A Matter of Time and Interactions:
Interactively Exploring Time-Oriented Data
Silvia Miksch
Vienna University of Technology
Institute of Software Technology and Interactive Systems (ISIS)
Data types
[Shneiderman, 1996]
1-dimensional
2-dimensional
3-dimensional
Temporal
Multi-dimensional
Tree
Network
= 4D space
“the world we are living in”
Spatial + temporal dimensions
Every data element we measure is related and often only
meaningful in context of
space + time
Example: price of a hotel
where?
when?
Differences between space and time
Space can be traversed “arbitrarily”
we can move back to where we came from
Time is unidirectional
we can’t go back or forward in time
Humans have senses for perceiving space
visually, touch
Humans don’t have senses for perceiving time
Visual Analytics of
Time-Oriented Data
visualizing
time-oriented data
characterizing
time & time-oriented data
modeling time
modeling time-oriented data
interacting
with time
analyzing
time-oriented data
1
automated analysis
2
3
4
Modelling time
Modelling time
Example:
Granularity paradoxon
Modelling time-oriented data
Modelling data & time
Visual Analytics of
Time-Oriented Data
visualizing
time-oriented data
characterizing
time & time-oriented data
modeling time
modeling time-oriented data
interacting
with time
analyzing
time-oriented data
1
automated analysis
2
3
4
Visualizing time
Time → Time (Animation)
Time → Space
Visual variables:
position, length, angle, slope, connection, thickness, ...
Visualizing time-oriented data
specific techniques
+
concepts, frameworks
Visualizing time-oriented data
specific techniques
+
concepts, frameworks
Visualizing time-oriented data
specific techniques
+
concepts, frameworks
Visualizing time-oriented data
specific techniques
+
concepts, frameworks
Visual Analytics of
Time-Oriented Data
visualizing
time-oriented data
characterizing
time & time-oriented data
modeling time
modeling time-oriented data
interacting
with time
analyzing
time-oriented data
1
automated analysis
2
3
4
Interaction facilitates active discourse with the
data and visualization
see
think
modify
[Card et al., 1983]
Interaction Levels
[Aigner; Presentation 2009]
Physical Level
How does the user physically interact?
E.g., Mouse Wheel, Touch Screen
 Interaction Devices
Control Level
How can it be carried out by the user?
E.g., Move Scrollbar
 User Interface
Conceptual Level
What to be done?
E.g., Scrolling / Navigating
 Task
Taxonomies :: low-level interactions
[Yi, Kang, Stasko 2007]
Taxonomies :: dimensions, operators, & user tasks
[Yi, Kang, Stasko 2007]
Additional task taxonomies
[McEachren 1995]
[Andrienko & Andrienko 2006]
Interaction :: user intents
Based on 1) [Yi et al., 2007]
Select:
mark something as interesting
Explore:
show me something else
Reconfigure: show me a different arrangement
Encode: show me a different representation
Abstract/Elaborate: show me more or less detail
Filter:
show me something conditionally
Connect:
show me related items
Undo/Redo: Let me go to where I have been already
Change configuration: Let me adjust the interface
Users & Tasks
User-Centered Design
data
representation
&
interaction
task
appropriateness
user
Interacting with time
[VisuExplore project]
specific interaction techniques
+
task & interaction taxonomies
Interacting with time
[VisuExplore project]
specific interaction techniques
+
task & interaction taxonomies
[VisuExplore project: measure tool]
Interacting with time
[Animated Scatterplot project]
specific interaction techniques
+
task & interaction taxonomies
[CHI09 workshop, VisuExplore project]
Interacting with time
[CareCruiser project]
specific interaction techniques
+
task & interaction taxonomies
[CHI09 workshop, VisuExplore project]
Visual Analytics of
Time-Oriented Data
visualizing
time-oriented data
characterizing
time & time-oriented data
modeling time
modeling time-oriented data
interacting
with time
analyzing
time-oriented data
1
automated analysis
2
3
4
Computational analysis of time-oriented
data
temporal data-abstraction
statistics
temporal data-mining
[MuTIny,
DisCo project]
Visual Analytics of
Time-Oriented Data
visualizing
time-oriented data
characterizing
time & time-oriented data
modeling time
modeling time-oriented data
interacting
with time
analyzing
time-oriented data
1
automated analysis
2
3
4
1. What has to be presented?
– Time and data!
2. Why has it to be presented?
– User tasks!
3. How is it presented?
– Visual representation!
[Aigner, Miksch
Schumann, Tominski,
2011]
Forthcoming Book 2011
Aigner, Miksch Schumann, Tominski, 2011
Visualization of Time-Oriented Time
Compared: 75 methods
Data
Variables: univariate vs. multivariate
Frame of reference: abstract vs. spatial
Time
Arrangement: linear vs. cyclic
Time primitive: instant vs. interval
Visualization
Mapping: static vs. dynamic
Dimensionality: 2D vs. 3D
[Aigner, Miksch
Schumann, Tominski,
2011]
Compared: 75 methods
Data
Variables: univariate vs. multivariate
Frame of reference: abstract vs. spatial
Time
Arrangement: linear vs. cyclic
Time primitive: instant vs. interval
Visualization
Mapping: static vs. dynamic
Dimensionality: 2D vs. 3D
[Aigner, Miksch
Schumann, Tominski,
2011]
Thanks to
Wolfgang Aigner
Alessio Bertone
Tim Lammarsch
Alexander Rind
Thomas Turic
(Danube Universty Krems, VUT)
(Danube Universty Krems)
(Danube Universty Krems, VUT)
(Danube Universty Krems)
(Danube Universty Krems)
Heidrun Schumann
Christian Tominski
(University of Rostock)
(University of Rostock)
Bilal Alsallakh
Theresia Gschwandtner
Klaus Hinum
Katharina Kaiser
Margit Pohl
Markus Rester
(CVAST, Vienna University of Technology)
(CVAST, Vienna University of Technology)
(Vienna University of Technology)
(CVAST, Vienna University of Technology)
(CVAST, Vienna University of Technology)
(Vienna University of Technology)
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