Dashboard visualizations are widely used in data-intensive applications such as business intelligence, operation monitoring, and urban planning. However, existing visualization authoring tools are inefficient in the rapid prototyping of dashboards because visualization expertise and user intention need to be integrated. We propose a novel approach to rapid conceptualization that can construct dashboard templates from exemplars to mitigate the burden of designing, implementing, and evaluating dashboard visualizations. The kernel of our approach is a novel deep learning-based model that can identify and locate charts of various categories and extract colors from an input image or sketch. We design and implement a web-based authoring tool for learning, composing, and customizing dashboard visualizations in a cloud computing environment. Examples, user studies, and user feedback from real scenarios in Alibaba Cloud verify the usability and efficiency of the proposed approach.

In recent years, the technology and applications of visualizations on large high-resolution displays (LHDs) have received widespread attention because of its perceptual benefits and improved productivity. However, existing work on LHD visualization lacks both comprehensive guidance for design requirements and tools developed for its specific usage scenarios. In this paper, we present the scenarios, design, and implementation of DataV, a Software-as-a-Service (SaaS) visual deployment tool that enables rapid construction and cross-platform publishing of interactive visualization on LHDs. Our framework can support rich components for the high-performance rendering of multi-source heterogeneous data. DataV provides a full-fledged toolchain to help the user efficiently specify layout and interactions. We present its accessibility and impressive visual effects with examples and comparison with Tableau, Power BI, VisComposer, and iVisDesigner. We also report the performance of using DataV for 3D map rendering by comparing it with deck.gl.

Analyzing social networks reveals the relationships between individuals and groups in the data. However, such analysis can also lead to privacy exposure (whether intentionally or inadvertently): leaking the real-world identity of ostensibly anonymous individuals. Most sanitization strategies modify the graph's structure based on hypothesized tactics that an adversary would employ. While combining multiple anonymization schemes provides a more comprehensive privacy protection, deciding the appropriate set of techniques-along with evaluating how applying the strategies will affect the utility of the anonymized results-remains a significant challenge. To address this problem, we introduce GraphProtector, a visual interface that guides a user through a privacy preservation pipeline. GraphProtector enables multiple privacy protection schemes which can be simultaneously combined together as a hybrid approach. To demonstrate the effectiveness of GraphProtector, we report several case studies and feedback collected from interviews with expert users in various scenarios.

Urban data is massive, heterogeneous, and spatio-temporal, posing a substantial challenge for visualization and analysis. In this paper, we design and implement a novel visual analytics approach, Visual Analyzer for Urban Data (VAUD), that supports the visualization, querying, and exploration of urban data. Our approach allows for cross-domain correlation from multiple data sources by leveraging spatial-temporal and social inter-connectedness features. Through our approach, the analyst is able to select, filter, aggregate across multiple data sources and extract information that would be hidden to a single data subset. To illustrate the effectiveness of our approach, we provide case studies on a real urban dataset that contains the cyber-, physical-, and social- information of 14 million citizens over 22 days.

Sharing data for public usage requires sanitization to prevent sensitive information from leaking. Previous studies have presented methods for creating privacy preserving visualizations. However, few of them provide sufficient feedback to users on how much utility is reduced (or preserved) during such a process. To address this, we design a visual interface along with a data manipulation pipeline that allows users to gauge utility loss while interactively and iteratively handling privacy issues in their data. Widely known and discussed types of privacy models, i.e., syntactic anonymity and differential privacy, are integrated and compared under different use case scenarios. Case study results on a variety of examples demonstrate the effectiveness of our approach.

As the amount of data being collected has increased, the need for tools that can enable the visual exploration of data has also grown. This has led to the development of a variety of widely used programming frameworks for information visualization. Unfortunately, such frameworks demand comprehensive visualization and coding skills and require users to develop visualization from scratch. An alternative is to create interactive visualization design environments that require little to no programming. However, these tools only supports a small portion of visual forms. We present a programmable integrated development environment (IDE), VisComposer, that supports the development of expressive visualization using a drag-and-drop visual interface. VisComposer exposes the programmability by customizing desired components within a modularized visualization composition pipeline, effectively balancing the capability gap between expert coders and visualization artists. The implemented system empowers users to compose comprehensive visualizations with real-time preview and optimization features, and supports prototyping, sharing and reuse of the effects by means of an intuitive visual composer. Visual programming and textual programming integrated in our system allow users to compose more complex visual effects while retaining the simplicity of use. We demonstrate the performance of VisComposer with a variety of examples and an informal user evaluation.

Although basketball games have received broad attention, the forms of game reports and webcast are purely content-based cross-media: texts, videos, snapshots, and performance figures. Analytical narrations of games that seek to compose a complete game from heterogeneous datasets are challenging for general media producers because such a composition is time-consuming and heavily depends on domain experts. In particular, an appropriate analytical commentary of basketball games requires two factors, namely, rich context and domain knowledge, which includes game events, player locations, player profiles, and team profiles, among others. This type of analytical commentary elicits a timely and effective basketball game data visualization made up of different sources of media. Existing visualizations of basketball games mainly profile a particular aspect of the game. Therefore, this paper presents an expressive visualization scheme that comprehensively illustrates NBA games with three levels of details: a season level, a game level, and a session level. We reorganize a basketball game as a sequence of sessions to depict the game states and heated confrontations. We design and implement a live system that integrates multimedia NBA datasets: play-by-play text data, box score data, game video data, and action area data. We demonstrate the effectiveness of this scheme with case studies and user feedbacks.