ampliation - Computer Science

Toward Interactive Visualization
for Deeply Digital Scholarship
Chris Weaver
School of Computer Science
University of Oklahoma
Visual analytics research is increasingly
focusing on deeply textured data
and structured information.
Analytics is poised to become the next
general-purpose methodological science.
Interactive visualization is the face of analytics.
Visualization helps us share ideas about data.
Interaction helps us express and explore them.
Collaborating with scientists and scholars
on realistic application of tool designs
in a wide variety of knowledge domains
is essential for methodological success.
It doesn’t matter whether data is
quantitative or qualitative,
simple or complex,
big or small.
Is it interesting or useful
for human understanding?
There are many ways to
visually encode data items,
visually represent data collections, and
interactively gesture to navigate and edit data
from multiple perspectives simultaneously.
Elucidating the possible and practical design space of interaction building blocks is the central theme of my visualization research.
How can interactive visualization support a complete digital workspace for science and scholarship?
Users
Whether recorded directly by people or indirectly using
machines, data captures our observations and
interpretations of the world around us.
Drag events and durations to shift them along a timeline.
Drag things on a map to specify location, direction, or region.
Developers
Draw and erase arrows to add and remove value co-occurrences.
Design
Press modifier keys to select, negate, or otherwise classify values.
Data
Transformation
Pipeline
Interaction
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Data
Edits
Movement
Highlighting
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Query
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Side-Effects
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We seek to expand the usefulness of interactive
visualization tools by making them into meaningful
graphical contexts for dynamically editing any data.
Display
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G Sele
How can people interact with data to express
their observations, interpretations, questions,
and reasoning in a more direct and natural form?
Pinch to associate uncertainty with a measurement or a set.
Open-ended extension and modification of data in context.
Populate new data sets by annotating values and associations.
Explore idiosyncratic models of data relationships through tagging.
Create, share, and assess competing hypotheses expressed as data.
Views
Interaction Mapping (interactions → data)
Visual Mapping (data → graphics)
raw interactions
keyboard, mouse, wheel,
multitouch, glove, wand, gaze...
gestures
keypress, click, drag,
roll, swipe, pinch, fixation...
data edits
select, annotate, collect, compose
+
graphical side-effects
cursor, rubberband, lasso, tooltip, menu...
What We’re Working On
ampliation
annotation as a part of open-ended interaction
physics
chemistry
a new language to question, observe, reason, conclude, share
www.math.nyu.edu/~crorres/Archimedes/Lever/leverBigCorners.gif
zoology
Ampliative
from Latin ampliare, "to enlarge"
used in logic to mean “extending” or
“adding to what is already known.”
In Norman law,
an “ampliation” was
a postponement of a sentence
to obtain further evidence.
Our software workbench
for creating visualizations
is called Improvise.
To see more, visit
www.cs.ou.edu/~weaver/improvise
urban planning
psychology
meteorology
knowledge networks
demographics
archives
economics
political science
history
A comprehensive survey of the forms of visual data
interaction in the research literature and in practice.
•
A new model of visualization interaction design that
significantly extends the useful reach of visualization
tools and facilitates their systematic and rapid
development and implementation.
•
A new model of open-ended, column-based data
organization that supports efficient transformation,
representation, manipulation, and extension of data
through interaction.
•
Understanding the data structures, transformations,
representations, and manipulations needed to
represent details of structured observations,
reasoning, and interpretation.
•
Understanding how gestures can be effectively
linked to visual encodings to support direct
manipulation of data values of different types.
•
Learning how gestures can be composed to ask
questions about data and make statements about
observed patterns by extending and refining data.
•
Generalizable patterns and guidelines, grounded in
evaluation, for designing visualizations that support
exploration, analysis, and interpretation tasks
through gesture composition.
The research presented here has been supported in part by
National Science Foundation Awards #1036331 and #1351055,
and by a grant from the Andrew W. Mellon Foundation.