By Topic

Information Visualization, 2004. INFOVIS 2004. IEEE Symposium on

Date 10-12 Oct. 2004

Filter Results

Displaying Results 1 - 25 of 76
  • IEEE Symposium on Information Visualization 2004 - Front Cover

    Publication Year: 2004 , Page(s): c1
    Save to Project icon | Request Permissions | PDF file iconPDF (2193 KB)  
    Freely Available from IEEE
  • IEEE Symposium on Information Visualization 2004 - Cover Image Credits

    Publication Year: 2004 , Page(s): c2
    Save to Project icon | Request Permissions | PDF file iconPDF (43 KB)  
    Freely Available from IEEE
  • IEEE Symposium on Information Visualization 2004 - Title Page

    Publication Year: 2004 , Page(s): i
    Save to Project icon | Request Permissions | PDF file iconPDF (26 KB)  
    Freely Available from IEEE
  • IEEE Symposium on Information Visualization 2004 - Copyright Page

    Publication Year: 2004 , Page(s): ii
    Save to Project icon | Request Permissions | PDF file iconPDF (39 KB)  
    Freely Available from IEEE
  • IEEE Symposium on Information Visualization 2004 - Table of contents

    Publication Year: 2004 , Page(s): iii - iv
    Save to Project icon | Request Permissions | PDF file iconPDF (52 KB)  
    Freely Available from IEEE
  • IEEE Symposium on Information Visualization 2004 - Back Cover

    Publication Year: 2004 , Page(s): c4
    Save to Project icon | Request Permissions | PDF file iconPDF (331 KB)  
    Freely Available from IEEE
  • Preface

    Publication Year: 2004 , Page(s): vii
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | PDF file iconPDF (30 KB)  
    Freely Available from IEEE
  • IEEE Visualization and Graphics Technical Committee (VGTC)

    Publication Year: 2004 , Page(s): viii
    Save to Project icon | Request Permissions | PDF file iconPDF (114 KB)  
    Freely Available from IEEE
  • Organizers

    Publication Year: 2004 , Page(s): ix
    Save to Project icon | Request Permissions | PDF file iconPDF (18 KB)  
    Freely Available from IEEE
  • Steering Committee

    Publication Year: 2004 , Page(s): ix
    Save to Project icon | Request Permissions | PDF file iconPDF (18 KB)  
    Freely Available from IEEE
  • Program Committee

    Publication Year: 2004 , Page(s): x
    Save to Project icon | Request Permissions | PDF file iconPDF (44 KB)  
    Freely Available from IEEE
  • External reviewers

    Publication Year: 2004 , Page(s): x
    Save to Project icon | Request Permissions | PDF file iconPDF (44 KB)  
    Freely Available from IEEE
  • Keynote Address: From Information Visualization to Sensemaking: Connecting the Mind's Eye to the Mind's Muscle

    Publication Year: 2004 , Page(s): xii
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (66 KB)  

    Provides an abstract of the keynote presentation and a brief professional biography of the presenter. The complete presentation was not made available for publication as part of the conference proceedings. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Capstone Address: Visualization as a Medium for Capturing and Sharing Thoughts

    Publication Year: 2004 , Page(s): xiii
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | PDF file iconPDF (43 KB)  
    Full text access may be available. Click article title to sign in or learn about subscription options.
  • An Evaluation of Microarray Visualization Tools for Biological Insight

    Publication Year: 2004 , Page(s): 1 - 8
    Cited by:  Papers (19)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (306 KB) |  | HTML iconHTML  

    High-throughput experiments such as gene expression microarrays in the life sciences result in large datasets. In response, a wide variety of visualization tools have been created to facilitate data analysis. Biologists often face a dilemma in choosing the best tool for their situation. The tool that works best for one biologist may not work well for another due to differences in the type of insight they seek from their data. A primary purpose of a visualization tool is to provide domain-relevant insight into the data. Ideally, any user wants maximum information in the least possible time. In this paper we identify several distinct characteristics of insight that enable us to recognize and quantify it. Based on this, we empirically evaluate five popular microarray visualization tools. Our conclusions can guide biologists in selecting the best tool for their data, and computer scientists in developing and evaluating visualizations View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • User Experiments with Tree Visualization Systems

    Publication Year: 2004 , Page(s): 9 - 16
    Cited by:  Papers (14)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4005 KB) |  | HTML iconHTML  

