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Pacific Visualization Symposium (PacificVis), 2014 IEEE

Date 4-7 March 2014

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  • [Front cover]

    Publication Year: 2014 , Page(s): C4
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  • [Title page i]

    Publication Year: 2014 , Page(s): i
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  • [Title page iii]

    Publication Year: 2014 , Page(s): iii
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  • [Copyright notice]

    Publication Year: 2014 , Page(s): iv
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  • Table of contents

    Publication Year: 2014 , Page(s): v - ix
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  • Foreword to PacificVis 2014 Symposium Proceedings

    Publication Year: 2014 , Page(s): x - xi
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  • Organizing Committee

    Publication Year: 2014 , Page(s): xii
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  • Program Committee

    Publication Year: 2014 , Page(s): xiii
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  • Visualization Notes Committee

    Publication Year: 2014 , Page(s): xiv
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  • Steering Committee

    Publication Year: 2014 , Page(s): xv
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  • Reviewers

    Publication Year: 2014 , Page(s): xvi - xvii
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  • Welcome to the PacificVAST Workshop

    Publication Year: 2014 , Page(s): xviii
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  • Keynotes

    Publication Year: 2014 , Page(s): xix - xx
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (428 KB)  

    These keynotes speeches discuss the following: Expanding the Universe- From Volume Rendering to High-Dimensional Data Visualization; Comparative Visualization. View full abstract»

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  • Exploring Curved Schematization

    Publication Year: 2014 , Page(s): 1 - 8
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (777 KB) |  | HTML iconHTML  

    Hand-drawn schematized maps traditionally make extensive use of curves. However, there are few automated approaches for curved schematization most previous work focuses on straight lines. We present a new algorithm for area-preserving curved schematization of geographic outlines. Our algorithm converts a simple polygon into a schematic crossing-free representation using circular arcs. We use two basic operations to iteratively replace consecutive arcs until the desired complexity is reached. Our results are not restricted to arcs ending at input vertices. The method can be steered towards different degrees of 'curviness': we can encourage or discourage the use of arcs with a large central angle via a single parameter. Our method creates visually pleasing results even for very low output complexities. We conducted an online user study investigating the effectiveness of the curved schematizations compared to straight-line schematizations of equivalent complexity. While the visual complexity of the curved shapes was judged higher than those using straight lines, users generally preferred curved schematizations. We observed that curves significantly improved the ability of users to match schematized shapes of moderate complexity to their unschematized equivalents. View full abstract»

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  • FlowString: Partial Streamline Matching Using Shape Invariant Similarity Measure for Exploratory Flow Visualization

    Publication Year: 2014 , Page(s): 9 - 16
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1819 KB) |  | HTML iconHTML  

    Measuring the similarity of integral curves is fundamental to many important flow data analysis and visualization tasks such as feature detection, pattern querying, streamline clustering and hierarchical exploration. In this paper, we introduce FlowString, a novel approach that extracts shape invariant features from streamlines and utilizes a string-based method for exploratory streamline analysis and visualization. Our solution first resamples streamlines by considering their local feature scales. We then classify resampled points along streamlines based on the shape similarity around their local neighborhoods. We encode each streamline into a string of well-selected shape characters, from which we construct meaningful words for querying and retrieval. A unique feature of our approach is that it captures intrinsic streamline similarity that is invariant under translation, rotation and scaling. Leveraging the suffix tree, we enable efficient search of streamline patterns with arbitrary lengths with the complexity linear to the size of the respective pattern. We design an intuitive interface and user interactions to support flexible querying, allowing exact and approximate searches for robust partial streamline similarity matching. Users can perform queries at either the character level or the word level, and define their own characters or words conveniently for customized search. We demonstrate the effectiveness of FlowString with several flow field data sets of different sizes and characteristics. View full abstract»

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  • Non-overlapping Aggregated Multivariate Glyphs for Moving Objects

    Publication Year: 2014 , Page(s): 17 - 24
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (827 KB) |  | HTML iconHTML  

    In moving object visualization, objects and their attributes are commonly represented by glyphs on a geographic map. In areas on the map densely populated by these objects, visual clutter and occlusion of glyphs occur. We propose a method to solve this problem by partitioning the set of all objects into subsets that are each visualized using an aggregated multivariate glyph that shows the distribution of several attributes of its objects, such as heading, type and velocity. We choose the combination of subsets and glyph design such that the glyphs do not overlap and the number of subsets is approximately maximal. The partition is maintained and updated while the objects move. We use examples from the maritime domain, but our method is applicable to a wider range of dynamic data. Through a user study we find that, for a set of representative tasks, our method does not perform significantly worse than competitive visualizations with respect to correctness. Furthermore, it performs significantly better for density comparison tasks in high density data sets. We also find that the participants of the user study have a preference for our method. View full abstract»

