• October 6, 2013 | Silicon Valley, CA

    Advanced instrument, computing, and imaging technologies enable scientists to study natural and physical phenomena at unprecedented precision, resulting in an explosive growth of data. The size of the collected information about the Web and mobile device users is even greater. The ability to make sense and maximize utilization of such vast amounts of data for knowledge discovery and decision-making is crucial to scientific advancement, business success, clinical treatments, cyber and national security, and disaster management. To provide this ability, we need new tools beyond conventional data mining and statistical analysis. Visualization is one such tool and shown to be effective for gleaning insight in big data. However, it is important that this crucial technology will properly address the challenges and match the needs of the users. This workshop, collocated with the first IEEE International Conference on Big Data (IEEE BigData 2013), will bring big data producers and users together with visualization researchersand practitioners, and foster greater exchange between them. Attendees will be introduced to the latest and greatest research innovations in big data visualization, and also learn how these innovations may impact decision making and knowledge discovery in real-world applications.The Workshop will consist of both invited and contributed talks, along with a panel discussion session. We solicit papers on both original research and practice of big data visualization and analysis. Topics include but not limited to:

    • Streaming data visualization
    • Visual data mining
    • Visual search and recommendation
    • Big data storytelling using visualization
    • Scalable parallel visualization methods
    • Advanced hardware and architecture for data visualization and analysis
    • HCI and big data visualization
    • Big data visualization applications including cyber intelligence, cyber security, business intelligence, e-commerce, scientific data analysis, education, etc.

    Accepted papers will appear in the IEEE Digital Library.

  • Technical Program

    Ballroom AB
    Hyatt Regency Santa Clara
    5101 Great America Parkway, Santa Clara, CA 95054

    08:30-08:40 Opening

    08:40-09:30 Keynote

    "Big Picture" Mixed-Initiative Visual Analytics of Big Data
    Michelle Zhou, IBM Research
    [slides]

    09:30-10:00 Invited Talk (Cancelled due to US government shutdown)

    Data Intensive Visualization and Analysis of Numerically Intensive Applications
    Chris Mitchell, Los Alamos National Laboratory

    VisReduce: Fast and Responsive Incremental Information Visualization of Large Datasets
    Jean-Francois Im, École de technologie supérieure
    Félix Giguère Villegas, Mate1.com
    Michael J. McGuffin, École de technologie supérieure

    [slides]

    10:00-10:30 Coffee Break

    10:30-12:00 Text Data

    Session Chair: Chris Muelder, University of California at Davis

    Visualization of Streaming Data: Observing Change and Context in Information Visualization Techniques
    Milos Krstajic, Daniel Keim, University of Konstanz
    (Presented by Alexander Jaeger)

    CompactMap: A Mental Map Preserving Visual Interface for Streaming Text Data
    Xiaotong Liu, Ohio State University
    Yifan Hu, Stephen North, AT&T Research
    Han-Wei Shen, Ohio State University

    [slides]

    Typograph: Multiscale Spatial Exploration of Text Documents
    Alexander Endert, Russ Burtner, Nick Cramer, Ralph Perko, Shawn Hampton, Kristin Cook, Pacific Northwest National Laboratory
    (Cancelled due to US government shutdown)
    [slides]

    12:00-01:30 Lunch

    01:30-02:30 Rendering

    Overplotting: Unified Solutions under Abstract Rendering
    Joseph Cottam, Indiana University
    Peter Wang, Continuum Analytics
    Andrew Lumsdaine, Indiana University

    DriveSense: Contextual Handling of Large-scale Route Map Data for the Automobile
    Frederik Wiehr, Saarland University
    Vidya Setlur, Nokia Research Center and Tableau Software
    Alark Joshi, University of San Francisco
    [slides]

    02:30-03:30 Visual Analysis

    Session Chair: Alark Joshi, University of San Francisco

    A Novel Visual Analysis Approach for Clustering Large-Scale Social Data
    Zhangye Wang, Wei Chen, Xiajuan Zhou, Chang Chen, Zhejiang University
    Ross Maciejewski, Arizona State University

    [slides]

    Egocentric Storylines for Visual Analysis of Large Dynamic Graphs
    Chris Muelder, Tarik Crnovrsanin, University of California at Davis
    Arnaud Sallaberry, LIRMM, Universit`e Paul Val´ery Montpellier 3
    Kwan-Liu Ma, University of California at Davis

    [slides]

    03:30-04:00 Coffee Break

    04:00-05:30 Scientific Data

    Session Chair: Ross Maciejewski, Arizona State University

    Visualization of Big SPH Simulations via Compressed Octree Grids
    Florian Reichl, Marc Treib, Rüdiger Westermann, Technische Universität München
    [slides]

