Visualization aims to strengthen data exploration and analysis, especially for complex and high-dimensional data. High-performance computing (HPC) systems are typically large and complicated instruments that generate massive performance and operation time series. Monitoring HPC systems’ performance is a daunting task for HPC admins and researchers due to their dynamic natures. This work proposes a visual design using the bipartite graph’s idea to visualize HPC clusters’ structure, metrics, and job scheduling data. We built a web-based prototype, called JobViewer, that integrates advanced methods in visualization and human-computer interaction (HCI) to demonstrate the benefits of visualization in real-time monitoring HPC centers. We also showed real use cases and a user study to validate the efficiency and highlight the current approach’s drawbacks.

Detecting outliers is one of the fundamental tasks in visual analytics and valuable in many application domains, such as suspicious network cyberattack recognition. This paper introduces an approach to analyzing and visualizing high-dimensional time series, focusing on identifying multivariate observations that are significantly different from the others. We also propose a prototype, called MTSAD, to guide users when interactively exploring abnormalities in large time series. The prototype contains two views: the main window provides an overview of identified outliers overtime, the detail window investigates and explores the ranked temporal data entries based on their outlying contributions to the overall plots. The visual interface supports a full range of interactions, such as lensing, brushing and linking, ranking, and filtering. To validate the benefits and usefulness of our approach, we demonstrate MTSAD on real-world datasets of different numbers of attributes.

Analyzing public user posts and shared information on social media can assist us in measuring various population characteristics, patterns, movements, and as well as the public health conditions. In recent years, researchers have been analyzing social media (such as Facebook or Twitter feeds) to detect and predict various emerging events and market trends. Fewer attentions have been paid to the epidemic of the diseases. In this paper, we present a social media analytics tool, called HealthTvizer, for exploring health awareness using Twitter data through interactive and interconnected multiple views. We use topic modeling to pick the relevant and meaningful terms from more than 57 million tweets. We detect the disease name and related contents which are shared by the users of different geographical locations (mostly in the United States). We believe that the collected geolocations from the users' tweets can reveal the patterns of diseases for a given term which allows a researcher to detect, analyze, and explore information about the diseases and hence take necessary steps to improve public health awareness. We validate the effectiveness of HealthTvizer through an informal user study. The feedback from this study also motivates us on interesting future extensions of the tool.

The explosion of social media has paved a way for big data in which entrepreneurs use this data to find out potential customers, market demands, individual behavior, thereby to improve existing products, to create new products according to users' need, or to analyze and evaluate financial risks. The challenges of the heterogeneity and fragmentation of data make it difficult for analysts to fully exploit the benefit of deluge information. Available statistical software lacks customization and address unknown research questions. This paper proposes FinanViz, a visual analytics tool for analyzing financial news on social media. The principal aim of FinanViz is to observe the dynamic behavior of terms/words over time along with their proximity to other terms/words. The tool provides an intuitive, interactive exploration of the financial topics and what events are emerging in which we would argue that it will give hints for financial marketers in the decision making process.