Fangzhou Guo

A Survey of Traffic Data Visualization

Data-driven intelligent transportation systems utilize data resources generated within intelligent systems to improve the performance of transportation systems and provide convenient and reliable services. Traffic data refer to datasets generated and collected on moving vehicles and objects. Data visualization is an efficient means to represent distributions and structures of datasets and reveal hidden patterns in the data. This paper introduces the basic concept and pipeline of traffic data visualization, provides an overview of related data processing techniques, and summarizes existing methods for depicting the temporal, spatial, numerical, and categorical properties of traffic data.

An Online Visualization System for Streaming Log Data of Computing Clusters

Monitoring a computing cluster requires collecting and understanding log data generated at the core, computer, and cluster levels at run time. Visualizing the log data of a computing cluster is a challenging problem due to the complexity of the underlying dataset: it is streaming, hierarchical, heterogeneous, and multi-sourced. This paper presents an integrated visualization system that employs a two-stage streaming process mode. Prior to the visual display of the multi-sourced information, the data generated from the clusters is gathered, cleaned, and modeled within a data processor. The visualization supported by a visual computing processor consists of a set of multivariate and time variant visualization techniques, including time sequence chart, treemap, and parallel coordinates. Novel techniques to illustrate the time tendency and abnormal status are also introduced. We demonstrate the effectiveness and scalability of the proposed system framework on a commodity cloud-computing platform.

A survey of network anomaly visualization

Network anomaly analysis is an emerging subtopic of network security. Network anomaly refers to the unusual behavior of network devices or suspicious network status. A number of intelligent visual tools are developed to enhance the ability of network security analysts in understanding the original data, ultimately solving network security problems. This paper surveys current progress and trends in network anomaly visualization. By providing an overview of network anomaly data, visualization tasks, and applications, we further elaborate on existing methods to depict various data features of network alerts, anomalous traffic, and attack patterns data. Directions for future studies are outlined at the end of this paper.

Visual exploration of latent ranking evolutions in time series

Rankings are everywhere in the world and they change constantly. Detecting and analyzing ranking changes in a ranked list is of great importance for recommendation and information retrieval tasks. Common to existing approaches is that the latent correlations and trends of ranked lists are not taken into account. This paper introduces RankEvo, an integration of rank structuring and visualization techniques, for detecting and analyzing latent evolutions in ranking time series. We characterize the ranking changes by computing the similarities among the time series of ranked items and organizing similar items into itemsets, and further forming ranking evolutions. The integrated RankEvo system provides visualization and intuitive interactions for exploring correlated itemsets, concurrent ranking evolutions, as well as outlier items of ranked lists. The system also employs additional information windows on demand for evolution elaboration and verification. Case studies are conducted to demonstrate the effectiveness and usability of the RankEvo system in assisting users to understand ranking changes.Graphical abstract

VisForum: A Visual Analysis System for Exploring User Groups in Online Forums

User grouping in asynchronous online forums is a common phenomenon nowadays. People with similar backgrounds or shared interests like to get together in group discussions. As tens of thousands of archived conversational posts accumulate, challenges emerge for forum administrators and analysts to effectively explore user groups in large-volume threads and gain meaningful insights into the hierarchical discussions. Identifying and comparing groups in discussion threads are nontrivial, since the number of users and posts increases with time and noises may hamper the detection of user groups. Researchers in data mining fields have proposed a large body of algorithms to explore user grouping. However, the mining result is not intuitive to understand and difficult for users to explore the details. To address these issues, we present VisForum, a visual analytic system allowing people to interactively explore user groups in a forum. We work closely with two educators who have released courses in Massive Open Online Courses (MOOC) platforms to compile a list of design goals to guide our design. Then, we design and implement a multi-coordinated interface as well as several novel glyphs, i.e., group glyph, user glyph, and set glyph, with different granularities. Accordingly, we propose the group Detecting 8 Sorting Algorithm to reduce noises in a collection of posts, and employ the concept of “forum-index” for users to identify high-impact forum members. Two case studies using real-world datasets demonstrate the usefulness of the system and the effectiveness of novel glyph designs. Furthermore, we conduct an in-lab user study to present the usability of VisForum.

TieVis: Visual Analytics of Evolution of Interpersonal Ties

Interpersonal ties, such as strong ties and weak ties, describe the information carried by an edge in social network. Tracking the dynamic changes of interpersonal ties can thus enhance our understanding of the evolution of a complex network. Nevertheless, existing studies in dynamic network visualization mostly focus on the temporal changes of nodes or structures of the network without an adequate support of analysis and exploration of the temporal changes of interpersonal ties. In this paper, we introduce a new visual analytics method that enables interactive analysis and exploration of the dynamic changes of interpersonal ties. The method integrates four well-linked visualizations, including a scatterplot, a pixelbar chart, a layered graph, and a node–link diagram, to allow for multi-perspective analysis of the evolution of interpersonal ties. The scatterplot created by multi-dimensional scaling can help reveal the clusters of ties and detect abnormal ties, while other visualizations allow users to explore the clusters of ties interactively from different perspectives. Two case studies have been conducted to demonstrate the effectiveness of our approach.Graphical abstract

egoComp: A node-link-based technique for visual comparison of ego-networks

Analysis of ego-networks is a critical research problem when analyzing large-scale social networks, as an ego-network represents the social circle a person actually contacts with. One of the core tasks in ego-network analysis is visual comparison, which includes edge comparison and node comparison. Although various works have been done to support comparing normal networks and ego-networks, intuitive node comparison of two ego-networks is still challenging. In this article, we propose egoComp, an intuitive and expressive visualization technique, to analyze the node difference between two ego-networks. To preserve the latent structure of ego-network and lay emphasis on intuitiveness, our design is node-link-based (radial tree layout) and uses a side-by-side method to compare ego-networks. We design a novel storyflow-like graph layout to reveal the relationship of two ego-networks at the individual node level. Furthermore, three different layout algorithms, including origin, greedy, and assignment algorithms, are proposed to meet different user requirements. We demonstrate the effectiveness of our system through case studies and a user study and then discuss the limitations thoroughly as well as the possible solutions and potential future work.

Visually Exploring Differences of DTI Fiber Models

Fiber tracking of Diffusion Tensor Imaging (DTI) datasets is a non-invasive tool to study the underlying fibrous structures in living tissues. However, DTI fibers may vary from subject to subject due to variations in anatomy, motions in scanning, and signal noise. In addition, fiber tracking parameters have a great influence on tracking results. Subtle changes of parameters can produce significantly different DTI fibers. Interactive exploration and analysis of differences among DTI fiber models are critical for the purposes of group comparison, atlas construction, and uncertainty analysis. Conventional approaches illustrate differences in the 3D space with either voxel-wise or fiber-based comparisons. Unfortunately, these approaches require an accurate alignment process and might give rise to visual clutter. This paper introduces a two-phase projection technique to reformulate a complex 3D fiber model as a unique 2D map for feature characterization and comparative analysis. To facilitate investigation, regions of significant differences among the 2D maps are further identified. Using these maps, differences that are difficult to be distinguished in the 3D space due to depth occlusion can be easily discovered. We design a visual exploration interface to study differences from multiple perspectives. We evaluate the effectiveness of our approach by examining two datasets.