Visualizing State Transition Graphs
Hannes PretoriusVisualization Group, TU/e
17 October [email protected]
www.win.tue.nl/~apretori/Video.edhole.com
State transition graph
Graph G = (V, E) where:• Node s in V is a possible system state• Directed edge t = (s, s’ ) in E is a transition
from source state s to target state s’
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Research question
“ Ho w can visualizatio n be use d to g aininsight into state transitio n g raphs? ”
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Research question
“ Ho w can visualizatio n be use d to g aininsight into state transitio n g raphs? ”
• What is insight?– Symmetries, patterns…
• What about size?– System behavior is often complex
• Typical users?– Small number of expert users
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Handle_posFront_wheel_posBack_wheel_pos
Seat_pos
= up= out= in= down
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Handle_posFront_wheel_posBack_wheel_pos
Seat_pos
= down= in= out= up
Video.edhole.com
State transition graph
Graph G = (V, E) where:• Node s in V is a possible system state• Directed edge t = (s, s’ ) in E is a transition
from source state s to target state s’
Video.edhole.com
State transition graph
Graph G = (V, E) where:• Node s in V is a possible system state• Directed edge t = (s, s’ ) in E is a transition
from source state s to target state s’Every node s in V has:• n associated attributes a i
• a i has domain Ai = {a i, 1 , … , a i, ki}
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Projection
• Multivariate data:– Select interesting subset– Show low-dimensional projection
Pre to rius and Van Wijk, IV, 2005 .Video.edhole.com
Projection
• Multivariate data:– Select interesting subset– Show low-dimensional projection
• Suggestive behavioral patterns• Meaning of positions projected to not clear• Select subset based on domain knowledge
Pre to rius and Van Wijk, IV, 2005 .Video.edhole.com
Clustering
• Choose subsets based on domain knowledge• Position clusters linearly• Show additional information on top of this:
– Clustering hierarchy– Arcs representing transitions– Bar tree representing size of clusters
Pre to rius and Van Wijk, Info Vis, 20 0 6 .Video.edhole.com
Clustering• Reduce complexity
– Location has meaning• Patterns:
– Attribute values– Behavior– Cluster sizes
• Different types of analysis:– Explorative (e.g. different perspectives)– Specific (e.g. deadlock analysis)
Pre to rius and Van Wijk, Info Vis, 20 0 6 .Video.edhole.com
Custom diagrams
Pre to rius and Van Wijk, CG& A, 200 7 .Mathijsse n and Pre to rius, LNCS, 200 7 .
Video.edhole.com
Pre to rius and Van Wijk, CG& A, 200 7 .Mathijsse n and Pre to rius, LNCS, 200 7 .
Video.edhole.com
Pre to rius and Van Wijk, CG& A, 200 7 .Mathijsse n and Pre to rius, LNCS, 200 7 .
Video.edhole.com
Custom diagrams
• Support diagramming in general way:– Edit diagrams– Link with attributes
• Capture conceptualization of problem
Pre to rius and Van Wijk, CG& A, 200 7 .Mathijsse n and Pre to rius, LNCS, 200 7 .
Video.edhole.com
Custom diagrams
• Support diagramming in general way:– Edit diagrams– Link with attributes
• Capture conceptualization of problem• Semantics clear and intuitive• Analysis and communication• Flexible
Pre to rius and Van Wijk, CG& A, 200 7 .Mathijsse n and Pre to rius, LNCS, 200 7 .
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Wafer stepper Paint factory Petri nets
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Trace visualization
Submitte d, PacificVis, 20 0 8 .Video.edhole.com
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Time
Att
ribu
tes
1 k
1n
1
2
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Time
Att
ribu
tes
1 k
1n
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3
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Submitte d, PacificVis, 20 0 8 .Video.edhole.com
Trace visualization
• Traces:– Curb size and complexity– Users intuitively relate to time
Submitte d, PacificVis, 20 0 8 .Video.edhole.com
Trace visualization
• Traces:– Curb size and complexity– Users intuitively relate to time
• Three views:1. Diagram: easier to interpret2. Time series: general trends3. Transition graph: generalized behavior
Submitte d, PacificVis, 20 0 8 .Video.edhole.com
Conclusion
• Visualization of state transition graphs• Prototyping• Focus on state attributes
– Clear semantics• Explorative analysis:
– E.g. different perspectives• Focused analysis:
– E.g. deadlock, steam flowVideo.edhole.com
Questions
www.win.tue.nl/~apretori/
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Projection (cont.)
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