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Visualization Symposium, 2009. PacificVis '09. IEEE Pacific

Date 20-23 April 2009

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  • [Title page]

    Publication Year: 2009 , Page(s): i
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  • [Copyright notice]

    Publication Year: 2009 , Page(s): ii
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  • Contents

    Publication Year: 2009 , Page(s): iii - iv
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  • Supporting organizations

    Publication Year: 2009 , Page(s): v
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  • Preface

    Publication Year: 2009 , Page(s): vi
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  • IEEE Computer Society Visualization & Graphics Technical Committee (VGTC)

    Publication Year: 2009 , Page(s): vii
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  • Committee

    Publication Year: 2009 , Page(s): viii
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  • list-reviewer

    Publication Year: 2009 , Page(s): ix
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  • Keynote address Knots, maps, and tiles: Three visual puzzles

    Publication Year: 2009 , Page(s): x
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    Visualization aims at providing insight to its users. Now and then I am a user myself, and use visualization trying to solve a puzzle and to satisfy my curiosity. Simple questions turn out to be challenging problems, leading to a personal quest for their solution and resulting in intriguing images and animations. In my presentation I will present three such puzzles, all in the area of mathematical visualization. View full abstract»

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  • Capstone address Introduction to processing and visualization of Chang'e-1 Lunar exploration data

    Publication Year: 2009 , Page(s): xi
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  • [Blank page]

    Publication Year: 2009
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  • Visualizing time-varying features with TAC-based distance fields

    Publication Year: 2009 , Page(s): 1 - 8
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (9880 KB) |  | HTML iconHTML  

    To analyze time-varying data sets, tracking features over time is often necessary to better understand the dynamic nature of the underlying physical process. Tracking 3D time-varying features, however, is non-trivial when the boundaries of the features cannot be easily defined. In this paper, we propose a new framework to visualize time-varying features and their motion without explicit feature segmentation and tracking. In our framework, a time-varying feature is described by a time series or time activity curve (TAC). To compute the distance, or similarity, between a voxel's time series and the feature, we use the dynamic time warping (DTW) distance metric. The purpose of DTW is to compare the shape similarity between two time series with an optimal warping of time so that the phase shift of the feature in time can be accounted for. After applying DTW to compare each voxel's time series with the feature, a time-invariant distance field can be computed. The amount of time warping required for each voxel to match the feature provides an estimate of the time when the feature is most likely to occur. Based on the TAC-based distance field, several visualization methods can be derived to highlight the position and motion of the feature. We present several case studies to demonstrate and compare the effectiveness of our framework. View full abstract»

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  • Dual streamline seeding

    Publication Year: 2009 , Page(s): 9 - 16
    Cited by:  Papers (9)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2994 KB) |  | HTML iconHTML  

    This work introduces a novel streamline seeding technique based on dual streamlines that are orthogonal to the vector field, instead of tangential. The greedy algorithm presented here produces a net of orthogonal streamlines that is iteratively refined resulting in good domain coverage and a high degree of continuity and uniformity. The algorithm is easy to implement and efficient, and it naturally extends to curved surfaces. View full abstract»

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  • Interactive feature extraction and tracking by utilizing region coherency

    Publication Year: 2009 , Page(s): 17 - 24
    Cited by:  Papers (8)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3482 KB) |  | HTML iconHTML  

    The ability to extract and follow time-varying flow features in volume data generated from large-scale numerical simulations enables scientists to effectively see and validate modeled phenomena and processes. Extracted features often take much less storage space and computing resources to visualize. Most feature extraction and tracking methods first identify features of interest in each time step independently, then correspond these features in consecutive time steps of the data. Since these methods handle each time step separately, they do not use the coherency of the feature along the time dimension in the extraction process. In this paper, we present a prediction-correction method that uses a prediction step to make the best guess of the feature region in the subsequent time step, followed by growing and shrinking the border of the predicted region to coherently extract the actual feature of interest. This method makes use of the temporal-space coherency of the data to accelerate the extraction process while implicitly solving the tedious correspondence problem that previous methods focus on. Our method is low cost with very little storage overhead, and thus facilitates interactive or runtime extraction and visualization, unlike previous methods which were largely suited for batch-mode processing due to high computational cost. View full abstract»

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  • An organization topographic map for visualizing business hierarchical relationships

    Publication Year: 2009 , Page(s): 25 - 32
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (831 KB) |  | HTML iconHTML  

    Visualization of the actual conditions of an organization is a very challenging issue. We propose a new system called a Business Microscope that senses the activities of people in an organization and provides visual feedback to users. We use name-tag shaped sensor nodes to measure face-to-face interaction between employees. The massive amount of data collected by the sensor-network terminal are signal-processed by the server and displayed as an organization's topographic map that displays the frequencies of organizational activities. To depict the organization's topographic map, our system creates a novel relation tree using the interaction data from all pairs of members. In this kind of map, some groups in the organization hierarchically form islands. Members in those islands who have relationships with many others form mountains that are plotted with contours. We can comprehend the actual conditions of organizations from our topographic map. We tested our technique in several experiments to evaluate this system. View full abstract»

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  • Visualizing metrics on areas of interest in software architecture diagrams

    Publication Year: 2009 , Page(s): 33 - 40
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1600 KB) |  | HTML iconHTML  

    We present a new method for the combined visualization of software architecture diagrams, such as UML class diagrams or component diagrams, and software metrics defined on groups of diagram elements. Our method extends an existing rendering technique for the so-called areas of interest in system architecture diagrams to visualize several metrics, possibly having missing values, defined on overlapping areas of interest. For this, we use a solution that combines texturing, blending, and smooth scattered-data point interpolation. Our new method simplifies the task of visually correlating the distribution and outlier values of a multivariate metric dataset with a system's structure. We demonstrate the application of our method on component and class diagrams extracted from real-world systems. View full abstract»

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  • HiMap: Adaptive visualization of large-scale online social networks

    Publication Year: 2009 , Page(s): 41 - 48
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (966 KB) |  | HTML iconHTML  

    Visualizing large-scale online social network is a challenging yet essential task. This paper presents HiMap, a system that visualizes it by clustered graph via hierarchical grouping and summarization. HiMap employs a novel adaptive data loading technique to accurately control the visual density of each graph view, and along with the optimized layout algorithm and the two kinds of edge bundling methods, to effectively avoid the visual clutter commonly found in previous social network visualization tools. HiMap also provides an integrated suite of interactions to allow the users to easily navigate the social map with smooth and coherent view transitions to keep their momentum. Finally, we confirm the effectiveness of HiMap algorithms through graph-travesal based evaluations. View full abstract»

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  • Toward effective insight management in visual analytics systems

    Publication Year: 2009 , Page(s): 49 - 56
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (191 KB) |  | HTML iconHTML  

    Although significant progress has been made toward effective insight discovery in visual sense making approaches, there is a lack of effective and efficient approaches to manage the large amounts of insights discovered. In this paper, we propose a systematic approach to leverage this problem around the concept of facts. Facts refer to patterns, relationships, or anomalies extracted from data under analysis. They are the direct products of visual exploration and permit construction of insights together with user's mental model and evaluation. Different from the mental model, the type of facts that can be discovered from data is predictable and application-independent. Thus it is possible to develop a general fact management framework (FMF) to allow visualization users to effectively and efficiently annotate, browse, retrieve, associate, and exchange facts. Since facts are essential components of insights, it will be feasible to extend FMF to effective insight management in a variety of visual analytics approaches. Toward this goal, we first construct a fact taxonomy that categorizes various facts in multidimensional data and captures their essential attributes through extensive literature survey and user studies. We then propose a conceptual framework of fact management based upon this fact taxonomy. A concrete scenario of visual sense making on real data sets illustrates how this FMF will work. View full abstract»

