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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

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a matter of time and interactions interactively exploring time oriented data

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
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
Spatial + temporal dimensions
  • Every data element we measure is related and often only meaningful in context ofspace + time
  • Example: price of a hotelwhere?when?
differences between space and time
Differences between space and time
  • Space can be traversed “arbitrarily”we can move back to where we came from
  • Time is unidirectionalwe can’t go back or forward in time
  • Humans have senses for perceiving spacevisually, touch
  • Humans don’t have senses for perceiving time
visual analytics of time oriented data

characterizingtime & time-oriented data

  • modeling time
  • modeling time-oriented data

visualizingtime-oriented data

2

interactingwith time

3

analyzingtime-oriented data

  • automated analysis

4

1

Visual Analytics of Time-Oriented Data
visual analytics of time oriented data1
Visual Analytics of Time-Oriented Data

characterizingtime & time-oriented data

  • modeling time
  • modeling time-oriented data

visualizingtime-oriented data

2

interactingwith time

3

analyzingtime-oriented data

  • automated analysis

4

1

visualizing time
Visualizing time
  • Time → Time (Animation) Time → Space
  • Visual variables:position, length, angle, slope, connection, thickness, ...
visualizing time oriented data
Visualizing time-oriented data
  • specific techniques
  • +
  • concepts, frameworks
visualizing time oriented data1
Visualizing time-oriented data
  • specific techniques
  • +
  • concepts, frameworks
visualizing time oriented data2
Visualizing time-oriented data
  • specific techniques
  • +
  • concepts, frameworks
visualizing time oriented data3
Visualizing time-oriented data
  • specific techniques
  • +
  • concepts, frameworks
visual analytics of time oriented data2
Visual Analytics of Time-Oriented Data

characterizingtime & time-oriented data

  • modeling time
  • modeling time-oriented data

visualizingtime-oriented data

2

interactingwith time

3

analyzingtime-oriented data

  • automated analysis

4

1

interaction facilitates active discourse with the data and visualization
Interaction facilitates active discourse with the data and visualization

see

think

modify

[Card et al., 1983]

interaction levels
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 dimensions operators user tasks
Taxonomies ::dimensions, operators, & user tasks

[Yi, Kang, Stasko 2007]

Additional task taxonomies

[McEachren 1995]

[Andrienko & Andrienko 2006]

interaction user intents
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

data

user

task

Users & Tasks
  • User-Centered Design

representation & interaction

expressiveness

effectiveness

appropriateness

interacting with time

[VisuExplore project]

Interacting with time
  • specific interaction techniques
  • +
  • task & interaction taxonomies
interacting with time1

[VisuExplore project]

[VisuExplore project: measure tool]

Interacting with time
  • specific interaction techniques
  • +
  • task & interaction taxonomies
interacting with time2
Interacting with time

[Animated Scatterplot project]

  • specific interaction techniques
  • +
  • task & interaction taxonomies

[CHI09 workshop, VisuExplore project]

interacting with time3
Interacting with time

[CareCruiser project]

  • specific interaction techniques
  • +
  • task & interaction taxonomies

[CHI09 workshop, VisuExplore project]

visual analytics of time oriented data3
Visual Analytics of Time-Oriented Data

characterizingtime & time-oriented data

  • modeling time
  • modeling time-oriented data

visualizingtime-oriented data

2

interactingwith time

3

analyzingtime-oriented data

  • automated analysis

4

1

computational analysis of time oriented data
Computational analysis of time-oriented data
  • temporal data-abstraction
  • statistics
  • temporal data-mining

[MuTIny,

DisCo project]

visual analytics of time oriented data4

characterizingtime & time-oriented data

  • modeling time
  • modeling time-oriented data

visualizingtime-oriented data

2

interactingwith time

3

analyzingtime-oriented data

  • automated analysis

4

1

Visual Analytics of Time-Oriented Data
slide31

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]

slide34

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]

slide35

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
Thanks to
  • Wolfgang Aigner (Danube Universty Krems, VUT)
  • Alessio Bertone (Danube Universty Krems)
  • Tim Lammarsch (Danube Universty Krems, VUT)
  • Alexander Rind (Danube Universty Krems)
  • Thomas Turic (Danube Universty Krems)
  • Heidrun Schumann (University of Rostock)
  • Christian Tominski (University of Rostock)
  • Bilal Alsallakh (CVAST, Vienna University of Technology)
  • Theresia Gschwandtner (CVAST, Vienna University of Technology)
  • Klaus Hinum (Vienna University of Technology)
  • Katharina Kaiser (CVAST, Vienna University of Technology)
  • Margit Pohl (CVAST, Vienna University of Technology)
  • Markus Rester (Vienna University of Technology)
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