The Week in Vis 10 (Mon, Nov 5 – Fri, Nov 9): Scale

by Mike Gleicher on November 2, 2018

Class Meetings
  • Mon, Nov 5 – Lecture:Too Much Stuff
  • Wed, Nov 7 – ICE:DC1 Critiques
  • Fri, Nov 9 – No Class
Week Deadlines

Last week we had an odd schedule – this week we’re back to normal. We talked about interaction.

This week, we’ll touch on one of the great challenges of visualization: how to deal with lots of data. What to do when you have “too much stuff” to show. (“Too Much Stuff” was a reference to an old blues song, but that was before the phrase took on a new meaning with the “minimalist” movement).

One of the “readings” (the T-SNE web page below) is a great example of what I was talking about last week with using interaction to help someone learn how a complicated model works.

The ICE this week will involve critiquing some DC1 assignments. (yes, you will get your grades back before then). We’ll mainly look at old ones. And in many cases, they will be good examples of challenges of having too much stuff.

And, of course, DC2 is due. Look for information how to sign up to give a demo (if you want to give a demo). As always, we have a pretty lenient late policy – but be aware, DC3 starts up immeditately!

If you’re curious DC3 is posted. But you probably want to finish DC2 first.

You may want to look at this week’s learning goals Learning Goals 10: Week 10 – Uncertainty and Modeling.

Readings (due Mon, Nov 5 – preferably before class)

This is a big and important topic, but rather than require a lot of reading, I’ll give you less – and hope that you’ll go beyond the minimum.

These 4 things are required. The Munzner chapters are fairly short, and the TSNE web page is light reading and fun to play with.

  1. Reduce Items and Dimensions (Chapter 13 from Munzner’s Visualization Analysis & Design) (Munzner-13-Reduce.pdf 0.4mb)
  2. Embed: Focus+Context (Chapter 14 from Munzner’s Visualization Analysis & Design) (Munzner-14-Embed.pdf 0.5mb)
  3. How to Use T-SNE Effectively – I wanted to give you a good foundation on dimensionality reduction. This isn’t it. But… it will make you appreciate why you need to be careful with dimensionality reduction (especially fancy kinds of it).
  4. Sarikaya, Gleicher and Szafir. Design Factors for Summary Visualization in Visual Analytics. (web) – This is a survey of different ways of doing summarization that appear in the visualization literature. There is a lot about how the survey was conducted, but the main thing for class is to see the different categories of summarization and how they interact.

Optional

These are both really good surveys. They were going to be required. Instead, consider them “strongly recommended”.

  • Ellis, Geoffrey, and Alan Dix. “A Taxonomy of Clutter Reduction for Information Visualisation.” IEEE Transactions on Visualization and Computer Graphics, 2007, 1216–23. (pdf) (doi)
  • Elmqvist, Niklas, and Jean-Daniel Fekete. “Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines.” IEEE Transactions on Visualization and Computer Graphics 16, no. 3 (2010): 439–54. (pdf) (doi)

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