21 August 2015

What is this?

Well. During my presentation at the big-data conference StampedeCon, I noted that I would have the references at the end of the presentation and that the slides would be published after the conference. And indeed, the slides include the references and the slides were published. w00t!


The reference slide is fairly hard to read, and not all of the links are working links in the slides. So here is an annotated set of the references.


I put the references in slide deck order below, since that will be the way you would naturally want to see the references. Well. Almost. First I'll give the link to the GitHub repository where the scripts that I used to create the charts I made for the presentation are.

Slides 5,6,9,10,11,13,16(partial),23,24,25 - Charts

These charts were created for the presentation using RStudio and the scripts located on GitHub in the repository below.

Slide 7 - Information Dense

It is hard to talk too much about Edward Tufte when talking about visualization. He is acknowledged as a leader in the field and the book here is often cited when discussing visualization. The chart of Napolean's march on Moscow in 1812 that I used is described in the book as “…may well be the best statistical graphic ever drawn”. High praise indeed.

  • Edward R. Tufte, 2001

    The Visual Display of Quantitative Information

Slide 8 - Hidden Insights

On this slide, I included a diagram from an article that introduces some of the interesting aspects of the new neural networking work that is being done. In this case it is an attempt to visualize what a neural network has “learned” during training. This is done by presenting noise as input and observing what the network produces - with the presumption that this will be what it “thinks” it has been trained to recognize. In this case a network that was trained to recognized dumbbells shows that it is recognizing dumbbells; but with arms attached! Something that would have been very difficult to determine without being able to visualize this.

I'll also note here that while there was no attribution requirement in the article at the time I accessed it. A notice was added since that the images (not including those derived from MIT images) are covered under the Creative Commons Attribution 4.0 International License.

Slide 13 - Brushing and Linking

The brushing and linking on this chart were indeed created using RStudio and the script is captured in the GitHub repository above. But the actual images were generated using the GGobi tool, an open source visualization tool funded (until about 2010) by the National Science Foundation. This tool has an ancillary R package that allows R data frames to be used as input data sets for the visualizations. That is what I used to show the concept of brushing and linking using one of the R data sets that the GGobi download included.

Slide 14 - Response Times

On this slide, I included a screen capture of my own visualization. The data was served using the open source imMens big data visualization tool which is then viewed on a browser. This view matches those that are included in the cited article. The article itself presents the results of research conducted by a team at the University of Washington Interactive Data Lab. The lab was moved from the original Stanford Visualization Lab in 2013, which had produced such visualization tools as D3.js and Protovis as well as the original Polaris which was commercialized as Tableau.

The article cited discusses the results that were published in the I.E.E.E. Transactions on Visualization and Computer Graphics (Proc InfoVis) in 2014. The imMens tool is available from GitHub at the GitHub link below as well.

Slide 29 - Covariance Aggregation

In this slide I include the formula that is presented in the Pébay paper for covariance. This is an efficient mechanism to implement sliding windows and other aggregation strategies for large sets of data where descriptive statistics are being generated. The Apache HIVE project includes a Java implementation of the moment and covariance generating formulas. I have implemented a C++ version of these formulas as well and might put that up on GitHub if there is any interest.

At this point, all of the references from the presentation have been included. Hopefully you will find this helpful in exploring some of the more interesting aspects of large data visualizations.

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