the demand for 360-degree video applications has surged. However, the overall spectral efficiency is compromised due to the inability of current multicast schemes to accurately identify 360-degree video users with similar characteristics, resulting in improper user grouping. To address these issues, we propose Hcast360, an adaptive user-correlation-based 360-degree video multicast strategy, in which we designed a user correlation metric based on the throughput difference between multicast and unicast to determine the feasibility of multicast among users. This strategy accurately assigns users to appropriate multicast groups, significantly improving the utilization efficiency of wireless resources. We decompose the 360- degree video multicast problem into three sub-problems: user grouping, resource allocation, and bitrate adaptation. To solve these problems, we propose an adaptive grouping algorithm that assigns users to suitable multicast groups without predefining the number of multicast groups. Finally, we employ a genetic algorithm to determine the bitrate for 360-degree video tiles. Experimental results show that our algorithm can improve users' Quality of Experience (QoE) significantly, achieving noticeable gains without any increase in bandwidth usage.

Node-link diagrams are widely used to visualize graphs. Most graph layout algorithms only use graph topology for aesthetic goals (e.g., minimize node occlusions and edge crossings) or use node attributes for exploration goals (e.g., preserve visible communities). Existing hybrid methods that bind the two perspectives still suffer from various generation restrictions (e.g., limited input types and required manual adjustments and prior knowledge of graphs) and the imbalance between aesthetic and exploration goals. In this article, we propose a flexible embedding-based graph exploration pipeline to enjoy the best of both graph topology and node attributes. First, we leverage embedding algorithms for attributed graphs to encode the two perspectives into latent space. Then, we present an embedding-driven graph layout algorithm, GEGraph, which can achieve aesthetic layouts with better community preservation to support an easy interpretation of the graph structure. Next, graph explorations are extended based on the generated graph layout and insights extracted from the embedding vectors. Illustrated with examples, we build a layout-preserving aggregation method with Focus+Context interaction and a related nodes searching approach with multiple proximity strategies. Finally, we conduct quantitative and qualitative evaluations, a user study, and two case studies to validate our approach.

Utilizing Visualization-oriented Natural Language Interfaces (V-NLI) as a complementary input modality to direct manipulation for visual analytics can provide an engaging user experience. It enables users to focus on their tasks rather than having to worry about how to operate visualization tools on the interface. In the past two decades, leveraging advanced natural language processing technologies, numerous V-NLI systems have been developed in academic research and commercial software, especially in recent years. In this article, we conduct a comprehensive review of the existing V-NLIs. In order to classify each article, we develop categorical dimensions based on a classic information visualization pipeline with the extension of a V-NLI layer. The following seven stages are used: query interpretation, data transformation, visual mapping, view transformation, human interaction, dialogue management, and presentation. Finally, we also shed light on several promising directions for future work in the V-NLI community.

Declarative programmatic approaches are an essential modality for data visualization construction. Despite the powerful customization ability, declarative programming requires users to create charts from scratch, thus building a well-designed visualization is an effort-consuming process. In this paper, we propose leveraging examples to alleviate the problem. The use of examples plays a vital role in visualization design. Users can be allowed to browse through designs for inspiration and adapt them for their own visualizations. In this demo, we directly leverage the entire Vega/Vega-Lite example galleries as chart templates and introduce an authoring pipeline to conveniently instantiate templates with the user's data for extensible programmatic modifications. Finally, we build GALVIS, an example-powered declarative programming tool for visualization construction, enabling efficient declarative programming and retaining the full spectrum of Vega/Vega-Lite characteristics.

General visualization recommendation systems typically make design decisions for the dataset automatically. However, most of them can only prune meaningless visualizations but fail to recommend targeted results. This paper contributes TaskVis, a task-oriented visualization recommendation system that allows users to select their tasks precisely on the interface. We first summarize a task base with 18 classical analytic tasks by a survey both in academia and industry. On this basis, we maintain a rule base, which extends empirical wisdom with our targeted modeling of the analytic tasks. Then, our rule-based approach enumerates all the candidate visualizations through answer set programming. After that, the generated charts can be ranked by four ranking schemes. Furthermore, we introduce a task-based combination recommendation strategy, leveraging a set of visualizations to give a brief view of the dataset collaboratively. Finally, we evaluate TaskVis through a series of use cases and a user study.