    A System for Large-Scale Visualization of Streaming Doppler Data
    Peter Kristof, Microsoft
    Bedrich Benes, Carol Song, Lan Zhao, Purdue University

    [slides]

    Dynamic Reduction of Query Result Sets for Interactive Visualization
    Leilani Battle, MIT
    Remco Chang, Tufts University
    Michael Stonebraker, MIT

    [slides]

    05:30-06:30 Fast, Incremental Visualization

    GPU-Accelerated Incremental Correlation Clustering of Large Data with Visual Feedback
    Eric Papenhausen, Bing Wang, Stony Brook University
    Sungsoo Ha, SUNY Korea
    Alla Zelenyuk, Pacific Northwest National Laboratory
    Dan Imre, Imre Consulting
    Klaus Mueller, Stony Brook University

    [slides]

    VisReduce: Fast and Responsive Incremental Information Visualization of Large Datasets
    Jean-Francois Im, École de technologie supérieure
    Félix Giguère Villegas, Mate1.com
    Michael J. McGuffin, École de technologie supérieure

    This talk has been moved to 9:30am



  • Workshop Chair

    Kwan-Liu Ma, University of California at Davis

    Program Committee

    Nan Cao, IBM T. J. Watson Research Center, U.S.A.
    Wei Chen, Zhejiang University, China
    Thomas Ertl, University of Stuttgart, Germany
    Markus Hadwiger, KAUST, Saudi Arabia
    Daniel Keim, University of Konstanz, Germany
    Koji Koyamada, Kyoto University, Japan
    Shixia Liu, Microsoft Research, China
    Klaus Mueller, Stony Brook University, U.S.A.
    Guy Melancon, University of Bordeaux I, France
    Kenneth Moreland, Sandia National Laboratories, U.S.A.
    Chris Muelder, University of California at Davis, U.S.A.
    Chaoli Wang, Michigan Technology University, U.S.A.
    Jing Yang, University of North Carolina at Charlotte, U.S.A.
    Hongfeng Yu, University of Nebraska-Lincoln, U.S.A.

  • Important Dates

    Paper Submission August 1, 2013
    Author Notification (revised) September 2, 2013
    Camera-ready of Accepted Papers September 10, 2013
    Workshop October 6, 2013 (Tentative)
  • Instructions for Authors

    Please submit your paper (up to 8 pages) to here.

    Papers should be formatted according to IEEE Computer Society Proceedings Manuscript Formatting Guidelines: DOC/PDF
    LaTex Template

  • Keynote Speech

    Title: “Big Picture”: Mixed-Initiative Visual Analytics of Big Data

    Abstract

    Visualization has been used for thousands of years to help illustrate ideas and communicate information. However, it requires skills and time to hand craft high-quality, customized information visualization for specific situations (e.g., data characteristics and user tasks). The problem becomes more acute when we must deal with big data. To address this problem, we are researching and developing mixed-initiative visual analytic systems that leverage both the intelligence of humans and machines to aid users in deriving insights from massive data. On the one hand, such a system automatically guides users to perform their data analytic tasks by recommending suitable visualization and discovery paths in context. On the other hand, users interactively explore, verify, and improve visual analytic results, which in turn helps the system to learn from users' behavior and improve its quality over time. In this talk, I will present key technologies that we have developed in building mixed-initiative visual analytic systems, including feature-based visualization recommendation and optimization-based approaches to dynamic data transformation for more effective visualization. I will also use concrete applications to demonstrate the use and value of mixed-initiative visual analytic systems, and discuss existing challenges and future directions in this area.

    Speaker: Michelle Zhou, IBM Research



    Dr. Michelle Zhou is a research senior manager at IBM Research – Almaden, where she manages the User Systems and Experience Research (USER) group. Michelle received a Ph.D. in Computer Science from Columbia University. Her expertise is in the interdisciplinary areas of intelligent user interaction, smart visual analytics (2D/3D), and people-centric information management. She has published over 70 peer-reviewed, refereed articles, and filed over twenty-five patents in above areas. Michelle is an ACM Distinguished Scientist and is active in several research communities, including intelligent user interfaces (IUI), information visualization and visual analytics, and multimedia (MM), where she has co-organized/co-chaired conferences and workshops, and often serves on the technical program committees for key conferences in these areas. Currently she is on the editorial board of three ACM journals: ACM Transactions on Intelligent Systems and Technology (TIST), ACM Transactions on Interactive Intelligent Systems (TiiS), and ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP).