Visualizing State Transition Graphs

Hannes PretoriusVisualization Group, TU/e

17 October 2007a.j.pretorius@tue.nl

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.)