Browsing by Author "Li, Jie"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Exploring Multi-dimensional Data via Subset Embedding
    (The Eurographics Association and John Wiley & Sons Ltd., 2021) Xie, Peng; Tao, Wenyuan; Li, Jie; Huang, Wentao; Chen, Siming; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
    Multi-dimensional data exploration is a classic research topic in visualization. Most existing approaches are designed for identifying record patterns in dimensional space or subspace. In this paper, we propose a visual analytics approach to exploring subset patterns. The core of the approach is a subset embedding network (SEN) that represents a group of subsets as uniformlyformatted embeddings. We implement the SEN as multiple subnets with separate loss functions. The design enables to handle arbitrary subsets and capture the similarity of subsets on single features, thus achieving accurate pattern exploration, which in most cases is searching for subsets having similar values on few features. Moreover, each subnet is a fully-connected neural network with one hidden layer. The simple structure brings high training efficiency. We integrate the SEN into a visualization system that achieves a 3-step workflow. Specifically, analysts (1) partition the given dataset into subsets, (2) select portions in a projected latent space created using the SEN, and (3) determine the existence of patterns within selected subsets. Generally, the system combines visualizations, interactions, automatic methods, and quantitative measures to balance the exploration flexibility and operation efficiency, and improve the interpretability and faithfulness of the identified patterns. Case studies and quantitative experiments on multiple open datasets demonstrate the general applicability and effectiveness of our approach.
  • Thumbnail Image
    Item
    The Gap between Visualization Research and Visualization Software in High-Performance Computing Center
    (The Eurographics Association, 2021) Dang, Tommy; Nguyen, Ngan; Hass, Jon; Li, Jie; Chen, Yong; Sill, Alan; Gillmann, Christina and Krone, Michael and Reina, Guido and Wischgoll, Thomas
    Visualizing and monitoring high-performance computing centers is a daunting task due to the systems' complex and dynamic nature. Moreover, different users may have different requirements and needs. For example, computer scientists carry out data analysis as batch jobs using various models, configurations, and parameters, and they often need to manage jobs. System administrators need to monitor and manage the system constantly. In this paper, we discuss the gap between visual monitoring research and practical applicability. We will start with the general requirements for managing high-performance computing centers and then share the experiences working with academic and industrial experts in this domain.