Visualization is great way to understand data, but it breaks down when the data gets big. Simply plotting everything to the screen won’t work, because there isn’t enough screen real estate, interactions slow to a crawl, and human working memory isn’t up to the task anyway. Big data requires specific interaction techniques for visual exploration, such as filtering, summarization, and context. We’ll go over some basic principles, and I’ll show examples of recent systems, including our work on the Overview Project, a system for visual exploration of huge unstructured document sets.

1. interfaces for big data visualization
jonathan stray
overview project
associated press

2. Ben Fry,
Computational Information Design

7. visualization extends perception

10. “Visualization allows people to offload
cognition to the perceptual system, using
carefully designed images as a form of
external memory.
The human visual system is a very high-
bandwidth channel to the brain, Tamara Munzner
with a
significant amount of processing occurring in
parallel and at the pre-conscious level.”

15. a visualization
turns something you want to find
into something you can see

16. clusters outliers
extents correlations

17. work with human perception

18. work with human senses
work with human memory
work with human cognition

19. you have never seen a violin.
perception involves interactively
directing narrow attention

20. don’t show it all.
(you can’t anyway)

21. filter

25. summarize

30. categories =
“distance metric” +
clustering algorithm
d(x, y) ≥ 0
d(x, x) = 0
d(x, y) = d(y, x)
d(x, z) ≤ d(x, y) + d(y, z)
k-means
single link
hierachical

32. annotate

33. snapshots tags
notes history

34. demo
the overview project
overview.ap.org

35. the world does not spring
magically into our heads.
neither does big data.
Interface extends perception. thanks
@jonathanstray