The development of interactive visualization applications that are applicable to many real-world problems is a challenging affair. For every new project, developers need to follow the same repetitive steps of fetching the raw data, transforming the data into processable form, defining visual structures and then displaying them appropriately. To accelerate this, we propose the Versatile Visual Analytics Framework for Exploration and Research (VVAFER). VVAFER is planned to be an extensible visual analytics framework, upon which different applications can be developed with minimum overload at the development side. Through modular architecture, unified data formats, reusable templates and software components, developers will be able to quickly deploy and create their visualization applications by configuring existing templates with their own specific functionalities. In this paper, we describe our motivation for this future framework and its architectural design.
In injection molding machines the molds are rarely equipped with sensor systems. The availability of non-invasive ultrasound-based in-mold sensors provides better means for guiding operators of injection molding machines throughout the production process. However, existing visualizations are mostly limited to plots of temperature and pressure over time. In this work, we present the result of a design study created in collaboration with domain experts. The resulting prototypical application uses real-world data taken from live ultrasound sensor measurements for injection molding cavities captured over multiple cycles during the injection process. Our contribution includes a definition of tasks for setting up and monitoring the machines during the process, and the corresponding web-based visual analysis tool addressing these tasks. The interface consists of a multi-view display with various levels of data aggregation that is updated live for newly streamed data of ongoing injection cycles.
Sports data analysis and visualization are useful for gaining insights into the games. In this paper, we present a new visual analytics technique called Tennis Fingerprinting to analyze tennis players’ tactical patterns and styles of play. Tennis is a complicated game, with a variety of styles, tactics, and strategies. Tennis experts and fans are often interested in discussing and analyzing tennis players’ different styles. In tennis, style is a complicated and often abstract concept that cannot be easily described or analyzed. The proposed visualization method is an attempt to provide a concrete and visual representation of a tennis player’s style. We demonstrate the usefulness of our method by analyzing matches played by Roger Federer and Rafael Nadal at Wimbledon, Roland Garros, and Australian Open. Although we focus on tennis data analysis and visualization in this paper, this idea can be extended to the analysis of other competitive sports, including E-sports.
Traffic signals are part of our critical infrastructure and protecting their integrity is a serious concern. Security flaws in traffic signal systems have been documented and effective detection of exploitation of these flaws remains a challenge. In this paper we present a visual analytics approach to look for anomalies in traffic signal data (i.e., abnormal traffic light patterns) that may indicate a compromise of the system. To our knowledge it is a first time a visual analytics approach is applied for the processing and exploration of traffic signal data. This system supports level-of-detail exploration with various visualization techniques. Data cleaning and a number of preprocessing techniques for the extraction of summary information (e.g., traffic signal cycles) of the data are also performed before the visualization and data exploration. Our system successfully reveals the errors in the input data that would be difficult to capture with simple plots alone. In addition, our system captures some abnormal signal patterns that may indicate intrusions into the system. In summary, this work offers a new and effective way to study attacks or intrusions to traffic signal control systems via the visual analysis of traffic light signal patterns.
In this paper, we present CNVis, a web-based visual analytics tool for exploring data from multiple related academic conferences, mainly consisting of the papers presented at the conferences and participants who bookmark these papers. Our goal is to investigate the bookmarking relationships within a single conference and interpret various conference relationships and trends via effective visualization, comparison, and recommendation. This is achieved through the design and development of three coordinated views (the bookmark, topic, and keyword views) for user interaction and exploration. We demonstrate the effectiveness of CNVis using real-world data from three related conferences over a period of five years, followed by an ad-hoc expert evaluation of the tool. Finally, we discuss the extension of this work and the generalizability of CNVis for other applications.