IEEE Computer Graphics and Applications Paper Sessions at IEEE VIS 2016

Visualization Systems & Applications

Wednesday, October 26     ||     Time: 10:30–13:10     ||     Room: Holiday 6     ||     Session Chair: Theresa-Marie Rhyne

Key-Node-Separated Graph Clustering and Layouts for Human Relationship Graph Visualization

Takayuchi Itoh, Karsten Klein

Many graph-drawing methods apply node-clustering techniques based on the density of edges to find tightly connected subgraphs and then hierarchically visualize the clustered graphs. However, users may want to focus on important nodes and their connections to groups of other nodes for some applications. For this purpose, it is effective to separately visualize the key nodes detected based on adjacency and attributes of the nodes. This article presents a graph visualization technique for attribute-embedded graphs that applies a graph-clustering algorithm that accounts for the combination of connections and attributes. The graph clustering step divides the nodes according to the commonality of connected nodes and similarity of feature value vectors. It then calculates the distances between arbitrary pairs of clusters according to the number of connecting edges and the similarity of feature value vectors and finally places the clusters based on the distances. Consequently, the technique separates important nodes that have connections to multiple large clusters and improves the visibility of such nodes' connections. To test this technique, this article presents examples with human relationship graph datasets, including a coauthorship and Twitter communication network dataset.


ENTVis: A Visual Analytic Tool for Entropy-Based Network Traffic Anomaly Detection

Fangfang Zhou, Wei Huang, Ying Zhao, Yang Shi, Xing Liang, Xiaoping Fan

Entropy-based traffic metrics have received substantial attention in network traffic anomaly detection because entropy can provide fine-grained metrics of traffic distribution characteristics. However, some practical issues--such as ambiguity, lack of detailed distribution information, and a large number of false positives--affect the application of entropy-based traffic anomaly detection. In this work, we introduce a visual analytic tool called ENTVis to help users understand entropy-based traffic metrics and achieve accurate traffic anomaly detection. ENTVis provides three coordinated views and rich interactions to support a coherent visual analysis on multiple perspectives: the timeline group view for perceiving situations and finding hints of anomalies, the Radviz view for clustering similar anomalies in a period, and the matrix view for understanding traffic distributions and diagnosing anomalies in detail. Several case studies have been performed to verify the usability and effectiveness of our method. A further evaluation was conducted via expert review.


Visualizing Rank Time Series of Wikipedia Top Viewed Pages

Jing Xia, Yumeng Hou, Victor Chen, Cheryl Qian, David Ebert, Wei Chen

Visual clutter is a common challenge in visualizing large rank time series data. Following the Gestalt’s law of continuity [11], we try out a variety of visual design approaches on large rank time series datasets. We useWikipedia top page view statistics to test and eval- uate these approaches. The data is a set of top viewed pages over time, which is of great importance in analyzing viewers’ interest in current affairs. Based on the visual designs we implement Wiki- TopReader, a reader of Wikipedia page rank, with which users are able to explore connections among those top viewed pages by con- necting the page rank behaviors with the page link relations. Such a combination enhances the unweightedWikipedia page link network and brings users’ page of interest to a broader attention. The eval- uation shows that the system is effective on representing evolving ranking patterns and page-wise correlation. The design is intuitive and visually appealing.


WarpIV: In Situ Visualization and Analysis of Ion Accelerator Simulations

Oliver Rübel, Burlen Loring, Jean-Luc Vay, David P. Grote, Remi Lehe, Stepan Bulanov, Henri Vincenti, E. Wes Bethel

The generation of short pulses of ion beams through the interaction of an intense laser with a plasma sheath offers the possibility of compact and cheaper ion sources for many applications--from fast ignition and radiography of dense targets to hadron therapy and injection into conventional accelerators. To enable the efficient analysis of large-scale, high-fidelity particle accelerator simulations using the Warp simulation suite, the authors introduce the Warp In situ Visualization Toolkit (WarpIV). WarpIV integrates state-of-the-art in situ visualization and analysis using VisIt with Warp, supports management and control of complex in situ visualization and analysis workflows, and implements integrated analytics to facilitate query- and feature-based data analytics and efficient large-scale data analysis. WarpIV enables for the first time distributed parallel, in situ visualization of the full simulation data using high-performance compute resources as the data is being generated by Warp. The authors describe the application of WarpIV to study and compare large 2D and 3D ion accelerator simulations, demonstrating significant differences in the acceleration process in 2D and 3D simulations. WarpIV is available to the public via The Warp In situ Visualization Toolkit (WarpIV) supports large-scale, parallel, in situ visualization and analysis and facilitates query- and feature-based analytics, enabling for the first time high-performance analysis of large-scale, high-fidelity particle accelerator simulations while the data is being generated by the Warp simulation suite. This supplemental material provides more details regarding the memory profiling and optimization and the Yee grid recentering optimization results discussed in the main article.


