Big Data Visualization, visual analytics, and Virtual Reality


Tommy Dang

Assistant Professor

Department of Computer Science

Texas Tech University



Huyen Nguyen, PhD student

Outline


  • Chemical Simulations
  • Geospatial visualizations
  • Multivariate data analysis
  • Images analysis
  • Matrix visualizations
  • Networks and structures
    • http://www.myweb.ttu.edu/tnhondan/

    Chemical Simulations

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    Link to the Connected Chemistry application

    Geospatial visualizations

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    Geospatial-temporal visualizations
    Visualizing the distribution of Lyme diseases in 3D reported in the U.S. in 2005, 2006, and 2007 by the CDC. One might argue that a county-level choropleth map would be a suitable presentation. Nevertheless, it would be hard to argue that a county-level choropleth map using hue or brightness could reveal the threefold increase in Lyme disease cases centered around New England vs. the Midwest.

    Outline

  • Chemical Simulations
  • Geospatial visualizations
  • Multivariate data analysis
  • Images analysis
  • Matrix visualizations
  • Networks and structures

    • Scatterplot


    Visual features


  • Scagnostics (Scatterplot Diagnostics)
  • Scagnostics help us to characterize 2D scatterplots
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    Scagnostics are computed on on three geometric graphs


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    A Stringy shape is a skinny shape with no branches

    Computing Convex: The ratio of the area of the alpha hull and the convex hull

    Example: Stringy Scagnostics


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    TimeSeer demo

    The US Employment data comprise monthly employment rates of various economy factors for 50 states over 22 years from 1990 to 2011.

            The data are retrieved from Bureau of Labor Statistics

    Transforming Scagnostics


    Annual NYC subway ridership from 1904-2006: http://davis.wpi.edu/xmdv/datasets/subway.html

    Transforming Scagnostics



    Tuan Dang and Leland Wilkinson. Transforming Scagnostics to Reveal Hidden Features. IEEE Transactions on Visualization and Computer Graphics 20(12), presented at VAST 2014
    Musk dataset from UCI: https://archive.ics.uci.edu/ml/datasets/Musk+(Version+2)

    Choice of transformation



    • The classical statistical transformations arose out of experiences applying models based on theoretical distributions to real data

    • ransformations we choose ought to cover the full range of negative to positive skewness as well as mixtures of distributions that are relatively symmetric

    Choice of transformation

    Example: Monotonic

    Rural population vs. Urban population from the World Bank data: http://data.worldbank.org/indicator

    Case study: Monotonic




    We inspect 100 variables in Economy and Growth from the World Bank data: http://data.worldbank.org/indicator

    Case study: Monotonic


    We inspect 100 variables in Economy and Growth from the World Bank data: http://data.worldbank.org/indicator

    Using Scagnostics for classification


    • Leland Wilkinson, Anushka Anand, and Tuan Dang. CHIRP: A new classifier based on Composite Hypercubes on Iterated Random Projections. Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2011

    • Leland Wilkinson, Anushka Anand, and Tuan Dang. Substantial improvements in the set-covering projection classifier CHIRP. Journal ACM Transactions on Knowledge Discovery from Data, TKDD 2012


      • CHIRP is maintained on Weka: http://weka.sourceforge.net/packageMetaData/CHIRP/index.html

    Outline


  • Chemical Simulations
  • Geospatial visualizations
  • Multivariate data analysis
  • Images analysis
  • Matrix visualizations
  • Networks and structures

    • Testing dataset: Wang

    Testing dataset: ZuBuD

    Comparison of different Image Retrieval systems

    Demo: 1,000 images in the Wang collection

    Demo on SETI plots

    Examples of SETI Waterfall Plots

    More information on Waterfall_plot: http://setiquest.org/wiki/index.php/

    Outline


  • Chemical Simulations
  • Geospatial visualizations
  • Multivariate data analysis
  • Images analysis
  • Matrix visualizations
  • Networks and structures

    • Pathway Matrix (BioVis 2015)

    Provenance Matrix (VOILA 2015)

    Reaction Flow (BioVis 2015)

    Networks and structures

    User study of Tree layouts

    Visualizing dynamic networks

    TimeArcs github repository: http://github.com/CreativeCodingLab/TimeArcs

    TimeArcs

    http://myweb.ttu.edu/tnhondan/iDVL/index.php

    Questions?

    Email: Tommy.Dang@ttu.edu

    This work was supported by NSF/DHS grant DMS-FODAVA-0808860 and DARPA Big Mechanism Program under ARO contract WF911NF-14-1-0395

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