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  • Visual support for the understanding of simulation processes

    Publication Year: 2009 , Page(s): 57 - 64
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2683 KB) |  | HTML iconHTML  

    Current visualization systems are typically based on the concept of interactive post-processing. This decoupling of data visualization from the process of data generation offers a flexible application of visualization tools. It can also lead, however, to information loss in the visualization. Therefore, a combination of the visualization of the data generating process with the visualization of the produced data offers significant support for the understanding of the abstract data sets as well as the underlying process. Due to the application-specific characteristics of data generating processes, the task requires tailored visualization concepts. In this work, we focus on the application field of simulating biochemical reaction networks as discrete-event systems. These stochastic processes generate multi-run and multivariate time-series, which are analyzed and compared on three different process levels: model, experiment, and the level of multi-run simulation data, each associated with a broad range of analysis goals. To meet these challenging characteristics, we present visualization concepts specifically tailored to all three process levels. The fundament of all three visualization concepts is a compact view that relates the multi-run simulation data to the characteristics of the model structure and the experiment. The view provides the visualization at the experiment level. The visualization at the model level coordinates multiple instances of this view for the comparison of experiments. At the level of multi-run simulation data, the views gives an overview on the data, which can be analyzed in detail in time-series views suited for the analysis goals. Although we derive our visualization concepts for one concrete simulation process, our general concept of tailoring the visualization concepts to process levels is generally applicable for the visualization of simulation processes. View full abstract»

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  • Optimized data transfer for time-dependent, GPU-based glyphs

    Publication Year: 2009 , Page(s): 65 - 72
    Cited by:  Papers (6)
    Multimedia
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1569 KB) |  | HTML iconHTML  

    Particle-based simulations are a popular tool for researchers in various sciences. In combination with the availability of ever larger COTS clusters and the consequently increasing number of simulated particles the resulting datasets pose a challenge for real-time visualization. Additionally the semantic density of the particles exceeds the possibilities of basic glyphs, like splats or spheres and results in dataset sizes larger by at least an order of magnitude. Interactive visualization on common workstations requires a careful optimization of the data management, especially of the transfer between CPU and GPU. We propose a flexible benchmarking tool along with a series of tests to allow the evaluation of the performance of different CPU/GPU combinations in relation to a particular implementation. We evaluate different uploading strategies and rendering methods for point-based compound glyphs suitable for representing the aforementioned datasets. CPU and GPU-based approaches are compared with respect to their rendering and storage efficiency to point out the optimal solution when dealing with time-dependent datasets. The results of our research are of general interest since they can be transferred to other applications where CPU-GPU bandwidth and a high number of graphical primitives per dataset pose a problem. The employed tool set for streamlining the measurement process is made publicly available. View full abstract»

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  • Out-of-core volume rendering for time-varying fields using a space-partitioning time (SPT) tree

    Publication Year: 2009 , Page(s): 73 - 80
    Cited by:  Papers (3)
    Multimedia
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (627 KB) |  | HTML iconHTML  

    In this paper, we propose a novel out-of-core volume rendering algorithm for large time-varying fields. Exploring temporal and spatial coherences has been an important direction for speeding up the rendering of time-varying data. Previously, there were techniques that hierarchically partition both the time and space domains into a data structure so as to re-use some results from the previous time step in multiresolution rendering; however, it has not been studied on which domain should be partitioned first to obtain a better re-use rate. We address this open question, and show both theoretically and experimentally that partitioning the time domain first is better. We call the resulting structure (a binary time tree as the primary structure and an octree as the secondary structure) the space-partitioning time (SPT) tree. Typically, our SPT-tree rendering has a higher level of details, a higher re-use rate, and runs faster. In addition, we devise a novel cut-finding algorithm to facilitate efficient out-of-core volume rendering using our SPT tree, we develop a novel out-of-core preprocessing algorithm to build our SPT tree I/O-efficiently, and we propose modified error metrics with a theoretical guarantee of a monotonicity property that is desirable for the tree search. The experiments on datasets as large as 25 GB using a PC with only 2 GB of RAM demonstrated the efficacy of our new approach. View full abstract»