A Decision Support System for Planning Sustainable Water Distribution Systems

Alina Freund, Nazli Yonca Aydin, Dirk Zeckzer, Hans Hagen

In this paper, an interactive decision support system is proposed to assist experts who prepare water resource management plans for decision makers and stakeholders. Several visualization techniques such as circle views, grid layout, small multiple maps, and simplification of nodes are combined to improve the data readability of water distribution systems. Views showing the original data and insets for dual network scenarios for further water demand scenarios complement the proposed visualization technique. Further, textual descriptions of the sustainability indices and water usage provide more detailed information. This is complemented by interaction techniques that ease the comparison of different water demand scenarios. Selecting circle views, zooming into an area of interest in the original data view in combination with linked views and a graph for the measured hydraulic simulation values enable users to analyze the scenarios and to understand the causes of individual problems. A case study with three urban water management and sanitary engineering experts was performed to assess this approach. Overall, the results show that the proposed decision support system is satisfactory, efficient, and effective.


Applied Visualization Techniques

Thursday, October 27     ||     Time: 10:30–13:10     ||     Room: Holiday 6     ||     Session Chair: Melanie Tory

Episogram: Visual Summarization of Egocentric Social Interactions

Nan Cao, Yuru Lin, Fan Du

The key challenges of visualizing social interaction data include the difficulties of understanding the general structure of social interactions and representing the data in the context of various user activities to reveal different behavior patterns. The design of the proposed interactive visualization tool Episogram is based on an anatomy of social interaction process in which the actors and objects involved can be formally represented as a time-varying tripartite network. The authors show the effectiveness of the proposed technique using real-world datasets and user studies.


VTK-m: Accelerating the Visualization Toolkit for Massively Threaded Architectures

Kenneth Moreland, Christopher Sewell, William Usher, Li-ta Lo, Jeremy Meredith, David Pugmire, James Kress, Hendrik Schroots, Kwan-Liu Ma, Hank Childs, Matthew Larsen, Chun-Ming Chen, Robert Maynard, Berk Geveci

One of the most critical challenges for high-performance computing (HPC) scientific visualization is execution on massively threaded processors. Of the many fundamental changes we are seeing in HPC systems, one of the most profound is a reliance on new processor types optimized for execution bandwidth over latency hiding. Our current production scientific visualization software is not designed for these new types of architectures. To address this issue, the VTK-m framework serves as a container for algorithms, provides flexible data representation, and simplifies the design of visualization algorithms on new and future computer architecture.


Evaluating Shape Alignment via Ensemble Visualization

Mukund Raj, Mahsa Mirzargar, J. Samuel Preston, Robert M. Kirby, Ross T. Whitaker

The visualization of variability in surfaces embedded in 3D, which is a type of ensemble uncertainty visualization, provides a means of understanding the underlying distribution of a collection or ensemble of surfaces. This work extends the contour boxplot technique to 3D and evaluates it against an enumeration-style visualization of the ensemble members and other conventional visualizations used by atlas builders. The authors demonstrate the efficacy of using the 3D contour boxplot ensemble visualization technique to analyze shape alignment and variability in atlas construction and analysis as a real-world application.


Spatial Analytic Interfaces: Spatial User Interfaces for In-Situ Visual Analytics

Barret Ens, Pourang Irani

As wearable devices gain acceptance, we ask "What do user interfaces look like in a post-smartphone world?" and "Can these future interfaces support sophisticated interactions in a mobile context?" In stark contrast to the micro-interactions of current wearable interfaces lies visual analytics. A hallmark of such platforms is the ability to simultaneously view multiple linked visualizations of diverse datasets. We draw from visual analytic concepts to address the growing need of individuals to manage information on personal devices. We propose Spatial Analytic Interfaces to leverage the benefits of spatial interaction to enable everyday visual analytic tasks to be performed in-situ, at the most beneficial place and time. We explore the possibilities for such interfaces using head-worn display technology, to integrate multiple information views into the user's physical environment. We discuss current developments and propose research goals for the successful development of SUI for in-situ visual analytics.


Visualizing Evaluation Structures using Layered Graph Drawings

Yosuke Onoue, Nobuyuki Kukimoto, Naohisa Sakamoto, Kazuo Misue, Koji Koyamada

We propose a method for visualizing evaluation structures that is based on layered graph drawing techniques. An evaluation structure is a hierarchical structure of human cognition extracted from interviews based on the evaluation grid method. An evaluation structure can be defined as a directed acyclic graph (DAG). The Sugiyama framework is a popular method for constructing DAGs. A new layer assignment method that is a part of the Sugiyama framework is proposed to satisfy the requirements for drawing evaluation structures. We formulate a layer assignment problem by considering the sum of squares of arc lengths to be an integer quadratic programming (IQP) problem. Moreover, we transform the IQP problem into an equivalent integer linear programming (ILP) problem for computational efficiency. Evaluations demonstrate that the layered graph drawing with the proposed layer assignment method is preferred by users and aids in the understanding of evaluation structures.