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  • FlowTour: An Automatic Guide for Exploring Internal Flow Features

    Publication Year: 2014 , Page(s): 25 - 32
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2339 KB) |  | HTML iconHTML  

    We present FlowTour, a novel framework that provides an automatic guide for exploring internal flow features. Our algorithm first identifies critical regions and extracts their skeletons for feature characterization and streamline placement. We then create candidate viewpoints based on the construction of a simplified mesh enclosing each critical region and select best viewpoints based on a viewpoint quality measure. Finally, we design a tour that traverses all selected viewpoints in a smooth and efficient manner for visual navigation and exploration of the flow field. Unlike most existing works which only consider external viewpoints, a unique contribution of our work is that we also incorporate internal viewpoints to enable a clear observation of what lies inside of the flow field. Our algorithm is thus particularly useful for exploring hidden or occluded flow features in a large and complex flow field. We demonstrate our algorithm with several flow data sets and perform a user study to confirm the effectiveness of our approach. View full abstract»

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  • Scalable Lagrangian-Based Attribute Space Projection for Multivariate Unsteady Flow Data

    Publication Year: 2014 , Page(s): 33 - 40
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2299 KB) |  | HTML iconHTML  

    In this paper, we present a novel scalable approach for visualizing multivariate unsteady flow data with Lagrangian-based Attribute Space Projection (LASP). The distances between spatial temporal samples are evaluated by their attribute values along the advection directions in the flow field. The massive samples are then projected into 2D screen space for feature identification and selection. A hybrid parallel system, which tightly integrates a MapReduce-style particle tracer with a scalable algorithm for massive projection, is designed to support the large scale analysis. Results show that the proposed methods and system are capable of visualizing features in the unsteady flow, which couples multivariate analysis of vector and scalar attributes with projection. View full abstract»

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  • Moment Invariants for 2D Flow Fields Using Normalization

    Publication Year: 2014 , Page(s): 41 - 48
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1199 KB) |  | HTML iconHTML  

    The analysis of 2D flow data is often guided by the search for characteristic structures with semantic meaning. One way to approach this question is to identify structures of interest by a human observer. The challenge then, is to find similar structures in the same or other datasets on different scales and orientations. In this paper, we propose to use moment invariants as pattern descriptors for flow fields. Moment invariants are one of the most popular techniques for the description of objects in the field of image recognition. They have recently also been applied to identify 2D vector patterns limited to the directional properties of flow fields. In contrast to previous work, we follow the intuitive approach of moment normalization, which results in a complete and independent set of translation, rotation, and scaling invariant flow field descriptors. They also allow to distinguish flow features with different velocity profiles. We apply the moment invariants in a pattern recognition algorithm to a real world dataset and show that the theoretic results can be extended to discrete functions in a robust way. View full abstract»

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  • 2D Vector Field Simplification Based on Robustness

    Publication Year: 2014 , Page(s): 49 - 56
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4476 KB) |  | HTML iconHTML  

    Vector field simplification aims to reduce the complexity of the flow by removing features in order of their relevance and importance, to reveal prominent behavior and obtain a compact representation for interpretation. Most existing simplification techniques based on the topological skeleton successively remove pairs of critical points connected by separatrices, using distance or area-based relevance measures. These methods rely on the stable extraction of the topological skeleton, which can be difficult due to instability in numerical integration, especially when processing highly rotational flows. These geometric metrics do not consider the flow magnitude, an important physical property of the flow. In this paper, we propose a novel simplification scheme derived from the recently introduced topological notion of robustness, which provides a complementary view on flow structure compared to the traditional topological-skeleton-based approaches. Robustness enables the pruning of sets of critical points according to a quantitative measure of their stability, that is, the minimum amount of vector field perturbation required to remove them. This leads to a hierarchical simplification scheme that encodes flow magnitude in its perturbation metric. Our novel simplification algorithm is based on degree theory, has fewer boundary restrictions, and so can handle more general cases. Finally, we provide an implementation under the piecewise-linear setting and apply it to both synthetic and real-world datasets. View full abstract»

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  • An Edge-Bundling Layout for Interactive Parallel Coordinates

    Publication Year: 2014 , Page(s): 57 - 64
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1762 KB) |  | HTML iconHTML  

