Review visual methods for analyzing time oriented data
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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

Review: Visual Methods for Analyzing Time-Oriented Data

Authors: Wolfgang Aigner, Silvia Miksch, Wolfgang Mu¨ ller, Heidrun Schumann, and Christian Tominski


Introduction
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


Types of times
Types of Times

  • Linear Time vs Cyclic Time

  • Time Points vs Time Intervals

  • Ordered Time vs Branching Time vs Time with multiple perspectives


Linear time vs cyclic time
Linear Time vs Cyclic Time


Time points vs time intervals
Time Points vs Time Intervals

  • Time Wheel


Time points vs time intervals1
Time Points vs Time Intervals

  • PlanningLines


Ordertime
OrderTime

  • ThemeRiver


Branching time and time with multiple perspectives
Branching Time and Time with multiple Perspectives

  • Not many effective tools to visualize such data

  • Mostly ordered data visualization or univariate visualization - planninglines


Analyzing time oriented data
Analyzing Time Oriented Data

  • Temporal Data Abstraction

  • Principal Component Analysis

  • Clustering


Temporal data abstraction
Temporal Data Abstraction

  • VIE_VENT’s schema

  • Knowledge on data

  • Context Sensitive

  • Expectation guided

  • Quant to Qualitative


Temporal data abstraction1
Temporal Data Abstraction

  • The Spread Visualization - Oscillating Data


Principal component based analysis
Principal Component Based Analysis

  • Dimensionality reduction technique

  • Finding dimensions with maximum variance

  • Transforming original dataset to different domain


PCA

  • Themeriver


PCA

  • Bar chart visualization of one component


Clustering
Clustering

  • Calendar metophar




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