Causality Visualization Using Animated Growing Polygons

1. Causality Visualization Using Animated Growing Polygons Niklas Elmqvist (elm@cs.chalmers.se) Philippas Tsigas (tsigas@cs.chalmers.se) IEEE 2003 Symposium on Information VisualizationOctober 19th-21st, Seattle, Washington, USA.

2. Outline Introduction and Motivating Example Related Work The Growing Polygons Technique User Study & Results Conclusions & Future Work Roadmap Causality Visualization Using Animated Growing Polygons

3. Introduction ”Since we believe that we know a thing only when we can say why it is as it is—which in fact means grasping its primary causes (aitia)—plainly we must try to achieve this [...] so that we may know what their principles are and may refer to these principles in order to explain everything into which we inquire.” -- Aristotle, Physics II.3. • The concepts of cause and effect are pervasive in human thinking • Causality is a very important reasoning tool in both science as well as everyday life • Causal relations can be very complex • This talk describes effective ways of visualizing causality Causality Visualization Using Animated Growing Polygons

4. Example: Citations • Let’s study the chain of citations in a collection of scientific papers • A citation can be seen as an influence • Citation graphs can be very large • Studying these chains can give the following information • How are authors are influenced by other authors? • How are ideas propagated in a scientific community? Causality Visualization Using Animated Growing Polygons

5. Author A Author B Author C Example: Citations (2) time 1999 2000 2001 2002 2003 Causality Visualization Using Animated Growing Polygons

6. Causality Visualization • Formally, we are looking to visualize systems of causal relations • Def: The causal relation is a relation that connects two elements (events) x and y as x  y iff x influences y. • Sets of events are called processes P1,..., PN • Internal events are sequential and causally related • External events interconnect processes through messages • Effective visualization is a difficult problem • Traditional visualization: Hasse diagrams Causality Visualization Using Animated Growing Polygons

7. Applications • General information flow problems • Rumor spreading • Citation networks • Software visualization • Learning, designing, or debugging distributed programs and algorithms Causality Visualization Using Animated Growing Polygons

8. Distributed system with n=20 processes and 60 system events Difficult to comprehend Intersecting and coinciding message arrows Fine granularity The user must manually maintain ”the context” of the relations Users may have to backtrace every single message Vital information is scattered Related Work: Hasse Diagrams Causality Visualization Using Animated Growing Polygons

9. Related Work: Growing Squares • Our earlier attempt at improving causality visualization • Processes represented by animated 2D squares • Presented at SoftVis 2003 • More efficient than Hasse diagrams but: • Similar colors reduce scalability • Influences are ”mixed up” • No absolute timing information Causality Visualization Using Animated Growing Polygons

10. Growing Polygons • Refinement of Growing Squares • Idea: Represent each process by an n-sided polygon (process polygon) • Assign each process a unique color • Assign each process a unique triangular sector in the polygons Causality Visualization Using Animated Growing Polygons

11. Growing Polygons (2) • Process polygons are laid out on a large n-sided layout polygon • Each polygon grows as time progresses • Animated timeline • Messages are shown as arrows travelling from one process to another at specific points in time • Messages carry influences (see next slide) Simplified GP diagram Causality Visualization Using Animated Growing Polygons

12. Growing Polygons: Influences • Messages carry influences (causal relations) • Source color is transferred to the destination • Causal relations are also transitive • Transitive ”colors” are also carried across • Both color and orientation used for separating processes Causality Visualization Using Animated Growing Polygons

13. Growing Polygons: Example (1) Causality Visualization Using Animated Growing Polygons

14. Growing Polygons: Example (2) Causality Visualization Using Animated Growing Polygons

15. Growing Polygons: Example (3) Causality Visualization Using Animated Growing Polygons

16. Growing Polygons: Example (4) Causality Visualization Using Animated Growing Polygons

17. Growing Polygons: Example (5) Causality Visualization Using Animated Growing Polygons

18. Hasse vs Growing Polygons Causality Visualization Using Animated Growing Polygons

19. User Study • A formal user study comparing Hasse diagrams to Growing Polygons was performed • Two-way repeated-measures ANOVA • Independent variables (both within-subjects): • Visualization type: Hasse or GP • Data density: sparse and dense • 4 different data sets: 1 of each data density for each visualization type • 20 subjects participated in the test • All subjects knowledgeable in distributed systems Causality Visualization Using Animated Growing Polygons

20. User Study: Tasks • Each data set required the user to solve 4 common questions related to causal relations: • Find the process with longest duration • Find the process that has had the most influence on the system • Find the process that has been influenced the most • Is process x causally related to process y? • Times were measured for these tasks • Users were also asked for their subjective opinion of the visualization (rating and ranking) Causality Visualization Using Animated Growing Polygons

21. Results • Performance measurement • Users were more efficient using Growing Polygons than Hasse diagrams • Hasse: 434 (s.d. 379) seconds • GP: 252 (s.d. 175) seconds • This is a significant difference for both sparse and densedensities Causality Visualization Using Animated Growing Polygons

22. Results (2) • Correctness • Users are more correct when solving problems using Growing Polygons than Hasse diagrams • Hasse: 4.4 (s.d. 1.1) correct • GP: 5.6 (s.d. 0.7) correct • This was a significant difference for both sparse and densedensities Causality Visualization Using Animated Growing Polygons

23. Results (3) • Subjective ratings • Very positive user feedback • Users consistently rated GP over Hasse diagrams in all respects (ease-of-use, enjoyability, efficiency) • These readings were all statistically significant • The majority of users also rated GP over Hasse Causality Visualization Using Animated Growing Polygons

24. Conclusions & Future Work • Visualization of causal relations is crucial for understanding complex systems • Traditional visualization techniques (Hasse diagrams) fall short • Growing Polygonis a novel idea of visualizing causality focused on the information flow • Our visualization technique is • Significantly more efficient to use than Hasse diagrams • Significantly more appealing to users than Hasse diagrams • In the future we want to explore scalability concerns in systems spanning long time periods and involving many processes Causality Visualization Using Animated Growing Polygons

25. Questions? • Contact information: • Address: Niklas Elmqvist and Philippas Tsigas Department of Computing Science Chalmers University of Technology SE-412 96 Göteborg, Sweden • Email: • {elm|tsigas}@cs.chalmers.se • Project website: • http://www.cs.chalmers.se/~elm/projects/causalviz Causality Visualization Using Animated Growing Polygons