visualizing state transition graphs hannes pretorius visualization group, tu/e 17 october 2007...
TRANSCRIPT
Visualizing State Transition Graphs
Hannes PretoriusVisualization Group, TU/e
17 October [email protected]
www.win.tue.nl/~apretori/
Introduction
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’
Research question
“How can visualization be used to gaininsight into state transition graphs?”
Research question
“How can visualization be used to gaininsight into state transition graphs?”
• What is insight?– Symmetries, patterns…
• What about size?– System behavior is often complex
• Typical users?– Small number of expert users
Related work
Van Ham et al., TVCG, 2002.
Van Ham et al., TVCG, 2002.
Approach
Handle_posFront_wheel_po
sBack_wheel_po
sSeat_pos
= up= out= in= down
Handle_posFront_wheel_po
sBack_wheel_po
sSeat_pos
= down= in= out= up
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’
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 ai
• ai has domain Ai = {ai,1, …, ai,ki}
Projection
Pretorius and Van Wijk, IV, 2005.
Projection
• Multivariate data:– Select interesting subset– Show low-dimensional projection
Pretorius and Van Wijk, IV, 2005.
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
knowledgePretorius and Van Wijk, IV, 2005.
Clustering
Pretorius and Van Wijk, InfoVis, 2006.
All states
Handle_pos
Seat_pos
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
Pretorius and Van Wijk, InfoVis, 2006.
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)
Pretorius and Van Wijk, InfoVis, 2006.
Custom diagrams
Pretorius and Van Wijk, CG&A, 2007.Mathijssen and Pretorius, LNCS, 2007.
Pretorius and Van Wijk, CG&A, 2007.Mathijssen and Pretorius, LNCS, 2007.
Pretorius and Van Wijk, CG&A, 2007.Mathijssen and Pretorius, LNCS, 2007.
Custom diagrams
• Support diagramming in general way:– Edit diagrams– Link with attributes
• Capture conceptualization of problem
Pretorius and Van Wijk, CG&A, 2007.Mathijssen and Pretorius, LNCS, 2007.
Custom diagrams
• Support diagramming in general way:– Edit diagrams– Link with attributes
• Capture conceptualization of problem• Semantics clear and intuitive• Analysis and communication• Flexible Pretorius and Van Wijk, CG&A, 2007.
Mathijssen and Pretorius, LNCS, 2007.
Wafer stepper Paint factory Petri nets
Trace visualization
Submitted, PacificVis, 2008.
Time
Att
ribu
tes
1 k
1n
1
2
Time
Att
ribu
tes
1 k
1n
3
Submitted, PacificVis, 2008.
Trace visualization
• Traces:– Curb size and complexity– Users intuitively relate to time
Submitted, PacificVis, 2008.
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
Submitted, PacificVis, 2008.
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 flow
Questions
www.win.tue.nl/~apretori/
Projection (cont.)