    Parallel Coordinates is an often used visualization method for multidimensional data sets. Its main challenges for large data sets are visual clutter and over plotting which hamper the recognition of patterns in the data. We present an edge-bundling method using density-based clustering for each dimension. This reduces clutter and provides a faster overview of clusters and trends. Moreover, it allows rendering the clustered lines using polygons, decreasing rendering time remarkably. In addition, we design interactions to support multidimensional clustering with this method. A user study shows improvements over the classic parallel coordinates plot in two user tasks: correlation estimation and subset tracing. View full abstract»

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  • Visual Signature of High-Dimensional Geometry in Parallel Coordinates

    Publication Year: 2014 , Page(s): 65 - 72
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1813 KB) |  | HTML iconHTML  

    Although we can interactively rotate a 3D projected high-dimensional geometry and observe its dynamic changes, this traditional visualization method is limited and highly sensitive to the choice of viewing direction. Parallel-coordinates plots supplement this visualization scenario by providing statistical analysis of the geometry for distinct pairs of co-dimensions. Such analysis results in visual signatures that embed geometric structures such as symmetry, and thus allows us to overview the status of the missing dimensions while exploring the projected geometry. This paper presents a blue-noise sampling approach for efficient construction of continuous parallel-coordinates plots of high-dimensional geometric surfaces defined by mathematical equations. We employ the parallel-coordinates plots with the embedded visual signatures to assist the interactive exploration of high-dimensional geometries, typically for 2-manifold embedded in 4-space. While we interactively explore the 3D projected geometry, we can observe dynamic changes on its visual signature. Various geometric properties can also be identified and visualized. Moreover, we can interactively brush the plots, and see their counterparts in the 3D projection. Assorted geometric properties such as curvature can further be used to enhance the visual signature. View full abstract»

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  • ScagExplorer: Exploring Scatterplots by Their Scagnostics

    Publication Year: 2014 , Page(s): 73 - 80
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2037 KB) |  | HTML iconHTML  

    A scatter plot displays a relation between a pair of variables. Given a set of v variables, there are v(v- 1)/2 pairs of variables, and thus the same number of possible pair wise scatter plots. Therefore for even small sets of variables, the number of scatter plots can be large. Scatter plot matrices (SPLOMs) can easily run out of pixels when presenting high-dimensional data. We introduce a theoretical method and a testbed for assessing whether our method can be used to guide interactive exploration of high-dimensional data. The method is based on nine characterizations of the 2D distributions of orthogonal pair wise projections on a set of points in multidimensional Euclidean space. Working directly with these characterizations, we can locate anomalies for further analysis or search for similar distributions in a large SPLOM with more than a hundred dimensions. Our testbed, ScagExplorer, is developed in order to evaluate the feasibility of handling huge collections of scatter plots. View full abstract»

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  • Using Entropy-Related Measures in Categorical Data Visualization

    Publication Year: 2014 , Page(s): 81 - 88
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1907 KB) |  | HTML iconHTML  

    A wide variety of real-world applications generate massive high dimensional categorical datasets. These datasets contain categorical variables whose values comprise a set of discrete categories. Visually exploring these datasets for insights is of great interest and importance. However, their discrete nature often confounds the direct application of existing multidimensional visualization techniques. We use measures of entropy, mutual information, and joint entropy as a means of harnessing this discreteness to generate more effective visualizations. We conduct user studies to assess the benefits in visual knowledge discovery. View full abstract»

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  • Hierarchical Focus+Context Heterogeneous Network Visualization

    Publication Year: 2014 , Page(s): 89 - 96
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1088 KB) |  | HTML iconHTML  

    Aggregation is a scalable strategy for dealing with large network data. Existing network visualizations have allowed nodes to be aggregated based on node attributes or network topology, each of which has its own advantages. However, very few previous systems have the capability to enjoy the best of both worlds. This paper presents OnionGraph, an integrated framework for exploratory visual analysis of large heterogeneous networks. OnionGraph allows nodes to be aggregated based on either node attributes, topology, or a mixture of both. Subsets of nodes can be flexibly split and merged under the hierarchical focus+context interaction model, supporting sophisticated analysis of the network data. Node aggregations that contain subsets of nodes are displayed with multiple concentric circles, or the onion metaphor, indicating how many levels of abstraction they contain. We have evaluated the OnionGraph tool in two real-world cases. Performance experiments demonstrate that on a commodity desktop, OnionGraph can scale to million-node networks while preserving the interactivity for analysis. View full abstract»

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