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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. [Shneiderman, 1996]. 1-dimensional 2-dimensional 3-dimensional Temporal

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

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  1. 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)

  2. Data types [Shneiderman, 1996] • 1-dimensional • 2-dimensional • 3-dimensional • Temporal • Multi-dimensional • Tree • Network = 4D space “the world we are living in”

  3. Spatial + temporal dimensions • Every data element we measure is related and often only meaningful in context ofspace + time • Example: price of a hotelwhere?when?

  4. 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

  5. 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

  6. Modelling time

  7. Modelling time

  8. Example:Granularity paradoxon

  9. Modelling time-oriented data

  10. Modelling data & time

  11. 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

  12. Visualizing time • Time → Time (Animation) Time → Space • Visual variables:position, length, angle, slope, connection, thickness, ...

  13. Visualizing time-oriented data • specific techniques • + • concepts, frameworks

  14. Visualizing time-oriented data • specific techniques • + • concepts, frameworks

  15. Visualizing time-oriented data • specific techniques • + • concepts, frameworks

  16. Visualizing time-oriented data • specific techniques • + • concepts, frameworks

  17. 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

  18. Interaction facilitates active discourse with the data and visualization see think modify [Card et al., 1983]

  19. 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

  20. Taxonomies :: low-level interactions [Yi, Kang, Stasko 2007]

  21. Taxonomies ::dimensions, operators, & user tasks [Yi, Kang, Stasko 2007] Additional task taxonomies [McEachren 1995] [Andrienko & Andrienko 2006]

  22. 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

  23. data user task Users & Tasks • User-Centered Design representation & interaction expressiveness effectiveness appropriateness

  24. [VisuExplore project] Interacting with time • specific interaction techniques • + • task & interaction taxonomies

  25. [VisuExplore project] [VisuExplore project: measure tool] Interacting with time • specific interaction techniques • + • task & interaction taxonomies

  26. Interacting with time [Animated Scatterplot project] • specific interaction techniques • + • task & interaction taxonomies [CHI09 workshop, VisuExplore project]

  27. Interacting with time [CareCruiser project] • specific interaction techniques • + • task & interaction taxonomies [CHI09 workshop, VisuExplore project]

  28. 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

  29. Computational analysis of time-oriented data • temporal data-abstraction • statistics • temporal data-mining [MuTIny, DisCo project]

  30. 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

  31. 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]

  32. Forthcoming Book 2011

  33. Aigner, Miksch Schumann, Tominski, 2011Visualization of Time-Oriented Time

  34. 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]

  35. 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]

  36. 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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