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Tsinghua Science and Technology

Issue 4 • Date Aug. 2012

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Displaying Results 1 - 14 of 14
  • [Front cover]

    Page(s): c1
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    Freely Available from IEEE
  • Contents

    Page(s): 1
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    Freely Available from IEEE
  • Guest editorial: Special issue on visualization and computer graphics

    Page(s): 363 - 364
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    In the decade of data explosion, a significant challenge is how to transform data into understandings and insights that are useful to people. Visualization, the use of computer-supported, interactive visual representations of data to amplify cognition[1], is an important approach to addressing this challenge. In particular, scientific visualization primarily represents physical or geometric data while information visualization mainly represents abstract data such as text documents, graphs, and multidimensional data. Visual analytics, springing out of the fields of information visualization and scientific visualization, is an emerging research area that targets analyzing massive amounts of information for timely decision making[2]. Its basic approach is to use interactive visual interfaces to facilitate analytical reasoning so that human perception abilities and domain knowledge can be exploited together with computational powers. Closely related to visualization, computer graphics is primarily about representation and manipulation of image data by a computer. View full abstract»

    Open Access
  • Network visualization in cell biology

    Page(s): 365 - 382
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (13335 KB)  

    Many of the processes known to take place in biological cells are analyzed in the form of different types of network. The complexity of these networks increases along with our knowledge of these processes, making their analysis more difficult. Network visualization is a powerful analysis method that will have to be developed further to deal with this complexity. This survey provides a brief overview of network visualization in general, followed by an in-depth discussion of its application to three network types specific to cell biology, namely gene regulatory, protein interaction, and metabolic networks. Finally, we discuss the difficulty of visually integrating these network types and trying to compare networks of cells that belong to different organisms. View full abstract»

    Open Access
  • Simple algorithms for network visualization: A tutorial

    Page(s): 383 - 398
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (2470 KB)  

    The graph drawing and information visualization communities have developed many sophisticated techniques for visualizing network data, often involving complicated algorithms that are difficult for the uninitiated to learn. This article is intended for beginners who are interested in programming their own network visualizations, or for those curious about some of the basic mechanics of graph visualization. Four easy-to-program network layout techniques are discussed, with details given for implementing each one: force-directed node-link diagrams, arc diagrams, adjacency matrices, and circular layouts. A Java applet demonstrating these layouts, with open source code, is available at http://www.michaelmcguffin.com/research/simpleNetVis/. The end of this article also briefly surveys research topics in graph visualization, pointing readers to references for further reading. View full abstract»

    Open Access
  • SideKnot: Edge bundling for uncovering relation patterns in graphs

    Page(s): 399 - 408
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (6933 KB)  

    The node-link diagram is an intuitive way to depict a graph and present relationships between entities. Addressing the visual clutter induced by edge crossing and node-edge overlapping is a challenging task as the size of graph outgrows the visualization space. Many edge bundling methods are proposed to disclose high-level edge patterns. Though previous methods can successfully reveal the skeleton graph structure, the relation patterns at the individual node level can be overlooked. In addition, most edge bundling algorithms are computationally complex, which prevents them from scaling up for extremely large graphs. In this article, we extend SideKnot, an efficient edge bundling method to cluster and knot edges at the node side. Our proposed method is light, runs faster than most existing algorithms, and can reveal the relation patterns at the individual node level. Our results show that SideKnot can disclose a node's standing in the graph as well as the directional connection patterns to its peers. View full abstract»

    Open Access
  • Sequential document visualization based on hierarchical parametric histogram curves

    Page(s): 409 - 418
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (2390 KB)  

    Recently, sequential document visualization has attracted much attention for its superior capability in depicting the sequential semantic progression in a single document. However, existing methods commonly take abstractive visual forms such as texts, numbers, and glyphs, and require much user expertise for document exploration. In this paper we propose a sequential visualization to represent a single document with a two-dimensional picture-based storyline, which semantically enhances the comprehension of textual information. We introduce a new parametric modeling approach called the Hierarchical Parametric Histogram Curve (HPHC), which encodes the statistical progression locally and adaptively. By transforming an HPHC into the two-dimensional space with a new locality-preserving embedding algorithm, we create a mapping from points along the curve to descriptive pictures and generate the visualization result. The new representation expresses the primary content with a graphical form, and allows for efficient multi-resolution and focus+context exploration in a long document. Our approach compares favorably with previous work in that it is more intuitive and requires less user expertise. Informal evaluation shows that it is useful in quick document browsing, communication, and understanding, especially for people with low literacy skills. View full abstract»

    Open Access
  • ClustNails: Visual analysis of subspace clusters

    Page(s): 419 - 428
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (655 KB)  

    Subspace clustering addresses an important problem in clustering multi-dimensional data. In sparse multi-dimensional data, many dimensions are irrelevant and obscure the cluster boundaries. Subspace clustering helps by mining the clusters present in only locally relevant subsets of dimensions. However, understanding the result of subspace clustering by analysts is not trivial. In addition to the grouping information, relevant sets of dimensions and overlaps between groups, both in terms of dimensions and records, need to be analyzed. We introduce a visual subspace cluster analysis system called ClustNails. It integrates several novel visualization techniques with various user interaction facilities to support navigating and interpreting the result of subspace clustering. We demonstrate the effectiveness of the proposed system by applying it to the analysis of real world data and comparing it with existing visual subspace cluster analysis systems. View full abstract»

    Open Access
  • Evaluation of a pointwise local visual pattern exploration method

    Page(s): 429 - 439
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (886 KB)  

    Sensitivity analysis is a powerful method for discovering the significant factors that contribute to understanding the interaction between variables in multivariate datasets. A number of sensitivity analysis methods fall into the class of local analysis, in which the sensitivity is defined as the partial derivatives of a target variable with respect to a group of independent variables. In a recent paper, we presented a novel pointwise local pattern exploration system for visual sensitivity analysis. Using this system, analysts are able to explore local patterns and the sensitivity at individual data points, which reveals the relationships between a focal point and its neighbors. In this paper we present several evaluations of the system, including case studies with real datasets, user studies on the effectiveness of the visualizations and interactions, and a detailed description of the experience of a user. View full abstract»

    Open Access
  • RiskVA: A visual analytics system for consumer credit risk analysis

    Page(s): 440 - 451
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    Consumer credit risk analysis plays a significant role in stabilizing a bank's investments and in maximizing its profits. As a large financial institution, Bank of America relies on effective risk analyses to minimize the net credit loss resulting from its credit products (e.g., mortgage and credit card loans). Due to the size and complexity of the data involved in this process, analysts are facing challenges in monitoring the data, comparing its geospatial and temporal patterns, and developing appropriate strategies based on the correlation from multiple analysis perspectives. To address these challenges, we present RiskVA, an interactive visual analytics system that is tailored to support credit risk analysis. RiskVA provides interactive data exploration and correlation, and visually facilitates depictions of market fluctuations and temporal trends for a targeted credit product. When evaluated by analysts from Bank of America, RiskVA was appreciated for its effectiveness in facilitating the bank's risk management. View full abstract»

    Open Access
  • Feature preserving milli-scaling of large format visualizations

    Page(s): 452 - 462
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (2529 KB)  

    Ultra-scale data analysis has created many new challenges for visualization. For example, in climate research with two-dimensional time-varying data, scientists find it crucial to study the hidden temporal relationships from a set of large scale images, whose resolutions are much higher than that of general computer monitors. When scientists can only visualize a small portion (< 1/1000) of a time step at one time, it is extremely challenging to analyze the temporal features from multiple time steps. As this problem cannot be simply solved with interaction or display technologies, this paper presents a milli-scaling approach by designing downscaling algorithms with significant ratios. Our approach can produce readable-sized images of multiple ultra-scale visualizations, while preserving important data features and temporal relationships. Using the climate visualization as the testing application, we demonstrate that our approach provides a new tool for users to effectively make sense of multiple, large-format visualizations. View full abstract»

    Open Access
  • Coherent streamline generation for 2-D vector fields

    Page(s): 463 - 470
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (3218 KB)  

    Visualizing 2-D vector fields using streamlines is one popular flow visualization technique. Standard streamline generation algorithms compute the density of streamlines across the domain, detect features, and employ customized rules to emphasize features. In this process, feature characterization and visual clarity are heavily considered. Simultaneously preserving the temporal coherence for time-varying vector fields, however, remains a challenge. In this paper, we present a coherent and feature-aware streamline generation algorithm by employing a feature-guided streamline seeding technique and a coherent streamline placing scheme. For each frame, a feature map is first computed with critical points or the Finite-Time Lyapunov Exponent (FTLE) approach, and is used to initialize a set of seeds by leveraging the Poisson Disk distribution. These seeds are further optimized by using a deformation-driven moving mesh method. To preserve the temporal coherence, the streamlines generated from the seeds are individually checked subject to their correspondences to the ones in the previous frame. Subsequently, additional streamlines are sequentially inserted in low-density regions. We demonstrate our algorithm on both Computation Fluid Dynamics (CFD) and non-CFD datasets, and compare it with the recent literature. View full abstract»

    Open Access
  • Stable geodesic surface signatures

    Page(s): 471 - 480
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    Shape analysis plays a fundamental role in computer graphics. We present a novel global and intrinsic shape representation for shape analysis, called stable geodesic signature. It is based on the theory of stable closed geodesics and surface Ricci flow. We examine the surface by dynamically deforming it by metric design through Ricci flow, then we observe the behavior of the stable closed geodesics under the evolving Riemannian metrics. When a metric is deforming, some stable geodesic loops will become unstable and shrink to points, or some geodesic loops may merge. The number of stable geodesics forms the signature, which is general for arbitrary surfaces. Experiments on a large amount of surfaces demonstrate the efficiency and efficacy of the stable geodesic signature for shape analysis. View full abstract»

    Open Access
  • The closest and farthest points to an affine ellipse or ellipsoid

    Page(s): 481 - 484
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (2736 KB)  

    Affine ellipses/ellipsoids based bounding volumes are widely used in various graphics applications, such as ray tracing and collision detection. They provide a much tighter fit than the regular ellipses/ellipsoids. The most important operation involved is to compute the closest/farthest point, on a given ellipse/ellipsoid, with respect to a user specified point. In this paper, we first formulate such a problem for the ellipse case into solving a quartic equation and then for the ellipsoid case by solving a system of quartic equations. The method proposed in this paper is elegant and highly efficient. View full abstract»

    Open Access

Aims & Scope

Tsinghua Science and Technology (Tsinghua Sci Technol) aims to highlight scientific achievements in computer science, electronic engineering, and other IT fields. Contributions all over the world are welcome.

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