Causality Visualization Using Animated Growing Polygons Niklas Elmqvist Philippas Tsigas IEEE 2003 Symposium on Information Visualization October 19 th -21 st, Seattle, Washington, USA.

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

3 Causality Visualization Using Animated Growing Polygons Introduction 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 ”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.

4 Causality Visualization Using Animated Growing Polygons 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?

5 Causality Visualization Using Animated Growing Polygons Example: Citations (2) time Author AAuthor BAuthor C

6 Causality Visualization Using Animated Growing Polygons 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 P 1,..., P N Internal events are sequential and causally related External events interconnect processes through messages Effective visualization is a difficult problem Traditional visualization: Hasse diagrams

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

8 Causality Visualization Using Animated Growing Polygons Related Work: Hasse Diagrams 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

9 Causality Visualization Using Animated Growing Polygons 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

10 Causality Visualization Using Animated Growing Polygons 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

11 Causality Visualization Using Animated Growing Polygons 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

12 Causality Visualization Using Animated Growing Polygons 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

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

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

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

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

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

18 Causality Visualization Using Animated Growing Polygons Hasse vs Growing Polygons

19 Causality Visualization Using Animated Growing Polygons 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

20 Causality Visualization Using Animated Growing Polygons User Study: Tasks Each data set required the user to solve 4 common questions related to causal relations: 1. Find the process with longest duration 2. Find the process that has had the most influence on the system 3. Find the process that has been influenced the most 4. 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)

21 Causality Visualization Using Animated Growing Polygons 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 dense densities

22 Causality Visualization Using Animated Growing Polygons 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 dense densities

23 Causality Visualization Using Animated Growing Polygons 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

24 Causality Visualization Using Animated Growing Polygons Conclusions & Future Work Visualization of causal relations is crucial for understanding complex systems Traditional visualization techniques (Hasse diagrams) fall short Growing Polygon is 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

25 Causality Visualization Using Animated Growing Polygons Questions? Contact information: Address: Niklas Elmqvist and Philippas Tsigas Department of Computing Science Chalmers University of Technology SE Göteborg, Sweden Project website: