Visual analytics techniques for time series data

1. Visual Analytics Techniques for
Time-Oriented Data
Kapil Jaisinghani @ Citrix

2. Agenda
• Modeling Time
• Characterizing Data
• User Tasks
• Visual Representation Concepts
• Visualization Techniques
• Time Series Visualization
• Event / Event Sequence Visualization
• Interactions
• Visualizing Large Datasets

3. We`re Surrounded By Time Series Data
• Operational data: Monitoring data, log events, ...
• Data Warehouse: Dimension time
• Measured Me: Activity tracking, ECG, ...
• Sensor telemetry: Sensor data, ...
• Financial data: Stock charts, ...
• Climate data: Temperature, ...
• Web tracking: Clickstreams, ...

4. Visualization Aspects
what is shown?
Data abstraction
why is the user looking at it?
Task abstraction
how is it shown?
Visual encoding and interaction

5. Modeling Time
Source: Visualization of Time-Oriented Data
By Wolfgang Aigner , Silvia Miksch , Heidrun Schumann, Christian Tominski

6. Scale – Ordinal vs Discrete vs Continuous
ordinal discrete continuous

7. Scope - Point-based vs. Interval-based
Point Based Interval Based

8. Arrangement - Linear vs. Cyclic
Linear
Cyclic

9. Viewpoint: Ordered vs. Branching vs. Multiple Perspectives
Branching
Multiple Perspective
Ordered
Totally vs Partial Ordered

10. Time Granularity

11. Time Primitives: Instant vs. Interval vs. Span
Instant Interval Span

12. Relationship between Instant, Interval
Instant Interval Instant + Interval

13. Determinacy - Determinate vs. Indeterminate

14. Characterizing Data
Source: Visualization of Time-Oriented Data
By Wolfgang Aigner , Silvia Miksch , Heidrun Schumann, Christian Tominski

15. Characterizing Data
Scale
Frame of reference: Abstract vs. Spatial
Kind of data: Events vs. States
Number of variables: Univariate vs. Multivariate
Power Turned ON Power Turned OFF Power Turned ON
Charging Not Charging
Z
Z
Z
Z
Z
Single Series Multiple Series Time Series Set

16. User Tasks
Lookup Comparison
Relation Seeking Pattern

17. User Tasks
Lookup
> Was a measurement made in June, 1960?
> What was the price of Google stocks on January 14?
Search for time point, interval, duration given data value
Direct:
Inverse:
Search for a data element given time
> On which day(s) was the lowest stock price for Amazon in 2010?
> When did the Olympic Games in Vancouver start?
> How long was the processing time for dataset A?
Comparison
> Compare the stock prices of Sun and Oracle on January 14
Compare time point, interval, duration given data value
Direct:
Inverse:
Compare data elements given time
> Did the price of Apple stocks reach $200 before or after January 14?
Pattern
Search for frequency of data elements
Search for rate of change over time
> How often was Jane sick last year?
> How did the price of gasoline vary in the last year?
Compare Patterns given time
Compare Patterns given Data Value
> Compare the behavior of the stock price of HP in January and June
> How is a decreasing trend of Dell stocks related to the period of
summer vacation?
Relation Seeking
Search for time sequence of diff data elements
> Did the explosion happen before or after the car accident?
> What was the price of Google stocks on January 14?
> Is Jill’s birthday on Easter Monday this year?
Search for correlation in data
> Find two contiguous months with opposite trends in the stock price of Lenovo.
> Is the behavior of Nokia stocks influencing the behavior of Motorola stocks?

18. Visual Representation
Marks
Encode
Linear
Cyclic

19. Visual Representation for Time

20. Visual Representation for Time

21. Basic Visualization Techniques

22. Visualization Techniques
Line Chart

24. Nobels, no degrees
This Visualization explores Nobel Prizes and
graduate qualifications from 1901 -1912, by
analyzing the age of the recipients, avg age
evolution, graduation grades, university
affiliations and principal hometowns

26. Visualization Techniques
Point Chart
Bar, Spike Chart

28. Visualization Techniques
Sparkline
Normal Range
SparkClouds

29. Visualization Techniques
Stacked Area Chart 100% Stacked Area Chart

31. Cycle Plot
Visualization Techniques

32. Small Multiples
Design
• Small multiples use the same basic graphic or chart to display
difference slices of a data set.
• Placement of the small multiples charts should reflect some
logical order. (Ex: Time, Geography)
• Small multiples should share the same measures, scales, size,
and shape.
• Simplicity of the chart is critical. Users should be able to
process information across many of these charts.
Scaling with Visualization Design
• Interaction: Brushing, Focus + Context Navigation
• Data Reduction: Filter , Search
Algorithmic Approach
• Clustering & Aggregating records
• Choosing Dimensions
Team-by-team Popularity
Small Multiples

33. Silhouette Graph, Circular Silhouette Graph
Visualization Techniques

34. Advanced Visualization Techniques

35. Heatmap
Visualization Techniques

36. Circle View – Single Variable, Multiple Time Granularities
Visualization Techniques
Circle View – Multiple Variable, Single Time Granularity

37. Visualization Techniques
Circle View – Multiple Variables, Multiple Time Granularities

38. Visualization Techniques
Circle View – Multi Level Hierarchy Spiral View – Multi Level Hierarchy

39. Visualization Techniques
Linear vs Cyclic

40. Visualization Techniques – Horizon Graph
Single Time Series Set of Time Series

41. Visualization Techniques – Kite Diagrams
Single Time Series Set of Time Series
Duplicate & flip around the horizontal axis
Fill the color

42. ChronoLense / Kronominer Lens Transformation
Kronominer – Radial Layout

43. Interactions

44. 4
4
Overview First, Zoom And Filter, Details On Demand
Influential Mantra from Shneiderman
1. Overview: Gain an overview of the entire
collection
2. Zoom : Zoom in on items of interest
3. Filter: Filter out uninteresting items
4. Details-on-demand: Select an item or
group and get details when needed
5. Relate: View relationships among items
6. History: Keep a history of actions to support
undo, replay, and progressive refinement
7. Extract: Allow extraction of sub-collections
and of the query parameters
Other Analytical Tasks

45. Overview + Detail
Panning a line chart Panning a Gantt chart

46. Overview + Detail
Browsing Multiple Views

47. Semantic zooming
• Visual Encoding Change
• Colored Box
• Sparkline
• Simple Line Chart
• Full Chart: Axes And Tickmarks
System: LiveRAC

48. Animation
Motion Chart – Animated Scatter Plot

49. Brushing & Linking
• See how regions
contiguous in one view are
distributed within another
• Powerful and pervasive
interaction idiom
• Encoding: different
• Multiform
• Data: all shared

50. Thank You