    This paper describes a comparative experiment with five well-known tree visualization systems, and Windows Explorer as a baseline system. Subjects performed tasks relating to the structure of a directory hierarchy, and to attributes of files and directories. Task completion times, correctness and user satisfaction were measured, and video recordings of subjects' interaction with the systems were made. Significant system and task type effects and an interaction between system and task type were found. Qualitative analyses of the video recordings were thereupon conducted to determine reasons for the observed differences, resulting in several findings and design recommendations as well as implications for future experiments with tree visualization systems View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A Comparison of the Readability of Graphs Using Node-Link and Matrix-Based Representations

    Publication Year: 2004 , Page(s): 17 - 24
    Cited by:  Papers (43)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (396 KB) |  | HTML iconHTML  

    In this paper, we describe a taxonomy of generic graph related tasks and an evaluation aiming at assessing the readability of two representations of graphs: matrix-based representations and node-link diagrams. This evaluation bears on seven generic tasks and leads to important recommendations with regard to the representation of graphs according to their size and density. For instance, we show that when graphs are bigger than twenty vertices, the matrix-based visualization performs better than node-link diagrams on most tasks. Only path finding is consistently in favor of node-link diagrams throughout the evaluation View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • GeoTime information visualization

    Publication Year: 2004 , Page(s): 25 - 32
    Cited by:  Papers (24)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (662 KB) |  | HTML iconHTML  

    Analyzing observations over time and geography is a common task but typically requires multiple, separate tools. The objective of our research has been to develop a method to visualize, and work with, the spatial interconnectedness of information over time and geography within a single, highly interactive 3D view. A novel visualization technique for displaying and tracking events, objects and activities within a combined temporal and geospatial display has been developed. This technique has been implemented as a demonstratable prototype called GeoTime in order to determine potential utility. Initial evaluations have been with military users. However, we believe the concept is applicable to a variety of government and business analysis tasks View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • RecMap: Rectangular Map Approximations

    Publication Year: 2004 , Page(s): 33 - 40
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1126 KB) |  | HTML iconHTML  

    In many application domains, data is collected and referenced by its geospatial location. Nowadays, different kinds of maps are used to emphasize the spatial distribution of one or more geospatial attributes. The nature of geospatial statistical data is the highly nonuniform distribution in the real world data sets. This has several impacts on the resulting map visualizations. Classical area maps tend to highlight patterns in large areas, which may, however, be of low importance. Cartographers and geographers used cartograms or value-by-area maps to address this problem long before computers were available. Although many automatic techniques have been developed, most of the value-by-area cartograms are generated manually via human interaction. In this paper, we propose a novel visualization technique for geospatial data sets called RecMap. Our technique approximates a rectangular partition of the (rectangular) display area into a number of map regions preserving important geospatial constraints. It is a fully automatic technique with explicit user control over all exploration constraints within the exploration process. Experiments show that our technique produces visualizations of geospatial data sets, which enhance the discovery of global and local correlations, and demonstrate its performance in a variety of applications View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • EZEL: a Visual Tool for Performance Assessment of Peer-to-Peer File-Sharing Network

    Publication Year: 2004 , Page(s): 41 - 48
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (521 KB) |  | HTML iconHTML  

    In this paper we present EZEL, a visual tool we developed for the performance assessment of peer-to-peer file-sharing networks. We start by identifying the relevant data transferred in this kind of networks and the main performance assessment questions. Then we describe the visualization of data from two different points of view. First we take servers as focal points and we introduce a new technique, faded cushioning, which allows visualizing the same data from different perspectives. Secondly, we present the viewpoint of files, and we expose the correlations with the server stance via a special scatter plot. Finally, we discuss how our tool, based on the described techniques, is effective in the performance assessment of peer-to-peer file-sharing networks View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A History Mechanism for Visual Data Mining

    Publication Year: 2004 , Page(s): 49 - 56
    Cited by:  Papers (10)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (347 KB) |  | HTML iconHTML  

    A major challenge of current visualization and visual data mining (VDM) frameworks is to support users in the orientation in complex visual mining scenarios. An important aspect to increase user support and user orientation is to use a history mechanism that, first of all, provides un- and redoing functionality. In this paper, we present a new approach to include such history functionality into a VDM framework. Therefore, we introduce the theoretical background, outline design and implementation aspects of a history management unit, and conclude with a discussion showing the usefulness of our history management in a VDM framework View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Steerable, Progressive Multidimensional Scaling

