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

3. 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?

4. 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

5. Visual Analytics of Time-Oriented Data visualizing time-oriented data 2 interacting with time 3 analyzing time-oriented data automated analysis 4 characterizing time & time-oriented data modeling time modeling time-oriented data 1

6. Modelling time

8. Example: Granularity paradoxon

9. Modelling time-oriented data

10. Modelling data & time

11. Visual Analytics of Time-Oriented Data visualizing time-oriented data 2 interacting with time 3 analyzing time-oriented data automated analysis 4 characterizing time & time-oriented data modeling time modeling time-oriented data 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 visualizing time-oriented data 2 interacting with time 3 analyzing time-oriented data automated analysis 4 characterizing time & time-oriented data modeling time modeling time-oriented data 1

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

19. Interaction Levels 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 [Aigner; Presentation 2009]

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 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]

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

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

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

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

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

28. Visual Analytics of Time-Oriented Data visualizing time-oriented data 2 interacting with time 3 analyzing time-oriented data automated analysis 4 characterizing time & time-oriented data modeling time modeling time-oriented data 1

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

30. visualizing time-oriented data 2 interacting with time 3 analyzing time-oriented data automated analysis 4 characterizing time & time-oriented data modeling time modeling time-oriented data 1 Visual Analytics of Time-Oriented Data

31. 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]

32. Forthcoming Book 2011

33. Aigner, Miksch Schumann, Tominski, 2011 Visualization 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)