A Matter of Time and Interactions: Interactively Exploring Time-Oriented Data

Silvia MikschVienna University of TechnologyInstitute of Software Technology and Interactive Systems (ISIS)

Data types

1-dimensional

2-dimensional

3-dimensional

Temporal

Multi-dimensional

Tree

Network

= 4D space“the world we are living in”

[Shneiderman, 1996]

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

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

visualizing time-oriented data 2interacting with time 3analyzing time-oriented data

automated analysis4

characterizing time & time-oriented data

modeling timemodeling time-oriented data

1

Modelling time

Modelling time

Example:Granularity paradoxon

Modelling time-oriented data

Modelling data & time

Visual Analytics of Time-Oriented Data

visualizing time-oriented data 2interacting with time 3analyzing time-oriented data

automated analysis4

characterizing time & time-oriented data

modeling timemodeling time-oriented data

1

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 2interacting with time 3analyzing time-oriented data

automated analysis4

characterizing time & time-oriented data

modeling timemodeling time-oriented data

1

Interaction facilitates active discourse with the data and visualization

see think

modify

[Card et al., 1983]

Interaction Levels

Physical LevelHow does the user physically interact?E.g., Mouse Wheel, Touch Screen Interaction Devices

Control LevelHow can it be carried out by the user?E.g., Move Scrollbar User Interface

Conceptual LevelWhat to be done?E.g., Scrolling / Navigating Task

[Aigner; Presentation 2009]

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

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

Based on 1) [Yi et al., 2007]

Users & Tasks

User-Centered Design

representation &

interaction

data

task user

expr

essi

vene

ss effectiveness

appropriateness

Interacting with time

specific interaction techniques+

task & interaction taxonomies

[VisuExplore project]

Interacting with time

specific interaction techniques+

task & interaction taxonomies

[VisuExplore project]

[VisuExplore project: measure tool]

Interacting with time

specific interaction techniques+

task & interaction taxonomies

[CHI09 workshop, VisuExplore project]

[Animated Scatterplot project]

Interacting with time

specific interaction techniques+

task & interaction taxonomies

[CHI09 workshop, VisuExplore project]

[CareCruiser project]

Visual Analytics of Time-Oriented Data

visualizing time-oriented data 2interacting with time 3analyzing time-oriented data

automated analysis4

characterizing time & time-oriented data

modeling timemodeling time-oriented data

1

Computational analysis of time-oriented data

temporal data-abstraction

statistics

temporal data-mining

[MuTIny, DisCo project]

visualizing time-oriented data 2interacting with time 3analyzing time-oriented data

automated analysis4

characterizing time & time-oriented data

modeling timemodeling time-oriented data

1

Visual Analytics of Time-Oriented Data

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

DataVariables: univariate vs. multivariateFrame of reference: abstract vs. spatial

TimeArrangement: linear vs. cyclicTime primitive: instant vs. interval

VisualizationMapping: static vs. dynamicDimensionality: 2D vs. 3D

[Aigner, Miksch Schumann, Tominski,

2011]

Compared: 75 methods

DataVariables: univariate vs. multivariateFrame of reference: abstract vs. spatial

TimeArrangement: linear vs. cyclicTime primitive: instant vs. interval

VisualizationMapping: static vs. dynamicDimensionality: 2D vs. 3D

[Aigner, Miksch Schumann, Tominski,

2011]

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)