    Publication Year: 2004 , Page(s): 57 - 64
    Cited by:  Papers (15)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (433 KB) |  | HTML iconHTML  

    Current implementations of multidimensional scaling (MDS), an approach that attempts to best represent data point similarity in a low-dimensional representation, are not suited for many of today's large-scale datasets. We propose an extension to the spring model approach that allows the user to interactively explore datasets that are far beyond the scale of previous implementations of MDS. We present MDSteer, a steerable MDS computation engine and visualization tool that progressively computes an MDS layout and handles datasets of over one million points. Our technique employs hierarchical data structures and progressive layouts to allow the user to steer the computation of the algorithm to the interesting areas of the dataset. The algorithm iteratively alternates between a layout stage in which a subselection of points are added to the set of active points affected by the MDS iteration, and a binning stage which increases the depth of the bin hierarchy and organizes the currently unplaced points into separate spatial regions. This binning strategy allows the user to select onscreen regions of the layout to focus the MDS computation into the areas of the dataset that are assigned to the selected bins. We show both real and common synthetic benchmark datasets with dimensionalities ranging from 3 to 300 and cardinalities of over one million points View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A Rank-by-Feature Framework for Unsupervised Multidimensional Data Exploration Using Low Dimensional Projections

    Publication Year: 2004 , Page(s): 65 - 72
    Cited by:  Papers (24)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (573 KB) |  | HTML iconHTML  

    Exploratory analysis of multidimensional data sets is challenging because of the difficulty in comprehending more than three dimensions. Two fundamental statistical principles for the exploratory analysis are (1) to examine each dimension first and then find relationships among dimensions, and (2) to try graphical displays first and then find numerical summaries (D.S. Moore, (1999). We implement these principles in a novel conceptual framework called the rank-by-feature framework. In the framework, users can choose a ranking criterion interesting to them and sort 1D or 2D axis-parallel projections according to the criterion. We introduce the rank-by-feature prism that is a color-coded lower-triangular matrix that guides users to desired features. Statistical graphs (histogram, boxplot, and scatterplot) and information visualization techniques (overview, coordination, and dynamic query) are combined to help users effectively traverse 1D and 2D axis-parallel projections, and finally to help them interactively find interesting features View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Value and Relation Display for Interactive Exploration of High Dimensional Datasets

    Publication Year: 2004 , Page(s): 73 - 80
    Cited by:  Papers (16)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (282 KB) |  | HTML iconHTML  

    Traditional multidimensional visualization techniques, such as glyphs, parallel coordinates and scatterplot matrices, suffer from clutter at the display level and difficult user navigation among dimensions when visualizing high dimensional datasets. In this paper, we propose a new multidimensional visualization technique named a value and relation (VaR) display, together with a rich set of navigation and selection tools, for interactive exploration of datasets with up to hundreds of dimensions. By explicitly conveying the relationships among the dimensions of a high dimensional dataset, the VaR display helps users grasp the associations among dimensions. By using pixel-oriented techniques to present values of the data items in a condensed manner, the VaR display reveals data patterns in the dataset using as little screen space as possible. The navigation and selection tools enable users to interactively reduce clutter, navigate within the dimension space, and examine data value details within context effectively and efficiently. The VaR display scales well to datasets with large numbers of data items by employing sampling and texture mapping. A case study on a real dataset, as well as the VaR displays of multiple real datasets throughout the paper, reveals how our proposed approach helps users interactively explore high dimensional datasets with large numbers of data items View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Uncovering Clusters in Crowded Parallel Coordinates Visualizations

    Publication Year: 2004 , Page(s): 81 - 88
    Cited by:  Papers (22)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (463 KB) |  | HTML iconHTML  

    The one-to-one strategy of mapping each single data item into a graphical marker adopted in many visualization techniques has limited usefulness when the number of records and/or the dimensionality of the data set are very high. In this situation, the strong overlapping of graphical markers severely hampers the user's ability to identify patterns in the data from its visual representation. We tackle this problem here with a strategy that computes frequency or density information from the data set, and uses such information in parallel coordinates visualizations to filter out the information to be presented to the user, thus reducing visual clutter and allowing the analyst to observe relevant patterns in the data. The algorithms to construct such visualizations, and the interaction mechanisms supported, inspired by traditional image processing techniques such as grayscale manipulation and thresholding are also presented. We also illustrate how such algorithms can assist users to effectively identify clusters in very noisy large data sets View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.