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Review: Visual Methods for Analyzing Time-Oriented Data

Review: Visual Methods for Analyzing Time-Oriented Data. Authors: Wolfgang Aigner , Silvia Miksch , Wolfgang Mu¨ ller , Heidrun Schumann, and Christian Tominski. Introduction. This paper presents visualization techniques for various types of temporal data.

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Review: Visual Methods for Analyzing Time-Oriented Data

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  1. Review: Visual Methods for Analyzing Time-Oriented Data Authors: Wolfgang Aigner, Silvia Miksch, Wolfgang Mu¨ ller, Heidrun Schumann, and Christian Tominski

  2. Introduction • This paper presents visualization techniques for various types of temporal data. • Large amount of data – requires human analysis and decision making • Aspects of visualizing temporal data • Visualization, analysis and user • Time characteristics must be considered while visualizing temporal data • Appropriate parameterization and interaction

  3. Types of Times • Linear Time vs Cyclic Time • Time Points vs Time Intervals • Ordered Time vs Branching Time vs Time with multiple perspectives

  4. Linear Time vs Cyclic Time

  5. Time Points vs Time Intervals • Time Wheel

  6. Time Points vs Time Intervals • PlanningLines

  7. OrderTime • ThemeRiver

  8. Branching Time and Time with multiple Perspectives • Not many effective tools to visualize such data • Mostly ordered data visualization or univariate visualization - planninglines

  9. Analyzing Time Oriented Data • Temporal Data Abstraction • Principal Component Analysis • Clustering

  10. Temporal Data Abstraction • VIE_VENT’s schema • Knowledge on data • Context Sensitive • Expectation guided • Quant to Qualitative

  11. Temporal Data Abstraction • The Spread Visualization - Oscillating Data

  12. Principal Component Based Analysis • Dimensionality reduction technique • Finding dimensions with maximum variance • Transforming original dataset to different domain

  13. PCA • Themeriver

  14. PCA • Bar chart visualization of one component

  15. Clustering • Calendar metophar

  16. Clustering

  17. User centered analysis via Events

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