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  • Point-based tree representation: A new approach for large hierarchies

    Publication Year: 2009 , Page(s): 81 - 88
    Cited by:  Papers (3)
    Multimedia
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4017 KB) |  | HTML iconHTML  

    Space-filling layout techniques for tree representations are frequently used when the available screen space is small or the data set is large. In this paper, we propose a new approach to space-filling tree representations, which uses mechanisms from the point-based rendering paradigm. Additionally, helpful interaction techniques that tie in with our layout are presented. We will relate our new technique to established space-filling techniques along the lines of a newly developed classification and also evaluate it numerically using the measures of the ink-paper-ratio and overplotted%. View full abstract»

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  • A visual canonical adjacency matrix for graphs

    Publication Year: 2009 , Page(s): 89 - 96
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1258 KB) |  | HTML iconHTML  

    Graph data mining algorithms rely on graph canonical forms to compare different graph structures. These canonical form definitions depend on node and edge labels. In this paper, we introduce a unique canonical visual matrix representation that only depends on a graph's topological information, so that two structurally identical graphs will have exactly the same visual adjacency matrix representation. In this canonical matrix, nodes are ordered based on a breadth-first search spanning tree. Special rules and filters are designed to guarantee the uniqueness of an arrangement. Such a unique matrix representation provides persistence and a stability which can be used and harnessed in visualization, especially for data exploration and studies. View full abstract»

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  • Evaluation of symbol contrast in scatterplots

    Publication Year: 2009 , Page(s): 97 - 104
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1033 KB) |  | HTML iconHTML  

    Symbols are frequently used to represent data objects in visualization. An appropriate contrast between symbols is a precondition that determines the efficiency of a visual analysis process. We study the contrast between different types of symbols in the context of scatterplots, based on user testing and a quantitative model for symbol contrast. In total, 32 different symbols were generated by using four sizes, two classes (polygon-and asterisk shaped), and four categories of rotational symmetry; and used three different tasks. From the user test results an internal separation space is established for the symbol types under study. In this space, every symbol is represented by a point, and the visual contrasts defined by task performance between the symbols are represented by the distances between the points. The positions of the points in the space, obtained by multidimensional scaling (MDS), reveal the effects of different visual feature scales. Also, larger distances imply better symbol separation for visual tasks, and therefore indicate appropriate choices for symbols. The resulting configurations are discussed, and a number of patterns in the relation between properties of the symbols and the resulting contrast are identified. In short we found that the size effect in the space is not linear and more dominant than shape effect. View full abstract»

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  • A self-adaptive treemap-based technique for visualizing hierarchical data in 3D

    Publication Year: 2009 , Page(s): 105 - 112
    Cited by:  Papers (1)
    Multimedia
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1068 KB) |  | HTML iconHTML  

    In this paper, we present a novel adaptive visualization technique where the constituting polygons dynamically change their geometry and other visual attributes depending on user interaction. These changes take place with the objective of conveying required level of detail to the user through each view. Our proposed technique is successfully applied to build a treemap-based but 3D visualization of hierarchical data, a widely used information structure. This new visualization exploits its adaptive nature to address issues like cluttered display, imperceptible hierarchy, lack of smooth zoom-in and out technique which are common in tree visualization. We also present an algorithm which utilizes the flexibility of our proposed technique to deal with occlusion, a problem inherent in any 3D information visualization. On one hand, our work establishes adaptive visualization as a means of displaying tree-structured data in 3D. On the other, it promotes the technique as a potential candidate for being employed to visualize other information structures also. View full abstract»

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